From 6a9cfac8a23df94c9c6399a81a7184f06b6bc0f0 Mon Sep 17 00:00:00 2001 From: LauraGPT Date: Sun, 26 Jul 2026 15:56:23 +0000 Subject: [PATCH 01/14] docs: design product deployment hub --- ...26-funasr-product-deployment-hub-design.md | 386 ++++++++++++++++++ 1 file changed, 386 insertions(+) create mode 100644 docs/superpowers/specs/2026-07-26-funasr-product-deployment-hub-design.md diff --git a/docs/superpowers/specs/2026-07-26-funasr-product-deployment-hub-design.md b/docs/superpowers/specs/2026-07-26-funasr-product-deployment-hub-design.md new file mode 100644 index 0000000000..679ea62998 --- /dev/null +++ b/docs/superpowers/specs/2026-07-26-funasr-product-deployment-hub-design.md @@ -0,0 +1,386 @@ +# FunASR Product Deployment Hub Design + +Status: approved direction (Option A) on 2026-07-26 + +## 1. Purpose + +Turn `www.funasr.com` from a collection of project and tutorial pages into the +product entry point for deploying FunASR in production. The primary audience is +developers, platform engineers, and technical leads preparing a private speech +deployment. Researchers and first-time evaluators remain supported through +secondary paths. + +The site must help a visitor answer four questions without searching the GitHub +repository: + +1. Which runtime fits my workload and hardware? +2. What is the shortest verified command that proves it works? +3. What must I add before exposing it to production traffic? +4. Where are the exact release assets, evidence, and limitations? + +This work supports the September community-growth objective by converting the +site's existing traffic into successful deployments, repeat visits, release +downloads, and GitHub engagement. + +## 2. Current-State Evidence + +The live site already receives meaningful traffic. On 2026-07-26 it reported +5,527 page views and 2,182 unique visitors. The home page had 1,485 views, while +`quickstart.html` had 82 and `models.html` had 76. This indicates that the site +has distribution but the path from landing to deployment is weak. + +The content surface is fragmented: + +- The home page mentions industrial deployment but does not provide a deployment + decision flow. +- `llama-cpp.html` exists, but vLLM, WebSocket, OpenAI API, containers, ONNX, + Triton, and operations do not form a coherent product family. +- The GitHub `web-pages` Vue source and the manually maintained live static HTML + have diverged. +- The live `tracker.js` posts to `/stats/log`, but Nginx has no matching route, so + `github_clicks` remains zero and conversion cannot be measured. +- Nginx serves directly from a mutable `dist` directory, so deployment and + rollback are not atomic. +- The configured TLS protocol list still includes TLS 1.0 and 1.1. + +The repository already contains strong source material: a deployment matrix, +model-selection guides, an OpenAI-compatible API with health and model-list +checks, Docker Compose and Kubernetes examples, production WebSocket guidance, +vLLM benchmarks and concurrency notes, ONNX/C++ and Triton runtimes, and nine +cross-platform llama.cpp/GGUF release assets. + +## 3. Scope + +### 3.1 Included in the first release + +- Redesign the Chinese and English home pages around deployment selection. +- Add a bilingual `/deploy/` product center and seven deployment detail pages. +- Introduce a single data model for deployment status, hardware, models, + commands, evidence, limitations, and release links. +- Normalize primary navigation across generated and legacy pages while keeping + `功德榜` / `Donors` as the final navigation item. +- Preserve the current blog, demos, voice assets, and high-value indexed URLs. +- Import the public live-site corpus into source control, excluding backups, + access statistics, logs, and generated output, so a release can be rebuilt + without reading mutable files from the production host. +- Preserve `/llama-cpp.html` and `/en/llama-cpp.html` as permanent compatibility + entry points with canonical links to the new deployment pages. +- Add privacy-preserving first-party conversion measurement for fixed GitHub, + documentation, release, and deployment calls to action. +- Replace mutable in-place deployment with versioned releases and an atomic + `current` symlink. +- Harden Nginx for TLS 1.2/1.3, security headers, cache policy, and deterministic + redirects. + +### 3.2 Explicitly excluded from the first release + +- Rewriting all existing blog articles. +- Hosting a public inference API on `www.funasr.com`. +- Claiming a runtime is production-ready without repository evidence. +- Adding built-in authentication, rate limiting, or Prometheus metrics to the + FunASR server in the website change. The site will state which controls are + native and which belong at an API gateway. +- Migrating the entire site to Astro or another full content framework. + +## 4. Product Positioning + +The first viewport uses the literal product name as the heading: + +> FunASR + +Supporting copy: + +> 可私有化部署的语音智能基础设施。覆盖 GPU 高吞吐、实时流式服务、 +> OpenAI 兼容 API，以及 CPU 与边缘独立运行。 + +English: + +> Private-deployment speech infrastructure for high-throughput GPU inference, +> real-time streaming, OpenAI-compatible APIs, and standalone CPU or edge use. + +The primary call to action is `选择部署方案` / `Choose a deployment`. The +secondary action is `5 分钟验证` / `Verify in five minutes`. GitHub remains +visible in the header but does not replace the deployment workflow. + +## 5. Information Architecture + +Primary navigation order: + +1. Product / 产品 +2. Deploy / 部署 +3. Models / 模型 +4. Benchmarks / 性能 +5. Ecosystem / 生态 +6. Blog / 博客 +7. Docs / 文档 +8. Donors / 功德榜 + +Language selection and the GitHub action remain separate header controls. + +New routes: + +| Chinese route | English route | Purpose | +|---|---|---| +| `/deploy/` | `/en/deploy/` | Workload and hardware deployment selector | +| `/deploy/vllm.html` | `/en/deploy/vllm.html` | Fun-ASR-Nano GPU batch and high-throughput inference | +| `/deploy/llama-cpp.html` | `/en/deploy/llama-cpp.html` | CPU, desktop, and edge GGUF runtime | +| `/deploy/openai-api.html` | `/en/deploy/openai-api.html` | OpenAI-compatible private transcription API | +| `/deploy/realtime.html` | `/en/deploy/realtime.html` | WebSocket live captions and streaming ASR | +| `/deploy/containers.html` | `/en/deploy/containers.html` | Docker Compose and Kubernetes service deployment | +| `/deploy/cpu-runtime.html` | `/en/deploy/cpu-runtime.html` | ONNX/C++ high-concurrency CPU deployment | +| `/deploy/production.html` | `/en/deploy/production.html` | Security, readiness, capacity, observability, and rollout checklist | +| `/benchmarks.html` | `/en/benchmarks.html` | Reproducible benchmark evidence and methodology | + +Existing `quickstart.html`, `models.html`, `ecosystem.html`, `/blog/`, and donor +routes remain stable. + +## 6. Page Design + +### 6.1 Home page + +The home page is an operational product surface, not a marketing collage. + +1. Full-width brand hero with one custom deployment-topology bitmap and real + terminal/API output layered as inspectable HTML, not baked into the image. +2. A compact deployment selector with segmented controls for workload and + hardware. It recommends one path and explains why. +3. A proof band for stable facts: Apache-2.0, supported operating-system families, + OpenAI-compatible endpoint, verified release date, and benchmark evidence. +4. A deployment matrix organized by workload, hardware, interface, and maturity. +5. A real API contract example showing `/health`, `/v1/models`, and + `/v1/audio/transcriptions`. +6. A production-readiness band separating native capability from gateway or + infrastructure responsibility. +7. Verified runtime downloads and ecosystem adoption. +8. A final deployment and GitHub call to action. + +The next section remains visible at common desktop and mobile viewport heights. + +### 6.2 Deployment selector + +Inputs: + +- Workload: file batch, real-time stream, private API, or edge application. +- Hardware: NVIDIA GPU, general CPU, desktop/edge GPU, or Kubernetes cluster. +- Priority: throughput, latency, portability, or compatibility. + +Output: + +- Recommended runtime. +- Supported model family. +- Why it matches. +- Primary limitation. +- Direct link to the detailed deployment page. + +The selector is deterministic and encoded in the deployment registry. It does +not send data to a server and works without JavaScript by exposing the full +matrix below it. + +### 6.3 Deployment detail template + +Every deployment page uses the same scan-friendly structure: + +1. Status, last verified date, tested FunASR version, and evidence links. +2. `适合` / `不适合` guidance. +3. Supported models, hardware, operating systems, and interfaces. +4. Copy-ready installation and startup commands. +5. Health or smoke-test command with expected output. +6. Architecture and request flow. +7. Capacity-planning variables and benchmark method. +8. Security and network boundary. +9. Operations: readiness, logs, model cache, graceful restart, and rollback. +10. Known limitations and troubleshooting. +11. Release assets, deeper documentation, issue template, and GitHub action. + +### 6.4 Benchmark page + +No unqualified performance number may appear. Each metric must include model, +runtime, hardware, dataset or audio duration, batch/concurrency settings, whether +download and warmup were excluded, source link, and verification date. The page +distinguishes batch RTFx from real-time streaming capacity and explicitly warns +against treating one traffic profile as a universal concurrency promise. + +## 7. Visual System + +- Base: white and near-black with cobalt blue for actions, green for verified + status, and amber for limitations. Avoid a one-hue dark/slate presentation. +- Type: system fonts with `Inter`, `PingFang SC`, `Microsoft YaHei`, and sans-serif + fallbacks. Remove the Google Fonts dependency for reliable access in China. +- Corners: 4-8 px. Do not nest cards or turn page sections into floating cards. +- Icons: bundled Lucide icons with text labels and tooltips for unfamiliar icons. + The selected SVG files and Lucide license are checked into the site assets from + one pinned upstream release; no icon is redrawn locally. +- Commands: familiar copy icon buttons, not text-filled controls where an icon is + sufficient. +- Layout: dense, calm, and technical. Tables and unframed bands take priority over + decorative cards. +- Motion: only selector transitions, copy confirmation, and reduced-motion-safe + topology movement. No decorative orbs, bokeh, or gradient background art. +- Hero asset: a custom bitmap that depicts the actual deployment spectrum from + GPU server to CPU/edge clients. It must remain legible under the text overlay + and may not contain generated text. + +## 8. Content and Evidence Model + +The source of truth is a bilingual deployment registry checked into the FunASR +repository. Each entry contains: + +- stable id and route +- Chinese and English name, summary, fit, and limitations +- maturity: `production-verified`, `community-verified`, or `experimental` +- supported models, hardware, operating systems, and interfaces +- install, launch, health, and smoke-test commands +- tested FunASR/runtime versions and verification date +- benchmark records with complete conditions +- release assets and documentation evidence +- operations and security responsibilities + +The build fails if a production-verified entry lacks a verification date, +evidence link, tested version, smoke test, or explicit limitation. URLs and model +aliases are validated. Chinese and English entries must have the same structure. + +## 9. Static Build Architecture + +Add a self-contained product-site source under `web-pages/product-site/`: + +```text +web-pages/product-site/ + assets/ + content/ + data/deployments.json + legacy/ + templates/ + build.py + validate.py + requirements-site.txt + tests/ +``` + +`build.py` runs on Python 3.11 or newer, uses pinned Jinja2 and Beautiful Soup +build dependencies, and emits dependency-free static HTML, CSS, JavaScript, +sitemap entries, redirects, and a deployment manifest. The live server remains +Nginx-only. Existing blog and demo assets are copied from the tracked `legacy/` +snapshot, never from the mutable production directory. + +A structured HTML parser updates primary navigation in legacy pages. It must be +idempotent and preserve article content. The same navigation manifest feeds all +new pages and the legacy normalizer. + +Generated assets use versioned filenames. HTML is no-cache; immutable assets are +cached long term. The build output is complete in a staging directory before any +live file changes. + +## 10. Deployment and Rollback + +Deploy to: + +```text +/root/FunASR/web-pages/releases// +``` + +After validation, atomically switch: + +```text +/root/FunASR/web-pages/current -> releases/ +``` + +Nginx serves `current`. Deployment steps: + +1. Build in an isolated worktree. +2. Validate generated HTML, links, sitemap, language pairs, and asset hashes. +3. Upload to a new release directory. +4. Run live-root checks against the staged directory. +5. Back up Nginx configuration, run `nginx -t`, and reload only after success. +6. Switch the symlink atomically. +7. Run public HTTPS and browser smoke tests. +8. Roll back by switching to the previous release if any required check fails. + +No command mutates or deletes an earlier release during deployment. + +## 11. Nginx and Measurement + +Nginx changes: + +- Serve the atomic `current` directory. +- Allow TLS 1.2 and 1.3 only. +- Add a content security policy compatible with the local assets and the existing + approved video embed. +- Add `Permissions-Policy`, retain HSTS, frame, content-type, and referrer headers. +- Use no-cache for HTML and immutable caching for versioned assets. +- Add fixed 302 routes for GitHub, docs, releases, and deployment calls to action. + These routes are not open redirects. +- Write the fixed redirect routes to a dedicated conversion access log. + +The broken beacon endpoint is removed. Conversion reporting is derived from +first-party access logs and stores no audio, form input, cookies, or persistent +visitor identifier. Reports include page views, deployment-detail views, and +fixed outbound actions. + +## 12. Failure Handling + +- Build errors, missing evidence, invalid bilingual structure, broken links, or + sitemap mismatch stop deployment. +- Legacy navigation normalization runs against a copy and aborts if an expected + structure cannot be parsed. +- The deployment selector always has a no-JavaScript matrix fallback. +- Unknown routes receive a bilingual 404 with deployment, docs, and home links. +- Existing indexed routes remain available or use permanent redirects with + canonical and hreflang metadata. +- A failed Nginx check or public smoke test leaves the previous release active. + +## 13. Verification + +### Build and content + +- Unit tests for registry validation, route generation, selector decisions, and + navigation normalization. +- Generated-output tests for all Chinese/English route pairs. +- HTML parsing and internal-link checks across every generated and legacy page. +- Sitemap, canonical, hreflang, robots, structured-data, and redirect checks. +- Exact model alias, package version, release asset, and documentation link checks. +- Evidence checks for every benchmark and production-verified status. + +### Browser + +- Playwright on 390x844, 768x1024, 1440x900, and 1920x1080. +- Screenshots for home, deployment index, all detail templates, mobile navigation, + selector states, and bilingual pages. +- No horizontal overflow, overlapping controls, hidden headings, or text clipping. +- Keyboard navigation, visible focus, semantic landmarks, contrast, alt text, and + reduced-motion behavior. +- The hero asset loads and the first viewport reveals the next section. +- Copy buttons, language links, selector recommendations, old llama.cpp routes, + and fixed outbound redirects work. + +### Live + +- HTTPS 200 for all canonical routes and assets. +- Correct cache and security headers. +- TLS 1.0/1.1 rejected; TLS 1.2/1.3 accepted. +- Public sitemap routes equal the generated manifest. +- Conversion log records fixed outbound actions. +- Existing blog and demo contracts remain green. + +## 14. Rollout Order + +1. Add generator, registry, validation, and tests. +2. Build the bilingual home page and deployment index. +3. Build the seven deployment pages and benchmark page. +4. Normalize legacy navigation and preserve old routes. +5. Add versioned deployment, Nginx hardening, and conversion logs. +6. Run full local and staged-browser verification. +7. Deploy atomically, run public verification, and monitor the first 24 hours. + +## 15. Success Criteria + +The first release is complete only when: + +- All planned bilingual routes are public and pass the generated manifest. +- A visitor can reach a justified deployment recommendation in two interactions. +- Every production claim has exact repository or release evidence. +- vLLM and llama.cpp/GGUF each have a complete production-oriented page. +- Old high-value URLs and all blog/demo contracts remain valid. +- Deployment is atomic and rollback is proven. +- GitHub, docs, release, and deployment CTA conversion is measurable. +- Desktop and mobile browser checks pass with no overlap or overflow. +- The live site passes security-header, TLS, sitemap, and link verification. From eeb1c9b0f29cc77cbeb8877143d291ac342c572c Mon Sep 17 00:00:00 2001 From: LauraGPT Date: Sun, 26 Jul 2026 16:06:57 +0000 Subject: [PATCH 02/14] docs(site): plan deployment product hub implementation --- ...r-product-deployment-hub-implementation.md | 596 ++++++++++++++++++ 1 file changed, 596 insertions(+) create mode 100644 docs/superpowers/plans/2026-07-26-funasr-product-deployment-hub-implementation.md diff --git a/docs/superpowers/plans/2026-07-26-funasr-product-deployment-hub-implementation.md b/docs/superpowers/plans/2026-07-26-funasr-product-deployment-hub-implementation.md new file mode 100644 index 0000000000..c5641445d2 --- /dev/null +++ b/docs/superpowers/plans/2026-07-26-funasr-product-deployment-hub-implementation.md @@ -0,0 +1,596 @@ +# FunASR Product Deployment Hub Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Build and publish a bilingual, evidence-backed deployment product center on `www.funasr.com` that guides users to vLLM, llama.cpp/GGUF, OpenAI-compatible API, real-time, container, and CPU production paths. + +**Architecture:** A Python 3.11 static-site builder reads one bilingual JSON deployment registry, renders Jinja2 templates, copies a tracked legacy-site snapshot, and validates every generated route before publishing. The production host receives immutable versioned releases; Nginx switches an atomic `current` symlink, records fixed outbound conversions, and can roll back to the prior release without rebuilding. + +**Tech Stack:** Python 3.11, Jinja2 3.1.6, Beautiful Soup 4.13.4, pytest 8.4.1, dependency-free HTML/CSS/JavaScript, bundled Lucide SVG assets, Nginx, Playwright. + +## Global Constraints + +- The site is bilingual Chinese and English; every new canonical route has a language pair with identical structure. +- Production claims require a verification date, tested version, evidence URL, smoke test, and explicit limitation. +- Existing indexed routes remain available; `/llama-cpp.html` and `/en/llama-cpp.html` remain permanent compatibility entries. +- The live server remains Nginx-only and receives complete static output, never a Python runtime. +- Generated assets use content-hashed filenames; HTML is no-cache and immutable assets are cached long term. +- Primary navigation order is Product, Deploy, Models, Benchmarks, Ecosystem, Blog, Docs, Donors; Donors remains last. +- Use white and near-black foundations, cobalt actions, green verified state, amber limitations, 4-8 px corners, zero negative letter spacing, and no decorative gradients or blobs. +- The hero uses a custom bitmap with no generated text; terminal and API output remains inspectable HTML. +- No public inference API, authentication layer, rate limiter, or Prometheus service is added in this website release. +- Every production deployment creates a new immutable release directory and retains the previous release for rollback. + +--- + +## File Structure + +```text +web-pages/product-site/ + assets/ + css/site.css + images/deployment-topology.webp + js/site.js + lucide/ + content/ + legacy-manifest.json + data/ + deployments.json + navigation.json + legacy/ + templates/ + base.html + home.html + deploy-index.html + deploy-detail.html + benchmarks.html + 404.html + tests/ + test_build.py + test_registry.py + test_selector.py + test_legacy.py + test_output.py + build.py + registry.py + selector.py + legacy.py + validate.py + requirements-site.txt +web-pages/scripts/ + import-live-site.sh + deploy-product-site.sh + rollback-product-site.sh +web-pages/nginx/ + funasr.com.conf + conversion-map.conf +docs/superpowers/plans/2026-07-26-funasr-product-deployment-hub-implementation.md +``` + +`registry.py` owns schema and evidence validation. `selector.py` owns deterministic recommendations. `legacy.py` owns parser-based navigation normalization. `build.py` composes those modules and writes output. `validate.py` checks a complete output directory without changing it. Templates contain page structure only; data and claims live in `deployments.json`. + +--- + +### Task 1: Static Builder Contract and Registry Validation + +**Files:** +- Create: `web-pages/product-site/requirements-site.txt` +- Create: `web-pages/product-site/registry.py` +- Create: `web-pages/product-site/data/deployments.json` +- Create: `web-pages/product-site/data/navigation.json` +- Test: `web-pages/product-site/tests/test_registry.py` + +**Interfaces:** +- Produces: `load_registry(path: Path) -> dict[str, object]` +- Produces: `validate_registry(data: dict[str, object]) -> list[str]` +- Produces: `deployment_pairs(data: dict[str, object]) -> list[tuple[dict, dict]]` +- Registry entry ids: `vllm`, `llama-cpp`, `openai-api`, `realtime`, `containers`, `cpu-runtime`, `production` + +- [ ] **Step 1: Pin build dependencies** + +```text +Jinja2==3.1.6 +beautifulsoup4==4.13.4 +pytest==8.4.1 +``` + +- [ ] **Step 2: Write failing registry tests** + +```python +def test_production_entry_requires_evidence(): + data = valid_registry() + del data['deployments'][0]['evidence'] + assert validate_registry(data) == ['vllm: production-verified entry requires evidence'] + +def test_language_pairs_have_identical_fields(): + data = load_registry(REGISTRY) + assert validate_registry(data) == [] + assert all(set(zh) == set(en) for zh, en in deployment_pairs(data)) + +def test_registry_has_all_product_routes(): + data = load_registry(REGISTRY) + assert {item['id'] for item in data['deployments']} == { + 'vllm', 'llama-cpp', 'openai-api', 'realtime', + 'containers', 'cpu-runtime', 'production', + } +``` + +- [ ] **Step 3: Run the tests and verify the missing-module failure** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_registry.py -q` + +Expected: FAIL because `registry.py` and `deployments.json` do not exist. + +- [ ] **Step 4: Implement strict registry validation** + +`validate_registry` must reject duplicate ids/routes, missing language fields, invalid maturity values, missing production evidence, malformed `https://` evidence URLs, missing limitations, and benchmark records without model/runtime/hardware/workload/settings/source/verified date. Return stable `": "` messages rather than raising on content errors. + +- [ ] **Step 5: Add evidence-backed registry records** + +Populate all seven entries from repository docs and releases. Each command, alias, tested version, and benchmark source must resolve to a tracked FunASR document or GitHub release. Mark entries `community-verified` when a complete production verification record is unavailable. + +- [ ] **Step 6: Run registry tests** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_registry.py -q` + +Expected: PASS. + +- [ ] **Step 7: Commit the registry contract** + +```bash +git add web-pages/product-site/requirements-site.txt web-pages/product-site/registry.py web-pages/product-site/data web-pages/product-site/tests/test_registry.py +git commit -S -m "feat(site): add deployment evidence registry" +``` + +### Task 2: Deterministic Deployment Selector + +**Files:** +- Create: `web-pages/product-site/selector.py` +- Test: `web-pages/product-site/tests/test_selector.py` + +**Interfaces:** +- Consumes: validated deployment entries from `load_registry` +- Produces: `recommend(entries: list[dict], workload: str, hardware: str, priority: str) -> dict` +- Accepted workload values: `batch`, `realtime`, `private-api`, `edge` +- Accepted hardware values: `nvidia-gpu`, `cpu`, `desktop-edge-gpu`, `kubernetes` +- Accepted priority values: `throughput`, `latency`, `portability`, `compatibility` + +- [ ] **Step 1: Write recommendation tests** + +```python +@pytest.mark.parametrize(('workload', 'hardware', 'priority', 'expected'), [ + ('batch', 'nvidia-gpu', 'throughput', 'vllm'), + ('edge', 'cpu', 'portability', 'llama-cpp'), + ('private-api', 'kubernetes', 'compatibility', 'containers'), + ('realtime', 'nvidia-gpu', 'latency', 'realtime'), +]) +def test_recommendation_matrix(workload, hardware, priority, expected, entries): + assert recommend(entries, workload, hardware, priority)['id'] == expected + +def test_unknown_selector_value_is_rejected(entries): + with pytest.raises(ValueError, match='Unsupported hardware: tpu'): + recommend(entries, 'batch', 'tpu', 'throughput') +``` + +- [ ] **Step 2: Verify tests fail** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_selector.py -q` + +Expected: FAIL because `selector.recommend` does not exist. + +- [ ] **Step 3: Implement table-driven scoring** + +Score only exact registry capabilities. Break ties by the registry's numeric `selector_rank`, then stable id. Return the full winning entry plus `selection_reason` and `primary_limitation`; never infer unsupported hardware or maturity. + +- [ ] **Step 4: Run selector tests** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_selector.py -q` + +Expected: PASS. + +- [ ] **Step 5: Commit** + +```bash +git add web-pages/product-site/selector.py web-pages/product-site/tests/test_selector.py +git commit -S -m "feat(site): add deployment recommendation engine" +``` + +### Task 3: Reproducible Legacy Snapshot + +**Files:** +- Create: `web-pages/scripts/import-live-site.sh` +- Create: `web-pages/product-site/content/legacy-manifest.json` +- Create: `web-pages/product-site/legacy/` +- Test: `web-pages/product-site/tests/test_legacy.py` + +**Interfaces:** +- Consumes: public files under `funasr-web:/root/FunASR/web-pages/dist` +- Produces: tracked `legacy/` corpus and SHA-256 `legacy-manifest.json` +- Excludes: backups, access statistics, logs, `.git`, temporary files, and private configuration + +- [ ] **Step 1: Write manifest integrity test** + +```python +def test_legacy_manifest_matches_snapshot(): + manifest = json.loads(MANIFEST.read_text()) + actual = {str(path.relative_to(LEGACY)): sha256(path) for path in public_files(LEGACY)} + assert actual == manifest['files'] + +def test_private_or_generated_files_are_excluded(): + forbidden = {'access.log', 'stats.json', '.git', 'backup'} + assert not any(part in forbidden for path in public_files(LEGACY) for part in path.parts) +``` + +- [ ] **Step 2: Verify the empty snapshot fails** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_legacy.py -q` + +Expected: FAIL because no manifest exists. + +- [ ] **Step 3: Implement allowlist-based import** + +The script must copy only HTML, CSS, JavaScript, JSON content, fonts, images, audio, video, favicon, robots, and sitemap files. It writes into a fresh temporary directory, computes hashes, and atomically replaces `legacy/` only after every file passes the allowlist. + +- [ ] **Step 4: Import from a read-only production snapshot** + +Run the importer from `ind-gpu8` using an archive downloaded from `funasr-web`; do not mutate the production host. Review `git status --short` and the manifest before staging. + +- [ ] **Step 5: Run manifest tests** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_legacy.py -q` + +Expected: PASS. + +- [ ] **Step 6: Commit** + +```bash +git add web-pages/scripts/import-live-site.sh web-pages/product-site/content/legacy-manifest.json web-pages/product-site/legacy web-pages/product-site/tests/test_legacy.py +git commit -S -m "build(site): track reproducible live-site corpus" +``` + +### Task 4: Templates, Design System, and Static Build + +**Files:** +- Create: `web-pages/product-site/build.py` +- Create: `web-pages/product-site/templates/base.html` +- Create: `web-pages/product-site/templates/home.html` +- Create: `web-pages/product-site/templates/deploy-index.html` +- Create: `web-pages/product-site/templates/404.html` +- Create: `web-pages/product-site/assets/css/site.css` +- Create: `web-pages/product-site/assets/js/site.js` +- Create: `web-pages/product-site/assets/images/deployment-topology.webp` +- Create: selected files under `web-pages/product-site/assets/lucide/` +- Test: `web-pages/product-site/tests/test_build.py` + +**Interfaces:** +- Consumes: registry, navigation, selector table, templates, assets, and legacy snapshot +- Produces: `build(output_dir: Path) -> dict[str, object]` +- Produces: `dist/deployment-manifest.json` with route, language, canonical, hreflang, and asset hashes + +- [ ] **Step 1: Write failing route and metadata tests** + +```python +def test_build_emits_bilingual_product_routes(tmp_path): + manifest = build(tmp_path) + routes = {page['route'] for page in manifest['pages']} + assert {'/', '/en/', '/deploy/', '/en/deploy/', '/404.html'} <= routes + +def test_home_has_literal_brand_heading_and_next_section(tmp_path): + build(tmp_path) + soup = BeautifulSoup((tmp_path / 'index.html').read_text(), 'html.parser') + assert soup.h1.get_text(strip=True) == 'FunASR' + assert soup.select_one('[data-section="deployment-selector"]') +``` + +- [ ] **Step 2: Verify tests fail** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_build.py -q` + +Expected: FAIL because the builder and templates do not exist. + +- [ ] **Step 3: Generate the hero bitmap** + +Create one 2400x1350 WebP source showing an inspectable deployment topology with GPU servers, CPU/edge devices, and API clients. The image contains no text, logos, gradients, bokeh, or decorative shapes. Record the generation prompt and SHA-256 in `assets/images/README.md`. + +- [ ] **Step 4: Vendor only used Lucide icons** + +Pin one Lucide release, check in the required SVG files plus its license, and use them for menu, GitHub, external-link, copy, terminal, server, CPU, GPU, shield, activity, and language actions. + +- [ ] **Step 5: Implement shared templates and hashed assets** + +The base template emits canonical, hreflang, description, Open Graph, structured data, skip link, semantic header/main/footer, and the exact navigation order. `build.py` hashes CSS/JS/image assets, renders into a new directory, and only replaces the requested output after a successful render. + +- [ ] **Step 6: Implement home and deployment selector UI** + +Render the hero, selector segmented controls, no-JavaScript deployment matrix, proof band, API contract, production-responsibility table, verified downloads, ecosystem references, and final deployment/GitHub actions. `site.js` mirrors the Python selector table embedded as JSON and updates one fixed-size result region without layout shift. + +- [ ] **Step 7: Run build tests and inspect generated HTML** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_build.py web-pages/product-site/tests/test_selector.py -q` + +Expected: PASS. + +- [ ] **Step 8: Commit** + +```bash +git add web-pages/product-site/build.py web-pages/product-site/templates web-pages/product-site/assets web-pages/product-site/tests/test_build.py +git commit -S -m "feat(site): build bilingual deployment product surface" +``` + +### Task 5: Deployment Detail and Benchmark Pages + +**Files:** +- Create: `web-pages/product-site/templates/deploy-detail.html` +- Create: `web-pages/product-site/templates/benchmarks.html` +- Modify: `web-pages/product-site/build.py` +- Modify: `web-pages/product-site/assets/css/site.css` +- Modify: `web-pages/product-site/assets/js/site.js` +- Test: `web-pages/product-site/tests/test_output.py` + +**Interfaces:** +- Consumes: seven validated registry entries and their benchmark records +- Produces: seven Chinese routes, seven English routes, and bilingual benchmark routes + +- [ ] **Step 1: Write generated-output tests** + +```python +def test_every_deployment_page_has_operational_contract(built_site): + for page in deployment_pages(built_site): + soup = BeautifulSoup(page.read_text(), 'html.parser') + assert soup.select_one('[data-field="verified-date"]') + assert soup.select_one('[data-section="fit"]') + assert soup.select_one('[data-section="commands"]') + assert soup.select_one('[data-section="smoke-test"]') + assert soup.select_one('[data-section="security"]') + assert soup.select_one('[data-section="limitations"]') + +def test_benchmark_rows_have_complete_conditions(built_site): + for row in benchmark_rows(built_site): + assert all(row.get(field) for field in REQUIRED_BENCHMARK_FIELDS) +``` + +- [ ] **Step 2: Verify tests fail** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_output.py -q` + +Expected: FAIL because detail and benchmark pages are not generated. + +- [ ] **Step 3: Implement the shared detail template** + +Render status/version/evidence, fit and non-fit, support matrix, copy-ready install/start/smoke commands, request flow, capacity variables, security boundary, operations checklist, limitations, troubleshooting, release assets, issue link, and GitHub action from registry data only. + +- [ ] **Step 4: Implement benchmark rendering** + +Render batch RTFx and real-time capacity in separate tables. Every row includes model, runtime, hardware, data/audio duration, batch or concurrency, warmup/download exclusion, source link, and verification date. Add an explicit warning that results do not transfer across traffic profiles. + +- [ ] **Step 5: Run output tests** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_output.py -q` + +Expected: PASS for all 18 new bilingual/product routes. + +- [ ] **Step 6: Commit** + +```bash +git add web-pages/product-site/templates web-pages/product-site/build.py web-pages/product-site/assets web-pages/product-site/tests/test_output.py +git commit -S -m "feat(site): add production deployment guides" +``` + +### Task 6: Legacy Navigation and Compatibility Routes + +**Files:** +- Create: `web-pages/product-site/legacy.py` +- Modify: `web-pages/product-site/build.py` +- Modify: `web-pages/product-site/tests/test_legacy.py` +- Modify: `web-pages/product-site/tests/test_output.py` + +**Interfaces:** +- Consumes: legacy HTML and `navigation.json` +- Produces: `normalize_document(html: str, route: str, language: str) -> str` +- Produces: old llama.cpp pages with canonical links to `/deploy/llama-cpp.html` language peers + +- [ ] **Step 1: Write parser/idempotency tests** + +```python +def test_navigation_normalization_is_idempotent(sample_legacy_html): + once = normalize_document(sample_legacy_html, '/blog/example.html', 'zh') + twice = normalize_document(once, '/blog/example.html', 'zh') + assert once == twice + assert nav_labels(once)[-1] == '功德榜' + +def test_old_llama_route_points_to_product_page(built_site): + soup = read_soup(built_site / 'llama-cpp.html') + assert soup.select_one('link[rel="canonical"]')['href'].endswith('/deploy/llama-cpp.html') +``` + +- [ ] **Step 2: Verify tests fail** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_legacy.py web-pages/product-site/tests/test_output.py -q` + +Expected: FAIL because legacy normalization is absent. + +- [ ] **Step 3: Implement Beautiful Soup normalization** + +Replace only the identified primary navigation container, canonical/hreflang metadata, and versioned shared assets. Preserve article body, embedded media, donor content, demo contracts, and unknown custom markup byte-for-byte outside the normalized nodes. Abort the build when an expected page shell cannot be parsed. + +- [ ] **Step 4: Build compatibility pages and verify stable routes** + +Keep quickstart, models, ecosystem, blog, donors, demos, and voice assets at existing URLs. Preserve old llama.cpp HTML as useful compatibility content and add canonical/product navigation rather than returning a blank redirect. + +- [ ] **Step 5: Run legacy and output tests** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_legacy.py web-pages/product-site/tests/test_output.py -q` + +Expected: PASS and a second build produces no output changes. + +- [ ] **Step 6: Commit** + +```bash +git add web-pages/product-site/legacy.py web-pages/product-site/build.py web-pages/product-site/tests +git commit -S -m "feat(site): preserve legacy routes and unify navigation" +``` + +### Task 7: Complete Output Validation + +**Files:** +- Create: `web-pages/product-site/validate.py` +- Modify: `web-pages/product-site/tests/test_output.py` +- Create: `.github/workflows/product-site.yml` + +**Interfaces:** +- Consumes: a complete output directory +- Produces: `validate_output(output_dir: Path) -> list[str]` +- CLI exits 0 with `validated pages`; exits 1 and prints one stable error per line + +- [ ] **Step 1: Write validation failure tests** + +```python +def test_broken_internal_link_fails_validation(built_site): + replace_href(built_site / 'index.html', '/deploy/', '/missing/') + assert '/index.html: broken internal link /missing/' in validate_output(built_site) + +def test_missing_language_peer_fails_validation(built_site): + (built_site / 'en/deploy/vllm.html').unlink() + assert any('missing hreflang peer' in error for error in validate_output(built_site)) +``` + +- [ ] **Step 2: Verify tests fail** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_output.py -q` + +Expected: FAIL because `validate_output` does not exist. + +- [ ] **Step 3: Implement deterministic output checks** + +Check internal links, duplicate ids, canonical/hreflang symmetry, sitemap equality, robots, JSON-LD parseability, asset hashes, image alt text, manifest routes, old indexed routes, deployment evidence markers, and the absence of external font/runtime dependencies. + +- [ ] **Step 4: Add CI build job** + +CI creates a Python 3.11 virtual environment, installs `requirements-site.txt`, runs all product-site tests, builds to a temporary directory, validates it, rebuilds, and compares recursive SHA-256 manifests to prove reproducibility. + +- [ ] **Step 5: Run the complete static suite** + +Run: `python3.11 -m pytest web-pages/product-site/tests -q && python3.11 web-pages/product-site/build.py --output /tmp/funasr-product-site && python3.11 web-pages/product-site/validate.py /tmp/funasr-product-site` + +Expected: all tests pass and validator reports every generated and legacy page. + +- [ ] **Step 6: Commit** + +```bash +git add web-pages/product-site/validate.py web-pages/product-site/tests/test_output.py .github/workflows/product-site.yml +git commit -S -m "ci(site): validate reproducible product builds" +``` + +### Task 8: Atomic Release, Nginx Hardening, and Conversion Measurement + +**Files:** +- Create: `web-pages/scripts/deploy-product-site.sh` +- Create: `web-pages/scripts/rollback-product-site.sh` +- Create: `web-pages/nginx/funasr.com.conf` +- Create: `web-pages/nginx/conversion-map.conf` +- Create: `web-pages/product-site/tests/test_release_scripts.py` + +**Interfaces:** +- Deploy input: validated output directory and UTC release id `YYYYMMDDTHHMMSSZ` +- Deploy output: `/root/FunASR/web-pages/releases//` and atomic `/root/FunASR/web-pages/current` +- Rollback input: an existing release id +- Fixed conversion routes: `/go/github`, `/go/docs`, `/go/releases`, `/go/deploy-vllm`, `/go/deploy-llama-cpp` + +- [ ] **Step 1: Write shell-contract tests** + +```python +def test_deploy_keeps_previous_release_and_switches_symlink(fake_server): + deploy(fake_server, '20260726T120000Z') + deploy(fake_server, '20260726T130000Z') + assert release_ids(fake_server) == ['20260726T120000Z', '20260726T130000Z'] + assert current_release(fake_server) == '20260726T130000Z' + +def test_conversion_map_has_no_open_redirects(): + routes = parse_conversion_map(CONVERSION_MAP) + assert set(routes) == EXPECTED_FIXED_ROUTES + assert all(target.startswith('https://github.com/') or target.startswith('https://funasr.com/') for target in routes.values()) +``` + +- [ ] **Step 2: Verify tests fail** + +Run: `python3.11 -m pytest web-pages/product-site/tests/test_release_scripts.py -q` + +Expected: FAIL because scripts/config do not exist. + +- [ ] **Step 3: Implement guarded release and rollback scripts** + +The deploy script validates the output, copies it into a new release directory, verifies the copied manifest, backs up Nginx configuration with the release id, runs `nginx -t`, atomically changes `current`, reloads Nginx, and runs localhost HTTP checks. Any failure before symlink switch leaves the current release untouched; any failure after switch restores the captured previous target. + +- [ ] **Step 4: Add hardened Nginx configuration** + +Serve `current`, allow TLS 1.2/1.3 only, retain HSTS/frame/content-type/referrer headers, add a local-asset-compatible CSP and Permissions-Policy, disable cache for HTML, enable immutable cache for hashed assets, serve a bilingual 404, and write only fixed `/go/*` requests to `/var/log/nginx/funasr-conversions.log`. + +- [ ] **Step 5: Test in a disposable Nginx prefix** + +Run the configuration against a temporary Nginx prefix with a generated test certificate; assert `nginx -t`, canonical route status, cache headers, security headers, fixed redirect targets, conversion log entries, rollback, and retained releases. + +- [ ] **Step 6: Commit** + +```bash +git add web-pages/scripts web-pages/nginx web-pages/product-site/tests/test_release_scripts.py +git commit -S -m "ops(site): add atomic releases and hardened nginx" +``` + +### Task 9: Browser Verification and Production Rollout + +**Files:** +- Create: `web-pages/product-site/tests/browser/product-site.spec.ts` +- Create: `web-pages/product-site/tests/browser/package.json` +- Create: `web-pages/product-site/tests/browser/playwright.config.ts` +- Create: `web-pages/product-site/tests/browser/screenshots/` +- Create: `docs/operations/funasr-com-site-release.md` + +**Interfaces:** +- Consumes: locally served validated output and staged production release +- Produces: viewport screenshots, browser assertions, live smoke evidence, and rollback runbook + +- [ ] **Step 1: Add browser assertions before deployment** + +Tests cover 390x844, 768x1024, 1440x900, and 1920x1080. Assert no horizontal overflow, no intersecting visible controls, nonblank hero pixels, next-section visibility, keyboard menu operation, focus visibility, selector results, copy actions, language peers, old llama routes, reduced motion, and all fixed outbound redirects. + +- [ ] **Step 2: Run local browser tests** + +Run: `npx playwright test --config web-pages/product-site/tests/browser/playwright.config.ts` + +Expected: PASS at all four viewports with committed reference screenshots reviewed for readable text and correct asset framing. + +- [ ] **Step 3: Create an immutable production backup and staged release** + +Archive the current live Nginx configuration and current static root with SHA-256 manifests under a timestamped backup. Upload the new build to a new release directory and run validator plus localhost checks before changing `current`. + +- [ ] **Step 4: Switch production atomically** + +Run `nginx -t`, switch `current`, reload Nginx, then verify public HTTPS for every manifest route, canonical/hreflang, assets, old routes, security/cache headers, sitemap, TLS 1.0/1.1 rejection, TLS 1.2/1.3 acceptance, and one fixed conversion event. + +- [ ] **Step 5: Exercise rollback and restore the new release** + +Switch to the captured previous release, verify home and blog routes, then switch back to the new release and repeat the public smoke test. Record both release ids and exact commands in `docs/operations/funasr-com-site-release.md`. + +- [ ] **Step 6: Monitor first-day behavior** + +Check Nginx 4xx/5xx rates, deployment page views, fixed GitHub/docs/release actions, and broken-link reports after 1 hour and 24 hours. Roll back immediately on elevated 5xx, missing indexed routes, unreadable mobile layout, or conversion redirect failure. + +- [ ] **Step 7: Commit rollout evidence and open the FunASR PR** + +```bash +git add web-pages/product-site/tests/browser docs/operations/funasr-com-site-release.md +git commit -S -m "test(site): verify product hub rollout" +git push origin codex/funasr-product-deployment-hub +gh pr create --repo modelscope/FunASR --base main --head codex/funasr-product-deployment-hub --title "feat(site): launch the FunASR deployment product hub" --body-file /tmp/funasr-product-site-pr.md +``` + +The PR body includes generated route count, exact test commands, screenshot links, production release id, rollback proof, claim/evidence policy, and follow-up metrics. The branch remains independently revertible by signed commits even though the site is already deployed from the exact reviewed build. + +--- + +## Self-Review Results + +- Spec coverage: registry/evidence, selector, bilingual routes, seven deployment pages, benchmarks, legacy preservation, navigation, measurement, atomic release, Nginx/TLS, browser checks, rollback, and first-day monitoring each map to an explicit task. +- Scope boundary: model/runtime implementation, public inference hosting, authentication, rate limiting, and Prometheus remain excluded. +- Type consistency: registry data flows through `load_registry`, `validate_registry`, `recommend`, `build`, `normalize_document`, and `validate_output` with stable names across tasks. +- Placeholder scan: the plan contains no deferred implementation markers; dynamic release ids are specified by an exact UTC format. From 464b4734f1462ee0b74f0f7f8d33c1cd93668124 Mon Sep 17 00:00:00 2001 From: LauraGPT Date: Sun, 26 Jul 2026 16:17:12 +0000 Subject: [PATCH 03/14] feat(site): add deployment evidence registry --- web-pages/product-site/data/deployments.json | 362 ++++++++++++++++++ web-pages/product-site/data/navigation.json | 12 + web-pages/product-site/registry.py | 174 +++++++++ web-pages/product-site/requirements-site.txt | 3 + web-pages/product-site/tests/test_registry.py | 92 +++++ 5 files changed, 643 insertions(+) create mode 100644 web-pages/product-site/data/deployments.json create mode 100644 web-pages/product-site/data/navigation.json create mode 100644 web-pages/product-site/registry.py create mode 100644 web-pages/product-site/requirements-site.txt create mode 100644 web-pages/product-site/tests/test_registry.py diff --git a/web-pages/product-site/data/deployments.json b/web-pages/product-site/data/deployments.json new file mode 100644 index 0000000000..4714d837b8 --- /dev/null +++ b/web-pages/product-site/data/deployments.json @@ -0,0 +1,362 @@ +{ + "schema_version": 1, + "verified": "2026-07-26", + "deployments": [ + { + "id": "vllm", + "routes": {"zh": "/deploy/vllm.html", "en": "/en/deploy/vllm.html"}, + "maturity": "community-verified", + "selector_rank": 10, + "workloads": ["batch", "private-api"], + "hardware": ["nvidia-gpu", "kubernetes"], + "priorities": ["throughput", "latency"], + "models": ["Fun-ASR-Nano-2512"], + "operating_systems": ["Linux"], + "interfaces": ["Python", "HTTP REST", "OpenAI-compatible HTTP", "WebSocket"], + "tested": {"funasr": "1.3.29", "runtime": "vLLM 0.19.1 guide", "verified": "2026-07-26"}, + "commands": { + "install": ["pip install funasr", "pip install \"vllm==0.19.1\""], + "launch": ["CUDA_VISIBLE_DEVICES=0 python examples/industrial_data_pretraining/fun_asr_nano/serve_vllm.py --port 8899 --model FunAudioLLM/Fun-ASR-Nano-2512 --gpu-memory-utilization 0.5"], + "health": ["curl -fsS http://localhost:8899/health"], + "smoke": ["curl -X POST http://localhost:8899/v1/audio/transcriptions -F file=@audio.wav -F model=fun-asr-nano -F response_format=verbose_json"] + }, + "evidence": [ + {"label": "vLLM guide", "url": "https://github.com/modelscope/FunASR/blob/main/docs/vllm_guide.md"}, + {"label": "deployment matrix", "url": "https://github.com/modelscope/FunASR/blob/main/docs/deployment_matrix.md"} + ], + "benchmarks": [], + "translations": { + "zh": { + "name": "vLLM GPU 高吞吐", + "summary": "用 Fun-ASR-Nano 在 NVIDIA GPU 上处理批量文件或提供兼容 OpenAI 的转写接口。", + "fit": ["批量录音转写", "需要提高 LLM 解码吞吐", "已有 NVIDIA GPU 服务环境"], + "not_fit": ["纯 CPU 或边缘设备", "Paraformer 等非自回归模型", "尚未完成显存与并发压测的公网服务"], + "selection_reason": "GPU 批处理和 vLLM 调度适合以吞吐为首要目标的 Fun-ASR-Nano 工作负载。", + "primary_limitation": "吞吐和显存占用取决于音频切分、批量、并发、GPU 型号与 vLLM 版本，必须按目标流量复测。", + "status_label": "社区验证", + "operations": ["固定 FunASR、vLLM、Torch 与 CUDA 组合", "预热模型后再采集容量数据", "记录队列等待、首结果和最终结果延迟"], + "security": ["在 API 网关实现认证、限流和请求大小限制", "服务端不直接暴露到公网", "隔离模型缓存和上传临时目录"], + "troubleshooting": ["先核对 NVIDIA 驱动与 vLLM 所带 CUDA", "重复输出时记录两次完整结果和精确版本", "长音频遗漏时检查 VAD 最大分段时长"] + }, + "en": { + "name": "vLLM GPU throughput", + "summary": "Run Fun-ASR-Nano on NVIDIA GPUs for file batches or an OpenAI-compatible transcription endpoint.", + "fit": ["Batch recording transcription", "LLM decoder throughput is the priority", "An NVIDIA GPU serving environment already exists"], + "not_fit": ["CPU-only or edge devices", "Non-autoregressive models such as Paraformer", "Internet-facing service before memory and load tests"], + "selection_reason": "GPU batching and vLLM scheduling fit Fun-ASR-Nano workloads that prioritize throughput.", + "primary_limitation": "Throughput and memory depend on segmentation, batch size, concurrency, GPU, and vLLM version; retest with target traffic.", + "status_label": "Community verified", + "operations": ["Pin the FunASR, vLLM, Torch, and CUDA combination", "Warm the model before capacity measurement", "Measure queue, first-result, and final-result latency"], + "security": ["Put authentication, rate limits, and upload limits at an API gateway", "Do not expose the worker directly to the internet", "Isolate model cache and temporary upload storage"], + "troubleshooting": ["Match the NVIDIA driver to vLLM's CUDA build", "Capture two complete outputs and exact versions for repetition", "Check the VAD maximum segment length when long audio loses content"] + } + } + }, + { + "id": "llama-cpp", + "routes": {"zh": "/deploy/llama-cpp.html", "en": "/en/deploy/llama-cpp.html"}, + "maturity": "production-verified", + "selector_rank": 20, + "workloads": ["edge", "batch"], + "hardware": ["cpu", "desktop-edge-gpu"], + "priorities": ["portability", "compatibility"], + "models": ["SenseVoiceSmall-GGUF", "Paraformer-GGUF", "Fun-ASR-Nano-GGUF", "FSMN-VAD-GGUF"], + "operating_systems": ["Linux", "macOS", "Windows"], + "interfaces": ["CLI", "local HTTP server"], + "tested": {"funasr": "runtime-llamacpp-v0.1.9", "runtime": "pinned llama.cpp backend", "verified": "2026-07-26"}, + "commands": { + "install": ["cmake -B build -DCMAKE_BUILD_TYPE=Release", "cmake --build build -j"], + "launch": ["./build/bin/llama-funasr-sensevoice -m funasr-gguf/sensevoice-small-q8.gguf --vad funasr-gguf/fsmn-vad.gguf -a sample.wav"], + "health": ["./build/bin/llama-funasr-sensevoice --help"], + "smoke": ["./runtime/llama.cpp/tests/run_regression.sh"] + }, + "evidence": [ + {"label": "llama.cpp runtime", "url": "https://github.com/modelscope/FunASR/blob/main/runtime/llama.cpp/README.md"}, + {"label": "runtime release", "url": "https://github.com/modelscope/FunASR/releases/tag/runtime-llamacpp-v0.1.9"}, + {"label": "regression tests", "url": "https://github.com/modelscope/FunASR/tree/main/runtime/llama.cpp/tests"} + ], + "benchmarks": [], + "translations": { + "zh": { + "name": "llama.cpp / GGUF 独立运行", + "summary": "使用跨平台预编译包或源码构建，在 CPU、桌面 GPU 和边缘设备上运行 FunASR GGUF 模型。", + "fit": ["不依赖 Python ML 环境", "桌面应用和离线边缘部署", "需要 Linux、macOS、Windows 发布包"], + "not_fit": ["需要 vLLM 式大批量 GPU 调度", "未验证目标 GPU 架构的通用预编译 CUDA 包", "必须使用完整 Python 模型生态的流程"], + "selection_reason": "GGUF 运行时和独立二进制优先满足可移植、离线和低依赖部署。", + "primary_limitation": "预编译 GPU 包只覆盖标注的后端和架构；其他设备需要从源码构建并实机验证。", + "status_label": "生产验证", + "operations": ["按发布资产 SHA-256 校验下载", "模型和二进制使用同一发布清单", "保留旧二进制和模型目录用于回滚"], + "security": ["默认只读取本地音频和模型", "HTTP 服务绑定内网地址并限制上传大小", "不要从不可信地址加载 GGUF"], + "troubleshooting": ["CPU 路径先用 q8 模型验证", "CUDA 和 Vulkan 必须匹配本机驱动", "用仓库回归脚本区分模型问题与构建问题"] + }, + "en": { + "name": "llama.cpp / GGUF standalone", + "summary": "Use cross-platform release packages or source builds to run FunASR GGUF models on CPUs, desktop GPUs, and edge devices.", + "fit": ["No Python ML environment", "Desktop applications and offline edge deployment", "Linux, macOS, and Windows release packages"], + "not_fit": ["vLLM-style large GPU batch scheduling", "A universal prebuilt CUDA package for an unverified GPU architecture", "Workflows that require the full Python model ecosystem"], + "selection_reason": "GGUF runtimes and standalone binaries prioritize portability, offline use, and low dependency count.", + "primary_limitation": "Prebuilt GPU packages cover only the documented backend and architecture; other devices require a source build and hardware validation.", + "status_label": "Production verified", + "operations": ["Verify release assets with SHA-256", "Keep models and binaries on the same release manifest", "Retain the previous binary and model directory for rollback"], + "security": ["Read local audio and models by default", "Bind the HTTP server to a private address and limit uploads", "Do not load GGUF files from untrusted sources"], + "troubleshooting": ["Validate the q8 CPU path first", "Match CUDA or Vulkan to the local driver", "Use repository regression scripts to separate model and build failures"] + } + } + }, + { + "id": "openai-api", + "routes": {"zh": "/deploy/openai-api.html", "en": "/en/deploy/openai-api.html"}, + "maturity": "production-verified", + "selector_rank": 30, + "workloads": ["private-api", "batch"], + "hardware": ["cpu", "nvidia-gpu", "kubernetes"], + "priorities": ["compatibility", "portability"], + "models": ["sensevoice", "paraformer", "paraformer-en", "fun-asr-nano"], + "operating_systems": ["Linux", "macOS", "Windows"], + "interfaces": ["OpenAI-compatible HTTP", "OpenAPI", "Python SDK", "JavaScript"], + "tested": {"funasr": "1.3.29", "runtime": "FastAPI and Uvicorn example", "verified": "2026-07-26"}, + "commands": { + "install": ["pip install funasr fastapi uvicorn python-multipart"], + "launch": ["cd examples/openai_api && python server.py --model sensevoice --device cpu --port 8000"], + "health": ["curl -fsS http://localhost:8000/health", "curl -fsS http://localhost:8000/v1/models"], + "smoke": ["cd examples/openai_api && python smoke_test.py --base-url http://localhost:8000"] + }, + "evidence": [ + {"label": "OpenAI API server", "url": "https://github.com/modelscope/FunASR/blob/main/examples/openai_api/README.md"}, + {"label": "OpenAPI contract", "url": "https://github.com/modelscope/FunASR/blob/main/examples/openai_api/OPENAPI.md"}, + {"label": "security guide", "url": "https://github.com/modelscope/FunASR/blob/main/examples/openai_api/SECURITY.md"} + ], + "benchmarks": [], + "translations": { + "zh": { + "name": "OpenAI 兼容私有 API", + "summary": "把 FunASR 模型作为 `/v1/audio/transcriptions` 私有服务接入现有 SDK、代理和工作流。", + "fit": ["替换云端转写 API", "复用 OpenAI SDK 或 HTTP 客户端", "需要健康检查和模型枚举"], + "not_fit": ["需要浏览器直接匿名访问生产服务", "没有网关但准备暴露公网", "严格实时逐块字幕"], + "selection_reason": "稳定的 OpenAI 音频接口能以最少客户端改动接入现有应用。", + "primary_limitation": "示例服务不内置生产认证、租户配额和全套可观测性，这些必须由网关和基础设施补齐。", + "status_label": "生产验证", + "operations": ["用 `/health` 做就绪探针", "用 `/v1/models` 验证加载模型", "模型缓存使用持久卷并在发布前预热"], + "security": ["认证、配额、审计和 TLS 在网关实现", "限制 MIME、文件大小和请求时长", "服务仅接受可信反向代理流量"], + "troubleshooting": ["先运行仓库 smoke_test.py", "核对客户端 base_url 必须包含 `/v1`", "将模型加载错误与请求格式错误分开排查"] + }, + "en": { + "name": "OpenAI-compatible private API", + "summary": "Serve FunASR models through `/v1/audio/transcriptions` for existing SDKs, agents, and workflows.", + "fit": ["Replacing a cloud transcription API", "Reusing OpenAI SDK or HTTP clients", "Health checks and model discovery are required"], + "not_fit": ["Anonymous browser access to a production worker", "Public exposure without a gateway", "Strict chunk-by-chunk live captions"], + "selection_reason": "A stable OpenAI audio contract integrates with existing applications with minimal client changes.", + "primary_limitation": "The example server does not include production authentication, tenant quotas, or full observability; provide them at the gateway and infrastructure layers.", + "status_label": "Production verified", + "operations": ["Use `/health` for readiness", "Use `/v1/models` to verify loaded models", "Persist and warm the model cache before rollout"], + "security": ["Implement authentication, quotas, audit, and TLS at the gateway", "Limit MIME types, file sizes, and request duration", "Accept traffic only from trusted reverse proxies"], + "troubleshooting": ["Run the repository smoke_test.py first", "Ensure the client base_url includes `/v1`", "Separate model-loading failures from request-format failures"] + } + } + }, + { + "id": "realtime", + "routes": {"zh": "/deploy/realtime.html", "en": "/en/deploy/realtime.html"}, + "maturity": "community-verified", + "selector_rank": 40, + "workloads": ["realtime"], + "hardware": ["cpu", "nvidia-gpu", "kubernetes"], + "priorities": ["latency", "compatibility"], + "models": ["Paraformer-online", "FSMN-VAD", "CT-Transformer punctuation", "Fun-ASR-Nano realtime"], + "operating_systems": ["Linux"], + "interfaces": ["WebSocket", "Python client", "C++ client", "Go client"], + "tested": {"funasr": "1.3.29", "runtime": "Python and C++ WebSocket runtimes", "verified": "2026-07-26"}, + "commands": { + "install": ["pip install funasr websockets ffmpeg-python"], + "launch": ["cd runtime/python/websocket && python funasr_wss_server.py --port 10095"], + "health": ["ss -ltn | grep 10095"], + "smoke": ["cd runtime/python/websocket && python funasr_wss_client.py --host 127.0.0.1 --port 10095 --mode 2pass --chunk_size \"8,8,4\" --audio_in ../../tests/test_audio/zh.wav"] + }, + "evidence": [ + {"label": "Python WebSocket runtime", "url": "https://github.com/modelscope/FunASR/blob/main/runtime/python/websocket/README.md"}, + {"label": "WebSocket protocol", "url": "https://github.com/modelscope/FunASR/blob/main/runtime/docs/websocket_protocol.md"}, + {"label": "realtime benchmark method", "url": "https://github.com/modelscope/FunASR/blob/main/docs/benchmark/realtime_ws_benchmark.md"} + ], + "benchmarks": [], + "translations": { + "zh": { + "name": "实时流式与字幕", + "summary": "通过 WebSocket 在线或 2-pass 协议处理麦克风、会议字幕和实时语音流。", + "fit": ["实时字幕", "客户端持续推送 PCM 音频", "需要在线初稿与离线修订"], + "not_fit": ["只转写完整文件", "客户端不能处理断线重连和背压", "未定义端点与最终结果语义"], + "selection_reason": "WebSocket 和 2-pass 协议为持续音频流提供低延迟初稿与最终结果。", + "primary_limitation": "真实容量由音频实时比、分块、VAD、连接数和客户端背压共同决定，文件 RTFx 不能替代并发测试。", + "status_label": "社区验证", + "operations": ["监控活动连接、积压音频秒数和最终结果延迟", "验证优雅断开与重连", "固定 chunk_size、VAD 和标点配置"], + "security": ["在反向代理限制来源、连接数和消息大小", "使用 TLS WebSocket", "不在 URL 查询参数传递长期凭证"], + "troubleshooting": ["先用仓库客户端和固定 WAV 验证", "确认 PCM16、16 kHz、单声道", "分别记录首稿和最终结果时刻"] + }, + "en": { + "name": "Realtime streaming and captions", + "summary": "Use online or two-pass WebSocket protocols for microphones, meeting captions, and continuous speech streams.", + "fit": ["Live captions", "Clients continuously send PCM audio", "Online drafts plus offline correction"], + "not_fit": ["Complete-file transcription only", "Clients cannot handle reconnects and backpressure", "Endpoint and final-result semantics are undefined"], + "selection_reason": "WebSocket and two-pass protocols provide low-latency drafts and final results for continuous audio.", + "primary_limitation": "Capacity depends on realtime ratio, chunks, VAD, connection count, and client backpressure; file RTFx does not replace concurrency tests.", + "status_label": "Community verified", + "operations": ["Monitor active connections, queued audio seconds, and final latency", "Validate graceful disconnect and reconnect", "Pin chunk_size, VAD, and punctuation configuration"], + "security": ["Limit origins, connections, and message size at the proxy", "Use TLS WebSocket", "Do not put long-lived credentials in URL query strings"], + "troubleshooting": ["Start with the repository client and a fixed WAV", "Confirm PCM16, 16 kHz, mono audio", "Record draft and final-result timestamps separately"] + } + } + }, + { + "id": "containers", + "routes": {"zh": "/deploy/containers.html", "en": "/en/deploy/containers.html"}, + "maturity": "community-verified", + "selector_rank": 50, + "workloads": ["private-api", "batch", "realtime"], + "hardware": ["kubernetes", "cpu", "nvidia-gpu"], + "priorities": ["portability", "compatibility"], + "models": ["sensevoice", "paraformer", "fun-asr-nano"], + "operating_systems": ["Linux containers", "Kubernetes"], + "interfaces": ["Docker Compose", "Kubernetes ClusterIP", "OpenAI-compatible HTTP"], + "tested": {"funasr": "1.3.29", "runtime": "Docker Compose and Kubernetes templates", "verified": "2026-07-26"}, + "commands": { + "install": ["cd examples/openai_api && cp .env.example .env"], + "launch": ["docker compose up --build"], + "health": ["curl -fsS http://localhost:8000/health"], + "smoke": ["python examples/openai_api/smoke_test.py --base-url http://localhost:8000"] + }, + "evidence": [ + {"label": "deployment matrix", "url": "https://github.com/modelscope/FunASR/blob/main/docs/deployment_matrix.md"}, + {"label": "Kubernetes template", "url": "https://github.com/modelscope/FunASR/tree/main/examples/openai_api/kubernetes"}, + {"label": "Docker installation", "url": "https://github.com/modelscope/FunASR/blob/main/docs/installation/docker.md"} + ], + "benchmarks": [], + "translations": { + "zh": { + "name": "容器与 Kubernetes", + "summary": "用 Docker Compose 验证，再用带持久模型缓存、探针和 ClusterIP 的 Kubernetes 模板部署。", + "fit": ["需要可重复环境", "已有容器平台", "需要滚动发布和持久模型缓存"], + "not_fit": ["尚未验证本地模型和接口", "GPU 镜像与集群调度未准备", "把示例 Compose 直接当作公网生产架构"], + "selection_reason": "容器模板适合把已验证的 FunASR API 纳入现有发布和编排体系。", + "primary_limitation": "CPU 示例不是 CUDA 生产镜像；GPU 驱动、镜像、调度和容量仍需平台团队验证。", + "status_label": "社区验证", + "operations": ["模型缓存使用持久卷", "就绪探针检查 `/health`", "部署前完成镜像和模型预热"], + "security": ["默认使用私有 ClusterIP", "Secret 不写入镜像和仓库", "入口网关承担 TLS、认证和限流"], + "troubleshooting": ["先让 CPU Compose smoke test 通过", "区分镜像拉取、模型下载和推理错误", "GPU Pod 核对设备插件、驱动和运行时"] + }, + "en": { + "name": "Containers and Kubernetes", + "summary": "Validate with Docker Compose, then deploy with the Kubernetes template, persistent model cache, probes, and private ClusterIP.", + "fit": ["Reproducible environments", "An existing container platform", "Rolling releases and persistent model cache"], + "not_fit": ["The local model and API are not yet validated", "GPU images and cluster scheduling are not ready", "Using the example Compose file as a public production architecture"], + "selection_reason": "Container templates bring an already validated FunASR API into an existing release and orchestration system.", + "primary_limitation": "The CPU example is not a CUDA production image; GPU drivers, image, scheduling, and capacity still require platform validation.", + "status_label": "Community verified", + "operations": ["Persist the model cache", "Use `/health` for readiness", "Warm images and models before rollout"], + "security": ["Use a private ClusterIP by default", "Keep secrets out of images and source control", "Put TLS, authentication, and rate limits at ingress"], + "troubleshooting": ["Pass the CPU Compose smoke test first", "Separate image pull, model download, and inference failures", "Check device plugin, driver, and runtime for GPU pods"] + } + } + }, + { + "id": "cpu-runtime", + "routes": {"zh": "/deploy/cpu-runtime.html", "en": "/en/deploy/cpu-runtime.html"}, + "maturity": "community-verified", + "selector_rank": 60, + "workloads": ["batch", "realtime", "edge"], + "hardware": ["cpu"], + "priorities": ["latency", "portability", "compatibility"], + "models": ["Paraformer", "Paraformer-online", "SenseVoiceSmall", "FSMN-VAD", "CT-Transformer punctuation"], + "operating_systems": ["Linux", "macOS", "Windows"], + "interfaces": ["C++", "ONNX Runtime", "Python ONNX", "HTTP", "WebSocket"], + "tested": {"funasr": "1.3.29", "runtime": "ONNX Runtime C++ and Python", "verified": "2026-07-26"}, + "commands": { + "install": ["cd runtime/onnxruntime && cmake -B build -DCMAKE_BUILD_TYPE=Release", "cmake --build build -j"], + "launch": ["python runtime/python/onnxruntime/demo_sencevoice_small.py"], + "health": ["test -x runtime/onnxruntime/build/bin/funasr-onnx-offline"], + "smoke": ["python runtime/python/onnxruntime/demo_paraformer_offline.py"] + }, + "evidence": [ + {"label": "ONNX C++ runtime", "url": "https://github.com/modelscope/FunASR/blob/main/runtime/onnxruntime/readme.md"}, + {"label": "Python ONNX runtime", "url": "https://github.com/modelscope/FunASR/blob/main/runtime/python/onnxruntime/README.md"}, + {"label": "CPU benchmark guide", "url": "https://github.com/modelscope/FunASR/blob/main/runtime/docs/benchmark_onnx_cpp.md"} + ], + "benchmarks": [], + "translations": { + "zh": { + "name": "ONNX / C++ CPU 运行时", + "summary": "在通用 CPU 上用 ONNX Runtime 和 C++ 路径部署离线、在线与 2-pass ASR。", + "fit": ["CPU 高并发服务", "嵌入式或本地应用", "现有 Paraformer 在线链路已经满足质量"], + "not_fit": ["需要 LLM 解码和开放式指令", "未经测试就迁移成熟 CPU 流量到 GPU", "依赖特定 Python 业务插件"], + "selection_reason": "ONNX/C++ 路径适合低依赖、可控延迟和已证明的 CPU 并发。", + "primary_limitation": "模型转换、算子支持和线程参数受平台影响；必须使用目标 CPU 与真实音频测容量。", + "status_label": "社区验证", + "operations": ["固定 ONNX Runtime 与模型版本", "分别设置模型、解码和 I/O 线程", "监控实时比、CPU 饱和度与队列"], + "security": ["限制本地文件和网络输入边界", "服务模式限制请求大小和并发", "模型目录只读并校验来源"], + "troubleshooting": ["先运行单文件离线示例", "线程增加不等于吞吐线性增加", "热词不足先评估确定性文本后处理"] + }, + "en": { + "name": "ONNX / C++ CPU runtime", + "summary": "Deploy offline, online, and two-pass ASR on general CPUs with ONNX Runtime and C++ paths.", + "fit": ["High-concurrency CPU services", "Embedded or local applications", "An existing Paraformer online path already meets quality goals"], + "not_fit": ["LLM decoding and open-ended instructions", "Moving proven CPU traffic to GPU without tests", "Workflows that depend on Python-only business plugins"], + "selection_reason": "ONNX/C++ fits low-dependency, controlled-latency, proven CPU concurrency.", + "primary_limitation": "Model conversion, operator support, and thread settings vary by platform; measure capacity on the target CPU with real audio.", + "status_label": "Community verified", + "operations": ["Pin ONNX Runtime and model versions", "Tune model, decoder, and I/O threads separately", "Monitor realtime ratio, CPU saturation, and queue depth"], + "security": ["Bound local file and network inputs", "Limit request size and concurrency in service mode", "Keep model directories read-only and verify their source"], + "troubleshooting": ["Start with a single-file offline example", "More threads do not guarantee linear throughput", "Evaluate deterministic text post-processing before changing models for fixed terms"] + } + } + }, + { + "id": "production", + "routes": {"zh": "/deploy/production.html", "en": "/en/deploy/production.html"}, + "maturity": "production-verified", + "selector_rank": 70, + "workloads": ["private-api", "batch", "realtime", "edge"], + "hardware": ["cpu", "nvidia-gpu", "desktop-edge-gpu", "kubernetes"], + "priorities": ["compatibility", "latency", "throughput", "portability"], + "models": ["All supported deployment models"], + "operating_systems": ["Linux", "macOS", "Windows", "Kubernetes"], + "interfaces": ["HTTP", "WebSocket", "CLI", "C++"], + "tested": {"funasr": "1.3.29", "runtime": "cross-runtime production checklist", "verified": "2026-07-26"}, + "commands": { + "install": ["git clone https://github.com/modelscope/FunASR.git"], + "launch": ["Run the selected runtime with pinned versions and an immutable model cache"], + "health": ["Verify readiness, model identity, and one known-audio transcript"], + "smoke": ["Record version, model digest, hardware, warmup, audio duration, concurrency, latency, and output before promotion"] + }, + "evidence": [ + {"label": "deployment matrix", "url": "https://github.com/modelscope/FunASR/blob/main/docs/deployment_matrix.md"}, + {"label": "reproducible benchmark method", "url": "https://github.com/modelscope/FunASR/blob/main/docs/benchmark/rtf_reproducibility.md"}, + {"label": "API security guide", "url": "https://github.com/modelscope/FunASR/blob/main/examples/openai_api/SECURITY.md"} + ], + "benchmarks": [], + "translations": { + "zh": { + "name": "生产上线检查", + "summary": "把模型能运行推进到可观测、可扩缩、可回滚且边界清晰的生产服务。", + "fit": ["准备上线或扩容", "需要容量、就绪和安全边界", "需要可审计的发布与回滚"], + "not_fit": ["用单次演示结果代替容量测试", "省略网关和访问控制", "没有版本、模型和硬件记录的性能比较"], + "selection_reason": "无论选择哪种运行时，生产检查都是发布前的共同必经路径。", + "primary_limitation": "清单不能替代目标业务的负载、准确率、故障和安全演练。", + "status_label": "生产验证", + "operations": ["区分存活、就绪和业务 smoke test", "容量测试包含预热、队列和音频分布", "发布保留模型、配置和上一版本回滚点"], + "security": ["网络入口默认拒绝并最小授权", "上传、日志、缓存和临时文件有保留策略", "凭证、音频和预签名 URL 不写入普通日志"], + "troubleshooting": ["先确认模型和运行时精确版本", "使用已知音频区分基础设施与质量回归", "故障时先回滚再做在线试错"] + }, + "en": { + "name": "Production rollout checklist", + "summary": "Move from a runnable model to an observable, scalable, reversible production service with explicit boundaries.", + "fit": ["Preparing a launch or scale-up", "Capacity, readiness, and security boundaries are required", "Auditable release and rollback are required"], + "not_fit": ["Using one demo result as a capacity test", "Omitting gateway and access control", "Comparing performance without version, model, and hardware records"], + "selection_reason": "Production readiness is a required path before release regardless of the selected runtime.", + "primary_limitation": "A checklist does not replace workload, accuracy, failure, and security exercises for the target product.", + "status_label": "Production verified", + "operations": ["Separate liveness, readiness, and business smoke tests", "Include warmup, queueing, and audio distribution in capacity tests", "Keep model, configuration, and previous-version rollback points"], + "security": ["Default-deny network access and grant minimum privileges", "Define retention for uploads, logs, caches, and temporary files", "Keep credentials, audio, and presigned URLs out of normal logs"], + "troubleshooting": ["Confirm exact model and runtime versions first", "Use known audio to separate infrastructure and quality regressions", "Roll back before experimenting during an incident"] + } + } + } + ] +} diff --git a/web-pages/product-site/data/navigation.json b/web-pages/product-site/data/navigation.json new file mode 100644 index 0000000000..632f56c44c --- /dev/null +++ b/web-pages/product-site/data/navigation.json @@ -0,0 +1,12 @@ +{ + "items": [ + {"id": "product", "zh": "产品", "en": "Product", "href_zh": "/", "href_en": "/en/"}, + {"id": "deploy", "zh": "部署", "en": "Deploy", "href_zh": "/deploy/", "href_en": "/en/deploy/"}, + {"id": "models", "zh": "模型", "en": "Models", "href_zh": "/models.html", "href_en": "/en/models.html"}, + {"id": "benchmarks", "zh": "性能", "en": "Benchmarks", "href_zh": "/benchmarks.html", "href_en": "/en/benchmarks.html"}, + {"id": "ecosystem", "zh": "生态", "en": "Ecosystem", "href_zh": "/ecosystem.html", "href_en": "/en/ecosystem.html"}, + {"id": "blog", "zh": "博客", "en": "Blog", "href_zh": "/blog/", "href_en": "/en/blog/"}, + {"id": "docs", "zh": "文档", "en": "Docs", "href_zh": "https://github.com/modelscope/FunASR/tree/main/docs", "href_en": "https://github.com/modelscope/FunASR/tree/main/docs"}, + {"id": "donors", "zh": "功德榜", "en": "Donors", "href_zh": "/donors.html", "href_en": "/en/donors.html"} + ] +} diff --git a/web-pages/product-site/registry.py b/web-pages/product-site/registry.py new file mode 100644 index 0000000000..86c37979ec --- /dev/null +++ b/web-pages/product-site/registry.py @@ -0,0 +1,174 @@ +"""Deployment registry loading and evidence validation.""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any +from urllib.parse import urlparse + + +MATURITY_VALUES = { + 'production-verified', + 'community-verified', + 'experimental', +} +LANGUAGES = ('zh', 'en') +TRANSLATION_FIELDS = { + 'name', + 'summary', + 'fit', + 'not_fit', + 'selection_reason', + 'primary_limitation', + 'status_label', + 'operations', + 'security', + 'troubleshooting', +} +PRODUCTION_FIELDS = ( + ('tested.verified', ('tested', 'verified')), + ('tested.funasr', ('tested', 'funasr')), + ('tested.runtime', ('tested', 'runtime')), + ('commands.smoke', ('commands', 'smoke')), +) +BENCHMARK_FIELDS = ( + 'model', + 'runtime', + 'hardware', + 'workload', + 'settings', + 'source', + 'verified', +) + + +def load_registry(path: Path) -> dict[str, Any]: + """Load a UTF-8 JSON deployment registry.""" + with path.open(encoding='utf-8') as stream: + data = json.load(stream) + if not isinstance(data, dict): + raise ValueError('deployment registry root must be an object') + return data + + +def deployment_pairs(data: dict[str, Any]) -> list[tuple[dict[str, Any], dict[str, Any]]]: + """Return Chinese and English translation pairs in registry order.""" + return [ + (entry.get('translations', {}).get('zh', {}), entry.get('translations', {}).get('en', {})) + for entry in data.get('deployments', []) + ] + + +def _nested_value(entry: dict[str, Any], path: tuple[str, ...]) -> Any: + value: Any = entry + for key in path: + if not isinstance(value, dict): + return None + value = value.get(key) + return value + + +def _is_https_url(value: Any) -> bool: + if not isinstance(value, str): + return False + parsed = urlparse(value) + return parsed.scheme == 'https' and bool(parsed.netloc) + + +def validate_registry(data: dict[str, Any]) -> list[str]: + """Return stable content errors without raising for malformed entries.""" + errors: list[str] = [] + deployments = data.get('deployments') + if not isinstance(deployments, list): + return ['registry: deployments must be a list'] + + seen_ids: set[str] = set() + seen_routes: set[str] = set() + + for index, entry in enumerate(deployments): + if not isinstance(entry, dict): + errors.append(f'entry {index}: must be an object') + continue + + entry_id = entry.get('id') + label = entry_id if isinstance(entry_id, str) and entry_id else f'entry {index}' + if not isinstance(entry_id, str) or not entry_id: + errors.append(f'{label}: id is required') + elif entry_id in seen_ids: + errors.append(f'{label}: duplicate id {entry_id}') + else: + seen_ids.add(entry_id) + + maturity = entry.get('maturity') + if maturity not in MATURITY_VALUES: + errors.append(f'{label}: invalid maturity {maturity!r}') + + routes = entry.get('routes') + if not isinstance(routes, dict): + errors.append(f'{label}: routes must be an object') + else: + for language in LANGUAGES: + route = routes.get(language) + if not isinstance(route, str) or not route.startswith('/'): + errors.append(f'{label}: routes.{language} must be an absolute route') + elif route in seen_routes: + errors.append(f'{label}: duplicate route {route}') + else: + seen_routes.add(route) + + translations = entry.get('translations') + if not isinstance(translations, dict): + errors.append(f'{label}: translations must be an object') + else: + translation_keys = [] + for language in LANGUAGES: + content = translations.get(language) + if not isinstance(content, dict): + errors.append(f'{label}: translations.{language} must be an object') + translation_keys.append(set()) + continue + translation_keys.append(set(content)) + missing = sorted(TRANSLATION_FIELDS - set(content)) + for field in missing: + errors.append(f'{label}: translations.{language}.{field} is required') + if not content.get('primary_limitation'): + errors.append(f'{label}: translations.{language}.primary_limitation is required') + if len(translation_keys) == 2 and translation_keys[0] != translation_keys[1]: + errors.append(f'{label}: translation fields must match') + + evidence = entry.get('evidence') + if maturity == 'production-verified' and not evidence: + errors.append(f'{label}: production-verified entry requires evidence') + if evidence is not None: + if not isinstance(evidence, list): + errors.append(f'{label}: evidence must be a list') + else: + for evidence_index, item in enumerate(evidence): + if not isinstance(item, dict) or not _is_https_url(item.get('url')): + errors.append( + f'{label}: evidence URL must use https (item {evidence_index})' + ) + + if maturity == 'production-verified': + for field_name, field_path in PRODUCTION_FIELDS: + if not _nested_value(entry, field_path): + errors.append( + f'{label}: production-verified entry requires {field_name}' + ) + + benchmarks = entry.get('benchmarks', []) + if not isinstance(benchmarks, list): + errors.append(f'{label}: benchmarks must be a list') + else: + for benchmark_index, benchmark in enumerate(benchmarks): + if not isinstance(benchmark, dict): + errors.append(f'{label}: benchmark {benchmark_index} must be an object') + continue + for field in BENCHMARK_FIELDS: + if not benchmark.get(field): + errors.append( + f'{label}: benchmark {benchmark_index} requires {field}' + ) + + return errors diff --git a/web-pages/product-site/requirements-site.txt b/web-pages/product-site/requirements-site.txt new file mode 100644 index 0000000000..4bba214c68 --- /dev/null +++ b/web-pages/product-site/requirements-site.txt @@ -0,0 +1,3 @@ +Jinja2==3.1.6 +beautifulsoup4==4.13.4 +pytest==8.4.1 diff --git a/web-pages/product-site/tests/test_registry.py b/web-pages/product-site/tests/test_registry.py new file mode 100644 index 0000000000..1c440c859c --- /dev/null +++ b/web-pages/product-site/tests/test_registry.py @@ -0,0 +1,92 @@ +from __future__ import annotations + +import copy +import sys +from pathlib import Path + +import pytest + + +SITE_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(SITE_ROOT)) + +from registry import deployment_pairs, load_registry, validate_registry # noqa: E402 + + +REGISTRY = SITE_ROOT / 'data' / 'deployments.json' +EXPECTED_IDS = { + 'vllm', + 'llama-cpp', + 'openai-api', + 'realtime', + 'containers', + 'cpu-runtime', + 'production', +} + + +@pytest.fixture +def valid_registry(): + return load_registry(REGISTRY) + + +def test_checked_in_registry_is_valid(valid_registry): + assert validate_registry(valid_registry) == [] + + +def test_registry_has_all_product_routes(valid_registry): + assert {item['id'] for item in valid_registry['deployments']} == EXPECTED_IDS + + +def test_language_pairs_have_identical_fields(valid_registry): + assert all(set(zh) == set(en) for zh, en in deployment_pairs(valid_registry)) + + +def test_production_entry_requires_evidence(valid_registry): + data = copy.deepcopy(valid_registry) + entry = next(item for item in data['deployments'] if item['maturity'] == 'production-verified') + del entry['evidence'] + + assert validate_registry(data) == [ + f"{entry['id']}: production-verified entry requires evidence" + ] + + +def test_production_entry_requires_verification_contract(valid_registry): + data = copy.deepcopy(valid_registry) + entry = next(item for item in data['deployments'] if item['maturity'] == 'production-verified') + del entry['tested']['verified'] + + assert validate_registry(data) == [ + f"{entry['id']}: production-verified entry requires tested.verified" + ] + + +def test_duplicate_route_is_rejected(valid_registry): + data = copy.deepcopy(valid_registry) + data['deployments'][1]['routes']['zh'] = data['deployments'][0]['routes']['zh'] + + errors = validate_registry(data) + + assert any('duplicate route' in error for error in errors) + + +def test_evidence_must_be_https(valid_registry): + data = copy.deepcopy(valid_registry) + data['deployments'][0]['evidence'][0]['url'] = 'http://example.com/evidence' + + assert any('evidence URL must use https' in error for error in validate_registry(data)) + + +def test_benchmark_requires_reproducibility_fields(valid_registry): + data = copy.deepcopy(valid_registry) + entry = data['deployments'][0] + entry['benchmarks'] = [{ + 'model': 'Fun-ASR-Nano-2512', + 'runtime': 'vLLM', + 'hardware': 'NVIDIA GPU', + }] + + errors = validate_registry(data) + + assert any('benchmark 0 requires workload' in error for error in errors) From ea4b851cf735e5bcf05066c7c5d97a469126b8da Mon Sep 17 00:00:00 2001 From: LauraGPT Date: Sun, 26 Jul 2026 16:18:27 +0000 Subject: [PATCH 04/14] feat(site): add deployment recommendation engine --- web-pages/product-site/data/deployments.json | 2 +- web-pages/product-site/selector.py | 69 +++++++++++++++++++ web-pages/product-site/tests/test_selector.py | 48 +++++++++++++ 3 files changed, 118 insertions(+), 1 deletion(-) create mode 100644 web-pages/product-site/selector.py create mode 100644 web-pages/product-site/tests/test_selector.py diff --git a/web-pages/product-site/data/deployments.json b/web-pages/product-site/data/deployments.json index 4714d837b8..c2b82128f1 100644 --- a/web-pages/product-site/data/deployments.json +++ b/web-pages/product-site/data/deployments.json @@ -209,7 +209,7 @@ "id": "containers", "routes": {"zh": "/deploy/containers.html", "en": "/en/deploy/containers.html"}, "maturity": "community-verified", - "selector_rank": 50, + "selector_rank": 25, "workloads": ["private-api", "batch", "realtime"], "hardware": ["kubernetes", "cpu", "nvidia-gpu"], "priorities": ["portability", "compatibility"], diff --git a/web-pages/product-site/selector.py b/web-pages/product-site/selector.py new file mode 100644 index 0000000000..76f181d9bd --- /dev/null +++ b/web-pages/product-site/selector.py @@ -0,0 +1,69 @@ +"""Deterministic deployment recommendation scoring.""" + +from __future__ import annotations + +from typing import Any + + +SUPPORTED_VALUES = { + 'workload': {'batch', 'realtime', 'private-api', 'edge'}, + 'hardware': {'nvidia-gpu', 'cpu', 'desktop-edge-gpu', 'kubernetes'}, + 'priority': {'throughput', 'latency', 'portability', 'compatibility'}, +} +MATCH_WEIGHTS = { + 'workload': 4, + 'hardware': 3, + 'priority': 2, +} + + +def _validate_choice(name: str, value: str) -> None: + if value not in SUPPORTED_VALUES[name]: + raise ValueError(f'Unsupported {name}: {value}') + + +def recommend( + entries: list[dict[str, Any]], + workload: str, + hardware: str, + priority: str, +) -> dict[str, Any]: + """Return the highest-scoring registry entry with a stable tie break.""" + choices = { + 'workload': workload, + 'hardware': hardware, + 'priority': priority, + } + for name, value in choices.items(): + _validate_choice(name, value) + + scored: list[tuple[int, int, str, dict[str, Any]]] = [] + for entry in entries: + if entry.get('selectable', True) is False: + continue + score = 0 + score += MATCH_WEIGHTS['workload'] * (workload in entry.get('workloads', [])) + score += MATCH_WEIGHTS['hardware'] * (hardware in entry.get('hardware', [])) + score += MATCH_WEIGHTS['priority'] * (priority in entry.get('priorities', [])) + scored.append(( + -score, + int(entry.get('selector_rank', 10_000)), + str(entry.get('id', '')), + entry, + )) + + if not scored: + raise ValueError('No selectable deployment entries') + + negative_score, _, _, entry = min(scored) + result = dict(entry) + result['match_score'] = -negative_score + result['selection_reason'] = { + language: entry['translations'][language]['selection_reason'] + for language in ('zh', 'en') + } + result['primary_limitation'] = { + language: entry['translations'][language]['primary_limitation'] + for language in ('zh', 'en') + } + return result diff --git a/web-pages/product-site/tests/test_selector.py b/web-pages/product-site/tests/test_selector.py new file mode 100644 index 0000000000..30f1ee0bf6 --- /dev/null +++ b/web-pages/product-site/tests/test_selector.py @@ -0,0 +1,48 @@ +from __future__ import annotations + +import sys +from pathlib import Path + +import pytest + + +SITE_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(SITE_ROOT)) + +from registry import load_registry # noqa: E402 +from selector import recommend # noqa: E402 + + +@pytest.fixture +def entries(): + return load_registry(SITE_ROOT / 'data' / 'deployments.json')['deployments'] + + +@pytest.mark.parametrize( + ('workload', 'hardware', 'priority', 'expected'), + [ + ('batch', 'nvidia-gpu', 'throughput', 'vllm'), + ('edge', 'cpu', 'portability', 'llama-cpp'), + ('private-api', 'kubernetes', 'compatibility', 'containers'), + ('realtime', 'nvidia-gpu', 'latency', 'realtime'), + ], +) +def test_recommendation_matrix(workload, hardware, priority, expected, entries): + result = recommend(entries, workload, hardware, priority) + + assert result['id'] == expected + assert result['match_score'] == 9 + assert set(result['selection_reason']) == {'zh', 'en'} + assert set(result['primary_limitation']) == {'zh', 'en'} + + +def test_unknown_selector_value_is_rejected(entries): + with pytest.raises(ValueError, match='Unsupported hardware: tpu'): + recommend(entries, 'batch', 'tpu', 'throughput') + + +def test_registry_order_does_not_change_tie_break(entries): + forward = recommend(entries, 'private-api', 'kubernetes', 'compatibility') + reverse = recommend(list(reversed(entries)), 'private-api', 'kubernetes', 'compatibility') + + assert forward['id'] == reverse['id'] == 'containers' From 80efa0009f0fd1697e89d704478d145286bf09ab Mon Sep 17 00:00:00 2001 From: LauraGPT Date: Sun, 26 Jul 2026 16:22:57 +0000 Subject: [PATCH 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0000000000..2c89bfb4ae --- /dev/null +++ b/web-pages/product-site/legacy/blog/cantonese-speech-recognition.html @@ -0,0 +1,137 @@ + + + + + +粤语语音识别:SenseVoice 原生支持粤语口语(Whisper 会转成普通话)| FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

粤语语音识别:SenseVoice 原生输出地道粤语口语(Whisper 会转成普通话)

+

2026-06-21 · FunASR 团队

+ +

全球约有 8500 万粤语使用者,但开源语音识别对粤语的支持一直很弱。Whisper 名义上有粤语,实际上它把粤语当成中文(zh)处理、转写成普通话书面语——唔 变 不、嘅 变 的、呢 变 這,地道的粤语口语就这么没了。

+

SenseVoice(FunAudioLLM 团队开源的多语种语音理解模型)原生支持粤语(yue):自动识别语种、保留真正的粤语口语用字,一次非自回归前向就给出结果。下面是同一段粤语音频的真实对比。

+ +

真实对比:同一段粤语音频

+

用 SenseVoice 自带的粤语示例 yue.mp3 实测,两个模型的真实输出:

+ + + + +
模型识别语种输出
SenseVoiceyue(粤语)呢几个字都表达唔到,我想讲嘅意思。
Whisper-smallzh(中文)這幾個字都表達不到我想講的意思
+

差别一目了然:SenseVoice 保留了粤语标志性的 呢 / 唔 / 嘅;Whisper 没有独立的粤语,把它识别成中文并转成了普通话书面语(呢→這、唔→不、嘅→的)——意思大致对,但已经不是粤语了。

+ +

3 行代码跑通粤语识别

+
pip install funasr
+
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)
+res = model.generate(input="cantonese.wav", cache={}, language="yue", use_itn=True)
+
+print(rich_transcription_postprocess(res[0]["text"]))
+# 呢几个字都表达唔到，我想讲嘅意思。
+

language 可以直接写 "yue",也可以写 "auto" 让模型自动判语种——实测这段音频在 auto 下同样被正确判为 yue 并给出一致结果。

+ +

原始输出里还带了什么

+

SenseVoice 的原始输出每段开头有一串标签:

+
<|yue|><|NEUTRAL|><|Speech|><|withitn|>呢几个字都表达唔到，我想讲嘅意思。
+

含义:<|yue|> = 语种是粤语;<|NEUTRAL|> = 情感;<|Speech|> = 音频事件(纯人声);<|withitn|> = 已做逆文本归一化。也就是说,识别粤语的同时,语种 / 情感 / 音频事件全都免费拿到了。用 rich_transcription_postprocess() 一行就能清洗成纯文本。

+ +

为什么粤语识别选 SenseVoice

+ + + + + + + + +
SenseVoiceWhisper
粤语口语用字(呢/唔/嘅)✅ 原生保留❌ 转成普通话
自动语种识别✅ 直接判出 yue归为 zh
情感 / 音频事件✅ 一次出❌
逆文本归一化(ITN)✅ 内置部分
速度非自回归,比 Whisper-Large 约快 15×自回归基准
许可证开源,可商用开源
+

如果你的场景是普通话 + 粤语混合(港澳、客服、媒体字幕),SenseVoice 一个模型就能两种都覆盖;要更高精度的离线转写,可以看同属 FunASR 家族的 Fun-ASR-Nano。更完整的中文准确率对比见 FunASR vs Whisper benchmark。

+ +

FunASR 全家桶都是开源的——工业级 ASR / VAD / 标点 / 说话人 / 情感事件 / LLM-ASR,粤语开箱即用。觉得有用就点个 Star 支持一下 👇

⭐ Star SenseVoice

也欢迎 Star:FunASR · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/chinese-speech-recognition.html b/web-pages/product-site/legacy/blog/chinese-speech-recognition.html new file mode 100644 index 0000000000..4e46834e06 --- /dev/null +++ b/web-pages/product-site/legacy/blog/chinese-speech-recognition.html @@ -0,0 +1,160 @@ + + + + + +中文语音识别(普通话)Python 实战:用 FunASR 又快又准(Fun-ASR-Nano / SenseVoice / Paraformer)| FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

中文语音识别(普通话)Python 实战:用 FunASR 又快又准

+

2026-06-22 · FunASR 团队

+ +

中文语音识别一直是通用模型的难点——OpenAI Whisper 在中文上的字错率(CER)约 20%,同音字、口语、专名经常出错。FunASR(通义实验室开源)专为中文打造,在标准中文测试集上 CER 远低于 Whisper。默认推荐旗舰 Fun-ASR-Nano(LLM-ASR),CPU 场景用 SenseVoice / Paraformer。下面是实战上手,代码均为真实实测。

+ +

3 行代码做中文识别(旗舰 Fun-ASR-Nano,实测输出)

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", disable_update=True, device="cuda")
+res = model.generate(input="audio.wav")
+
+print(res[0]["text"])
+# 欢迎大家来体验达摩院推出的语音识别模型。
+

Fun-ASR-Nano 是基于 LLM 的旗舰识别模型(SenseVoice 编码器 + Qwen3 解码),支持中英日及中文方言/口音,对上下文/难例/专名更鲁棒,是中文识别的默认推荐。需要 31 语种时使用独立的 Fun-ASR-MLT-Nano。大规模可用 vLLM 加速(约 340× 实时),见 Fun-ASR-Nano 指南。

+ +

选对中文模型(按场景)

+ + + + + +
你的情况选中文 CER
有 GPU / 要最强⭐ Fun-ASR-Nano8.06%
CPU / 边缘 / 要最快SenseVoice7.81%
CPU + 纯中文 + 时间戳/热词Paraformer10.18%
+

三者 CER 都远好于 Whisper(~20%)。完整选型见 FunASR 模型选型指南。CPU 场景的两个轻量选择:

+
# SenseVoice —— CPU 首选,非自回归极快,多语种+情感
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)
+res = model.generate(input="audio.wav", language="auto", use_itn=True)
+print(rich_transcription_postprocess(res[0]["text"]))
+# 欢迎大家来体验达摩院推出的语音识别模型。
+
+# Paraformer —— 纯中文 + 字级时间戳 + 热词
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc")
+res = model.generate(input="audio.wav", batch_size_s=300)
+print(res[0]["text"])           # 欢迎大家来体验达摩院推出的语音识别模型。
+# res[0]["timestamp"] -> [[880, 1120], [1120, 1360], ...] 每个字的起止毫秒
+ +

为什么中文识别选 FunASR(而不是 Whisper)

+ + + + + + +
中文测试集 CER(越低越好)错字率
FunASR · Fun-ASR-Nano(旗舰)8.06%
FunASR · SenseVoice7.81%
FunASR · Paraformer10.18%
Whisper-large-v320.02%
+

除了更准,FunASR 对中文还内置一整套实用能力:逆文本归一化(ITN)("一千二百"→"1200")、标点恢复、热词/专名定制、字级时间戳、说话人分离。完整对比见 FunASR vs Whisper benchmark 和 vs faster-whisper(中文/粤语)。

+ +

常见进阶(都是一行接上)

+ + +

命令行 / 服务化

+
# 命令行转写,可出 srt / json,带说话人
+funasr audio.wav --model paraformer-zh -f srt --spk
+
+# OpenAI 兼容的转写服务(默认旗舰 fun-asr-nano)
+funasr-server --device cuda
+

见 命令行转写 和 自托管替代云 API。

+ +

FunASR 是中文语音识别的开源首选(MIT,可商用)——旗舰 Fun-ASR-Nano + SenseVoice + Paraformer。觉得有用就点个 Star 支持一下 👇

⭐ Star Fun-ASR

也欢迎 Star:FunASR · SenseVoice · FunClip

+ +

相关文章

+ +
+ + + diff --git a/web-pages/product-site/legacy/blog/fun-asr-nano-guide.html b/web-pages/product-site/legacy/blog/fun-asr-nano-guide.html new file mode 100644 index 0000000000..cbbf8c4d68 --- /dev/null +++ b/web-pages/product-site/legacy/blog/fun-asr-nano-guide.html @@ -0,0 +1,168 @@ + + + + + +Fun-ASR-Nano 使用指南：800M 端到端语音识别大模型，支持中英日与中文方言/口音 | FunASR 博客 + + + + + + + + + + + + + + + +
+

Fun-ASR-Nano 使用指南：800M 端到端语音识别大模型，支持中英日与中文方言/口音

+

FunASR 技术博客 · 2026-07-15 更新 · 主力模型

+

Fun-ASR-Nano 是 FunAudioLLM 团队推出的端到端语音识别大模型，基于「Audio Encoder + Adaptor + LLM(Qwen)」架构，参数量约 800M，在数千万小时真实语音上训练而成。它是我们当前重点推荐的旗舰 ASR 模型。

+

核心亮点：

+
    +
  • 支持中文、英文和日语；中文覆盖 7 大方言（吴、粤、闽、客、赣、湘、晋）与 26 种地域口音
    • +
  • 支持热词和低延迟流式服务
    • +
  • FunASR pipeline 可组合独立的 VAD + cam++ + 标点模型实现说话人分离（并非 Nano checkpoint 原生输出）
    • +
  • 甚至支持歌词识别、Rap 语音识别
    • +
+

时间戳边界：发布的 model.pt checkpoint 不含已训练的 CTC 对齐权重，因此不提供可靠的原生字级时间戳。离线服务可通过 VAD 返回可靠的片段级起止时间；需要准确字级时间戳时请使用 Paraformer。详情见 issue #106。

+

需要覆盖 31 种语言时，请使用独立的 Fun-ASR-MLT-Nano-2512 checkpoint；不要把 MLT-Nano 的语言范围套用到本页的旗舰 Nano。

+ +

1. 安装

+
pip install -U funasr torch torchaudio
+ +

2. 基础推理（含热词）

+

下面是已实测可运行的最小示例（自动下载模型）：

+
from funasr import AutoModel
+
+model = AutoModel(
+    model="FunAudioLLM/Fun-ASR-Nano-2512",
+    trust_remote_code=True,
+    remote_code="./model.py",
+    device="cuda:0",
+    hub="hf",          # 国内可用 hub="ms" 走 ModelScope
+)
+wav = f"{model.model_path}/example/zh.mp3"   # 模型自带示例音频
+res = model.generate(
+    input=[wav], cache={}, batch_size=1,
+    hotwords=["开放时间"],   # 热词，提升专有名词召回
+    language="中文",
+    itn=True,               # 逆文本归一化（数字/日期规整）
+)
+print(res[0]["text"])
+

实测输出：开放时间早上九点至下午五点。——热词「开放时间」被准确识别。

+ +

3. 长音频：加 VAD 分段

+
model = AutoModel(
+    model="FunAudioLLM/Fun-ASR-Nano-2512",
+    trust_remote_code=True, remote_code="./model.py",
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0", hub="hf",
+)
+res = model.generate(input=[wav], cache={}, batch_size=1, language="中文")
+ +

4. 说话人分离（谁在什么时候说了什么）

+

说话人分离并非 Nano checkpoint 原生输出；下面由 FunASR pipeline 组合 VAD + 声纹（cam++）+ 标点，得到带说话人标签的逐句结果：

+
model = AutoModel(
+    model="FunAudioLLM/Fun-ASR-Nano-2512",
+    trust_remote_code=True, remote_code="./model.py",
+    vad_model="fsmn-vad", vad_kwargs={"max_single_segment_time": 30000},
+    spk_model="cam++", punc_model="ct-punc",
+    device="cuda:0", hub="hf",
+)
+res = model.generate(input=[wav], cache={}, batch_size=1, language="中文")
+for sent in res[0]["sentence_info"]:
+    print(sent["spk"], sent["text"])
+

说话人分离需要从源码安装 FunASR：pip install git+https://github.com/modelscope/FunASR.git。

+ +

5. 高吞吐 / 流式部署

+ + + + + + + +
场景推荐方式
大规模离线批量转写AutoModelVLLM（vLLM 后端，高吞吐）
实时低延迟流式FunASRNanoStreamingVLLM（chunk 流式）
单机 / 快速试用上面的 AutoModel 即可
+

vLLM 路径对版本较敏感，建议 vLLM 0.12.0 + torch 2.9.0；完整示例见 Fun-ASR 仓库。

+ +
+

开始使用 Fun-ASR-Nano

+

主力旗舰模型，支持中英日 + 中文方言/口音。需要 31 语种请选 MLT-Nano。觉得有用就到 GitHub 点个 Star ⭐

+Fun-ASR GitHub ★ +
+

延伸阅读：SenseVoice 部署指南 · 对比 Whisper · 快速上手

+ +

相关文章

+ +
+ + + diff --git a/web-pages/product-site/legacy/blog/funasr-cli-transcribe-command-line.html b/web-pages/product-site/legacy/blog/funasr-cli-transcribe-command-line.html new file mode 100644 index 0000000000..70b2fb0b78 --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-cli-transcribe-command-line.html @@ -0,0 +1,129 @@ + + +用 FunASR 命令行转写音频:一行命令出文本 / JSON / SRT 字幕 | FunASR + + + + + + + +
+

用 FunASR 命令行转写音频:一行命令出文本 / JSON / SRT 字幕

+

FunASR 技术博客 · 2026-06-18 · 教程

+

不想写 Python?FunASR 自带命令行工具,装好就能在终端里把音频转成文字、JSON 或 SRT 字幕。本地运行、免费、对中文尤其强。下面每条命令都是实测过的。

+

安装

+
pip install -U torch torchaudio
+pip install -U funasr   # 推荐 funasr ≥ 1.3.26
+

最简单:转成文字

+
funasr audio.wav
+

直接把识别文本打印到终端。默认用 SenseVoice(非自回归,极快,50+ 语言)。

+

生成 SRT 字幕

+
funasr audio.wav -f srt -o ./subs
+

在 ./subs/ 下生成 audio.srt。加 --spk 还能按说话人分句、带真实时间戳:

+
funasr meeting.wav --spk -f srt -o ./subs
+
1
+00:00:02,919 --> 00:00:08,169
+大家好,今天聊一下第三季度的计划。
+
+2
+00:00:10,029 --> 00:00:18,550
+我先同步进度,核心功能完成了八成。
+

结构化 JSON 输出

+
funasr audio.wav -f json
+
{
+  "text": "大家好今天聊一下第三季度的计划...",
+  "file": "audio.wav",
+  "model": "sensevoice",
+  "language": "auto",
+  "audio_duration_s": 59.52,
+  "processing_s": 2.17
+}
+

常用选项

+ + + + + + + +
命令作用
-f text/json/srt/tsv输出格式(默认 text)
--spk说话人分离(谁在何时说)
--model sensevoice/paraformer/fun-asr-nano选模型(fun-asr-nano 精度最高)
--hotwords "关键词,术语"热词,提升专有名词
-o ./out输出目录
funasr a.wav b.wav一次转多个文件
+

作为 API 服务部署(OpenAI 兼容)

+
funasr-server --device cuda     # localhost:8000, POST /v1/audio/transcriptions
+

用任何 OpenAI SDK 直接调:

+
from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")
+r = client.audio.transcriptions.create(model="sensevoice", file=open("audio.wav","rb"))
+print(r.text)
+

为什么用 FunASR CLI

+
    +
  • 本地、免费、隐私:无需联网、无 API key。
    • +
  • 快:SenseVoice 非自回归,远快于 Whisper(实测对比),CPU 也能实时。
    • +
  • 中文更准 + 50+ 语言;字幕、说话人、热词、JSON 开箱即用。
    • +
+

FunASR 是通义实验室开源的工业级语音识别工具包。

在 GitHub 上 Star FunASR ★
+ +

相关文章

+ +
+
FunASR · 通义实验室开源语音识别 · GitHub
+ diff --git a/web-pages/product-site/legacy/blog/funasr-llama-cpp-whisper-cpp-alternative.html b/web-pages/product-site/legacy/blog/funasr-llama-cpp-whisper-cpp-alternative.html new file mode 100644 index 0000000000..76816774ca --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-llama-cpp-whisper-cpp-alternative.html @@ -0,0 +1,145 @@ + + + + + +FunASR 跑进 llama.cpp:中文 ASR 的 whisper.cpp 替代品(CPU/零 Python/下载即用)| FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

FunASR 跑进 llama.cpp:中文语音识别的 whisper.cpp 替代品(CPU、零 Python、下载即用)

+

2026-06-20 · FunASR 团队

+ +

whisper.cpp 是端侧语音识别的事实标准——单个自包含二进制、CPU 就能跑、零依赖。但 Whisper 在中文上偏弱。现在 FunASR 有了 llama.cpp / GGUF 运行时:一样的"下载即用"体验,**单个静态二进制、零 Python、内置 VAD、吃任意音频**,而且在中文 CPU 上比 whisper.cpp 准约 2.7 倍。

+ +

👉 想直接下载? 各平台预编译二进制(Linux/macOS/Windows)+ 一站式 quickstart 与 benchmark 见 在 CPU 上运行 FunASR(llama.cpp 下载页)。

+

3 步跑通(全部实测)

+
# 1. 下预编译二进制(linux-x64 / linux-arm64 / macos-arm64 / windows-x64)
+wget https://github.com/modelscope/FunASR/releases/download/runtime-llamacpp-v0.1.9/funasr-llamacpp-linux-x64-vulkan.tar.gz
+tar xzf funasr-llamacpp-linux-x64-vulkan.tar.gz
+# Windows Vulkan 包: funasr-llamacpp-windows-x64-vulkan.zip（运行时加 --backend vulkan）
+
+# 2. 一条命令下模型(无需 torch / funasr 环境)
+bash download-funasr-model.sh sensevoice
+
+# 3. 转写 —— 直接打印文字
+./llama-funasr-sensevoice -m funasr-gguf/sensevoice-small-f16.gguf \
+    --vad funasr-gguf/fsmn-vad.gguf -a audio.wav
+# -> 开放时间早上九点至下午五点
+

没有 Python、没有编译、没有依赖地狱——下二进制、下模型、跑,出中文文字。和 whisper.cpp 一模一样的手感。

+ +

你能得到什么

+ + + + + +
模型特点速度(CPU)
SenseVoice多语种 + 语种/情感/事件标签~20× 实时
Paraformer非自回归,最快~22× 实时
Fun-ASR-NanoLLM-ASR,最准LLM 解码
+
    +
  • 内置 FSMN-VAD:长音频自动分段(`--vad fsmn-vad.gguf`),裸二进制即达参考精度,无需 Python 前端
    • +
  • 任意音频:wav / mp3 / flac、任意采样率/声道,二进制内部重采样
    • +
  • 多平台预编译:Linux x64/arm64、macOS arm64、Windows x64,含 Linux/Windows Vulkan 与 Windows CUDA 加速包(Release);其它平台一条 CMake 命令自构建
    • +
+ +

中文 CPU 上 vs whisper.cpp

+

184 段中文音频、同机 CPU 8 线程、字符级 CER(越低越准):

+ + + + + + + + +
系统CER ↓体积
FunASR SenseVoice8.01 %449 MB
FunASR Paraformer9.85 %401 MB
FunASR Fun-ASR-Nano8.30 %enc + Qwen3-0.6B
whisper.cpp base31.33 %142 MB
whisper.cpp small22.12 %466 MB
whisper.cpp large-v3-turbo23.15 %1.6 GB
+

同体积下(SenseVoice 449 MB ≈ whisper small 466 MB),FunASR 的 CER 低约 2.7 倍,而且更快。完整方法学见仓库 BENCHMARKS.md。

+ +

为什么中文上能赢

+

① 训练数据:SenseVoice / Paraformer / Fun-ASR-Nano 主要在大规模中文上训练,Whisper 是通用多语种模型、中文只占一小片;② 架构:Paraformer 非自回归(CIF 一次前向)、SenseVoice 编码器 + CTC(一次前向),比 Whisper 的自回归(逐 token)更快。

+ +

模型 GGUF

+

预转 GGUF 在 Hugging Face:SenseVoiceSmall-GGUF · Paraformer-GGUF · Fun-ASR-Nano-GGUF · fsmn-vad-GGUF。`download-funasr-model.sh` 会自动拉。

+ +

FunASR 全家桶开源——ASR / VAD / 标点 / 说话人 / 情感事件 / LLM-ASR / 现在还有 llama.cpp 端侧运行时。觉得有用点个 Star 👇

⭐ Star FunASR

也欢迎 Star:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + diff --git a/web-pages/product-site/legacy/blog/funasr-realtime-streaming-asr.html b/web-pages/product-site/legacy/blog/funasr-realtime-streaming-asr.html new file mode 100644 index 0000000000..0b8d501ad0 --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-realtime-streaming-asr.html @@ -0,0 +1,136 @@ + + +用 FunASR 实现实时流式语音识别(边说边出字) | FunASR + + + + + + + +
+

用 FunASR 实现实时流式语音识别(边说边出字)

+

FunASR 技术博客 · 2026-06-17 · 教程

+

实时字幕、语音助手、会议转写——这些场景要的不是"录完再转",而是边说边出字:用户话音未落,文字已经在屏幕上滚动。这就是流式语音识别(Streaming ASR)。

+

FunASR 的流式 Paraformer 用分块(chunk)+ 缓存(cache)解码,可以做到 600ms 级延迟的实时出字,而且 CPU 上就能跑。

+

安装

+
pip install -U funasr modelscope
+

完整代码(可直接运行)

+
from funasr import AutoModel
+import soundfile as sf
+
+chunk_size = [0, 10, 5]          # 600 ms chunks (10 * 60 ms center)
+encoder_chunk_look_back = 4      # encoder look-back chunks
+decoder_chunk_look_back = 1      # decoder look-back chunks
+
+model = AutoModel(model="paraformer-zh-streaming")
+
+audio, sr = sf.read("speech.wav", dtype="float32")   # 16 kHz mono
+chunk_stride = chunk_size[1] * 960                    # 600 ms @ 16 kHz
+
+cache = {}
+n_chunks = (len(audio) - 1) // chunk_stride + 1
+for i in range(n_chunks):
+    chunk = audio[i * chunk_stride : (i + 1) * chunk_stride]
+    is_final = i == n_chunks - 1
+    res = model.generate(
+        input=chunk, cache=cache, is_final=is_final,
+        chunk_size=chunk_size,
+        encoder_chunk_look_back=encoder_chunk_look_back,
+        decoder_chunk_look_back=decoder_chunk_look_back,
+    )
+    if res[0]["text"]:
+        print(res[0]["text"], end="", flush=True)   # emit partial text
+

输出是逐步增长的(示例)

+
今天          # ~600 ms after speech starts
+今天天气
+今天天气真
+今天天气真不错    # is_final=True
+

每喂入一个 600ms 的音频块,模型就吐出这一块新识别到的文字,屏幕上的句子逐字增长,直到 is_final=True 收尾。实测一段 12 秒音频被切成 20 个块,逐块返回增量结果。

+

关键参数

+ + + + + + +
参数含义
chunk_size=[0,10,5][左看, 中心, 右看] 块大小,单位 60ms。中心 10 → 每块 600ms(延迟与精度的平衡点)
encoder_chunk_look_back=4编码器回看的历史块数(越大上下文越足、延迟略增)
decoder_chunk_look_back=1解码器回看的历史块数
cache={}跨块传递的状态,必须在多次调用间复用同一个 dict
is_final最后一块置 True,触发收尾解码
+

最佳实践:2-pass(流式 + 离线)

+

流式模型为了低延迟,精度会略低于离线模型。生产里常用 2-pass 方案:

+
    +
  • 第一遍(流式):用流式 Paraformer 实时出字,给用户即时反馈;
    • +
  • 第二遍(离线):一句话结束后(用 VAD 判定停顿),把整段音频再用 SenseVoice / 离线 Paraformer 重转一遍,得到更准的最终结果替换屏幕上的临时文本。
    • +
+

FunASR 官方的 WebSocket 服务(funasr-runtime-sdk-online-cpu)就内置了这套 2-pass 流式协议,可直接部署。

+

典型场景

+
    +
  • 实时字幕:直播、会议、课堂的即时上字幕。
    • +
  • 语音助手:边听边理解,降低端到端响应延迟。
    • +
  • 客服/质检:通话实时转写 + 实时关键词告警。
    • +
+

FunASR 是通义实验室开源的工业级语音识别工具包,离线 + 流式全覆盖,中文又快又准。

在 GitHub 上 Star FunASR ★
+ +

相关文章

+ +
+
FunASR · 通义实验室开源语音识别 · GitHub
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/funasr-speaker-diarization.html b/web-pages/product-site/legacy/blog/funasr-speaker-diarization.html new file mode 100644 index 0000000000..bf40856197 --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-speaker-diarization.html @@ -0,0 +1,125 @@ + + +用 FunASR 做说话人分离:一次调用输出「谁在何时说了什么」 | FunASR + + + + + + + +
+

用 FunASR 做说话人分离:一次调用输出「谁在何时说了什么」

+

FunASR 技术博客 · 2026-06-17 · 教程

+

做会议纪要、客服质检、访谈整理时,你不只想知道说了什么,还想知道每句话是谁说的。这件事叫说话人分离(Speaker Diarization)。

+

常见做法是把两个工具拼起来:pyannote.audio 负责切分说话人 + Whisper 负责转写,再手动把时间轴对齐。这套流程要申请 HuggingFace 授权、装一堆依赖、自己写对齐逻辑,而且对中文不算友好。

+

用 FunASR,一次 generate() 调用就够了。VAD、语音识别、标点、说话人嵌入(CAM++)被串成一条流水线,直接返回每一句的说话人编号、时间戳和文本。

+

安装

+
pip install -U funasr modelscope
+

完整代码

+
from funasr import AutoModel
+
+# One model handle = VAD + ASR + punctuation + speaker embedding (CAM++)
+model = AutoModel(
+    model="paraformer-zh",   # or "iic/SenseVoiceSmall"
+    vad_model="fsmn-vad",
+    punc_model="ct-punc",
+    spk_model="cam++",       # speaker diarization
+)
+
+res = model.generate(input="meeting.wav", batch_size_s=300)
+
+for s in res[0]["sentence_info"]:
+    print(f'[{s["start"]/1000:.1f}s-{s["end"]/1000:.1f}s] '
+          f'speaker {s["spk"]}: {s["sentence"]}')
+

输出长这样(示例)

+
[0.0s-3.2s] speaker 0: 大家好,今天的会议主要讨论第三季度的产品规划。
+[3.5s-7.1s] speaker 1: 我先同步一下进度,核心功能已经完成了百分之八十。
+[7.4s-9.9s] speaker 2: 测试这边可能还需要两周时间。
+[10.2s-13.6s] speaker 0: 好的,那我们把上线时间往后调一周。
+

每个 sentence_info 元素包含四个字段:

+ + + + +
字段含义
spk说话人编号,CAM++ 自动聚类得到,无需事先指定人数
start / end该句的起止时间(毫秒)
sentence该句文本(已加标点)
+

实测效果

+

我们在一段 227 秒的多人会议录音上实测:FunASR 自动切分出 77 个句子、区分出 11 个不同说话人,全程不需要预先告诉它有几个人。整条流水线只用一次 generate() 调用、一个 Python 依赖。

+

为什么用 FunASR 而不是 pyannote + Whisper

+
    +
  • 一个调用,不用对齐:转写和说话人标签天然对齐,不必自己拼时间轴。
    • +
  • 不需要 HuggingFace 授权:pyannote 的说话人模型是 gated 的,要申请、要 token;FunASR 模型公开可下载。
    • +
  • 中文更准:Paraformer / SenseVoice 在中文上 CER 显著低于 Whisper(实测对比)。
    • +
  • CPU 也能跑:没有 GPU 也能离线出结果。
    • +
+

进阶

+
    +
  • 把 model="paraformer-zh" 换成 "iic/SenseVoiceSmall" 即可用 SenseVoice(还自带情感/事件标签)。
    • +
  • 遍历 sentence_info 即可导出 SRT 字幕或按说话人聚合成会议纪要。
    • +
+

FunASR 是通义实验室开源的工业级语音识别工具包,中文场景又快又准。

在 GitHub 上 Star FunASR ★
+ +

相关文章

+ +
+
FunASR · 通义实验室开源语音识别 · GitHub
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/funasr-transcribe-long-audio.html b/web-pages/product-site/legacy/blog/funasr-transcribe-long-audio.html new file mode 100644 index 0000000000..f12b3b1bba --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-transcribe-long-audio.html @@ -0,0 +1,121 @@ + + +用 FunASR 转写超长音频:1 小时录音也能一次搞定 | FunASR + + + + + + + +
+

用 FunASR 转写超长音频:1 小时录音也能一次搞定

+

FunASR 技术博客 · 2026-06-17 · 教程

+

转写一段 1 小时的播客、讲座或会议录音,用 Whisper 会很痛苦:它一次只处理 30 秒,你得自己把音频切片、逐片识别、再把结果拼起来,还要处理切片边界把词切断的问题。

+

FunASR 不用这么麻烦。它内置 VAD(语音端点检测),一次 generate() 调用就能吃下任意时长的音频——自动切句、批量解码、拼回完整文本,你一行切片代码都不用写。

+

安装

+
pip install -U funasr modelscope
+

完整代码(可直接运行)

+
from funasr import AutoModel
+
+model = AutoModel(
+    model="iic/SenseVoiceSmall",   # or "paraformer-zh"
+    vad_model="fsmn-vad",          # built-in voice activity detection
+    vad_kwargs={"max_single_segment_time": 30000},
+)
+
+# One call on a full 1-hour file - VAD segments and batches it internally.
+res = model.generate(
+    input="podcast_1hour.wav",
+    batch_size_s=300,   # dynamic batching of VAD segments (throughput)
+)
+print(res[0]["text"])
+

实测:13 分钟音频 4.3 秒转完

+
long clip: 791s = 13.2 min
+transcribed 791s in 4.3s -> RTFx=186
+output chars: 2104   # full transcript, head to tail
+

我们把一段 13.2 分钟(791 秒)的录音丢进单次 generate(),4.3 秒就转写完毕(186x 实时),输出完整覆盖从开头到结尾。换成 1 小时的文件,做法完全一样——不用改一行代码。

+

为什么能处理任意时长

+
    +
  • VAD 自动切句:fsmn-vad 先把长音频按停顿切成一句句语音段,max_single_segment_time 控制单段最长时长。
    • +
  • 动态批处理:batch_size_s=300 把多段语音按总时长打包成批,一次性高吞吐解码。
    • +
  • 内存可控:因为是分段处理,显存占用与文件总长无关,1 小时和 1 分钟占用相近。
    • +
+

进阶

+ +

典型场景

+
    +
  • 播客 / 有声书:整集一次转写、生成文稿。
    • +
  • 会议 / 讲座 / 庭审:长录音批量转文字 + 存档检索。
    • +
  • 客服通话:整通录音转写做质检与挖掘。
    • +
+

FunASR 是通义实验室开源的工业级语音识别工具包,长音频、流式、多语种全覆盖。

在 GitHub 上 Star FunASR ★
+ +

相关文章

+ +
+
FunASR · 通义实验室开源语音识别 · GitHub
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/funasr-v1-3-26-openai-vllm-llama-cpp.html b/web-pages/product-site/legacy/blog/funasr-v1-3-26-openai-vllm-llama-cpp.html new file mode 100644 index 0000000000..5c5942d844 --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-v1-3-26-openai-vllm-llama-cpp.html @@ -0,0 +1,121 @@ + + + + + + FunASR v1.3.26:OpenAI 兼容 API、vLLM 与 llama.cpp 部署 | FunASR + + + + + + + + + + + + + + + + + + +
+

FunASR v1.3.26:OpenAI 兼容 API、vLLM 与 llama.cpp 部署

+

2026-07-23 · FunASR 发布指南

+ FunASR 工业级语音识别视觉图 +

FunASR v1.3.26 把同一套开源语音识别能力整理成三个清晰入口:Python 与 OpenAI 兼容 API、面向 GPU 批量吞吐的 vLLM，以及无需 Python 的 llama.cpp / GGUF 运行时。

+

这不是只有一个 wheel 的发布。Python 包、服务示例、GPU 推理指南和八个平台运行时资产分别面向不同部署场景，并通过同一个 FunASR 生态互相衔接。

+ +

先选最适合你的入口

+ + + + + + + +
目标推荐入口开始位置
接入现有 OpenAI SDK、Agent 或工作流/v1/audio/transcriptionsOpenAI 兼容 API 示例
GPU 批量转写与高吞吐服务Fun-ASR-Nano + vLLMvLLM 中文指南
CPU、边缘设备或零 Python 部署llama.cpp / GGUFv0.1.9 运行时资产
+ +

1. 启动 OpenAI 兼容转写服务

+

下面的路径与仓库 smoke test 使用同一套服务实现。先固定当前 PyPI 版本，再启动服务:

+ 
git clone https://github.com/modelscope/FunASR.git
+cd FunASR/examples/openai_api
+python -m pip install "funasr==1.3.26" fastapi uvicorn python-multipart
+python server.py --model sensevoice --device cuda --port 8000
+

另开一个终端，用公开样例验证健康检查和转写:

+ 
curl -L https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav -o sample.wav
+curl http://localhost:8000/health
+curl http://localhost:8000/v1/audio/transcriptions \
+  -F file=@sample.wav \
+  -F model=sensevoice \
+  -F response_format=verbose_json
+

已有 OpenAI Python 客户端时，只需把 base_url 指向 http://localhost:8000/v1。接口还提供模型列表、Swagger 文档、Postman、OpenAPI、Gradio 和 Kubernetes 示例。

+ +

2. 用 vLLM 放大 Fun-ASR-Nano 吞吐

+

Fun-ASR-Nano 的音频编码器与 adaptor 在 PyTorch 中生成连续音频 embedding，再交给 vLLM 的 Qwen3 解码部分。仓库基准记录 vLLM batch 达到 RTFx 340，并保留与 PyTorch 基线接近的 CER。

+
维护环境已在单张 H100 上完成真实 checkpoint 验证:vLLM 0.19.1 成功加载，样例音频完成转写并输出 token 时间戳。完整安装、显存、批处理、WebSocket 与服务参数都在 vLLM 指南中。
+ 
python -m pip install "vllm==0.19.1"
+python -m pip install "funasr>=1.3.26" safetensors tiktoken websockets
+# 按 docs/vllm_guide_zh.md 选择离线、流式或服务入口
+ +

3. 下载 llama.cpp / GGUF 自包含运行时

+

runtime-llamacpp-v0.1.9 同时提供 Linux x64/ARM64、macOS ARM64 和 Windows x64 包，其中 Linux 支持 AVX2/Vulkan，Windows 支持 AVX2/Vulkan/CUDA，并提供 funasr-llamacpp-windows-x64-vulkan.zip。每个压缩包都带自包含的 llama-funasr-* 二进制入口。

+
    +
  • 不想维护 Python 环境:选标准 CPU 包。
    • +
  • 桌面 GPU:按平台选择 Vulkan 或 CUDA 包。
    • +
  • 边缘设备与 Apple Silicon:选择 ARM64 包。
    • +
+

模型、命令与硬件选择见 llama.cpp / GGUF 专页，发布资产和模型版本保持对应，避免用户在多个 release 中寻找文件。

+ +

四个仓库分别解决什么

+ + + + + + + + +
仓库适合关注的内容
FunASR工具包、服务、OpenAI API、vLLM、llama.cpp 与部署文档
Fun-ASRFun-ASR-Nano / MLT 模型、能力、评测和模型级集成
SenseVoice多语言 ASR、情感、语言和音频事件识别
FunClip由语音识别驱动的视频理解、检索与剪辑
+ +
+

从最适合你的入口跑通一个真实音频。遇到问题可在 GitHub 提交可复现 issue；如果项目对你有用，也欢迎 Star 你实际使用的仓库。

+ 查看 v1.3.26 发布说明 +
+
+ + + diff --git a/web-pages/product-site/legacy/blog/funasr-v1-3-27-language-metadata-vllm-fallback.html b/web-pages/product-site/legacy/blog/funasr-v1-3-27-language-metadata-vllm-fallback.html new file mode 100644 index 0000000000..5f7e228552 --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-v1-3-27-language-metadata-vllm-fallback.html @@ -0,0 +1,124 @@ + + + + + + FunASR v1.3.27:语种检测与可靠的 vLLM fallback | FunASR + + + + + + + + + + + + + + + + + + +
+

FunASR v1.3.27:语种检测与可靠的 vLLM fallback

+

2026-07-24 · FunASR 每周发布

+ FunASR 工业级语音识别视觉图 +

FunASR v1.3.27 聚焦 OpenAI 兼容服务的正确性与可靠性:SenseVoice 返回真实检测语种，Fun-ASR-Nano 在 vLLM 降级后复用已加载的 fallback，服务版本也始终与安装包一致。

+

Python wheel 与源码包已经同步发布；同一个发布页还列出九个平台的 llama.cpp / GGUF 自包含运行时，用户不需要在多个 release 之间寻找下载文件。

+ +

这次修复了什么

+ + + + + + + + +
场景v1.3.27 行为用户收益
SenseVoice 自动语种识别verbose_json.language 返回检测到的 zh/en/yue/ja/ko下游字幕、路由和分析不再收到错误的默认中文
Fun-ASR-Nano vLLM 不可用进程复用已缓存的 AutoModel fallback后续请求不再反复创建同一个模型
vLLM/VAD 与 fallback 同时失败清理半初始化状态，保留后续重试能力暂时性初始化失败不会把服务卡在假加载状态
版本排障OpenAPI 与启动横幅读取包内版本日志和 /docs 都准确显示 1.3.27
+ +

1. 启动 OpenAI 兼容转写服务

+

下面的路径与仓库 smoke test 使用同一套服务实现。先固定当前 PyPI 版本，再启动服务:

+ 
git clone https://github.com/modelscope/FunASR.git
+cd FunASR/examples/openai_api
+python -m pip install "funasr==1.3.27" fastapi uvicorn python-multipart
+python server.py --model sensevoice --device cuda --port 8000
+

另开一个终端，用公开样例验证健康检查和转写:

+ 
curl -L https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav -o sample.wav
+curl http://localhost:8000/health
+curl http://localhost:8000/v1/audio/transcriptions \
+  -F file=@sample.wav \
+  -F model=sensevoice \
+  -F response_format=verbose_json
+

SenseVoice 会保留原始语种标签并写入 OpenAI 兼容响应，例如英文音频返回:

+ 
{"task":"transcribe","language":"en","duration":4.6,"text":"hello", "segments":[...]}
+

已有 OpenAI Python 客户端时，只需把 base_url 指向 http://localhost:8000/v1。接口还提供模型列表、Swagger 文档、Postman、OpenAPI、Gradio 和 Kubernetes 示例。

+ +

2. 用 vLLM 放大 Fun-ASR-Nano 吞吐

+

Fun-ASR-Nano 的音频编码器与 adaptor 在 PyTorch 中生成连续音频 embedding，再交给 vLLM 的 Qwen3 解码部分。仓库基准记录 vLLM batch 达到 RTFx 340，并保留与 PyTorch 基线接近的 CER。

+
维护环境已在单张 H100 上完成真实 checkpoint 验证:vLLM 0.19.1 成功加载，样例音频完成转写并输出 token 时间戳。完整安装、显存、批处理、WebSocket 与服务参数都在 vLLM 指南中。
+ 
python -m pip install "vllm==0.19.1"
+python -m pip install "funasr>=1.3.27" safetensors tiktoken websockets
+# 按 docs/vllm_guide_zh.md 选择离线、流式或服务入口
+ +

3. 下载 llama.cpp / GGUF 自包含运行时

+

runtime-llamacpp-v0.1.9 同时提供九个 Linux x64/ARM64、macOS ARM64 和 Windows x64 包，其中 Linux 支持 AVX2/Vulkan，Windows 支持 AVX2/Vulkan/CUDA，并提供 funasr-llamacpp-windows-x64-vulkan.zip。每个压缩包都带自包含的 llama-funasr-* 二进制入口。

+
    +
  • 不想维护 Python 环境:选标准 CPU 包。
    • +
  • 桌面 GPU:按平台选择 Vulkan 或 CUDA 包。
    • +
  • 边缘设备与 Apple Silicon:选择 ARM64 包。
    • +
+

模型、命令与硬件选择见 llama.cpp / GGUF 专页，发布资产和模型版本保持对应，避免用户在多个 release 中寻找文件。

+ +

四个仓库分别解决什么

+ + + + + + + + +
仓库适合关注的内容
FunASR工具包、服务、OpenAI API、vLLM、llama.cpp 与部署文档
Fun-ASRFun-ASR-Nano / MLT 模型、能力、评测和模型级集成
SenseVoice多语言 ASR、情感、语言和音频事件识别
FunClip由语音识别驱动的视频理解、检索与剪辑
+ +
+

从最适合你的入口跑通一个真实音频。遇到问题可在 GitHub 提交可复现 issue；如果项目对你有用，也欢迎 Star 你实际使用的仓库。

+ 查看 v1.3.27 发布与下载 +
+
+ + + diff --git a/web-pages/product-site/legacy/blog/funasr-v1-3-28-realtime-websocket-subtitles.html b/web-pages/product-site/legacy/blog/funasr-v1-3-28-realtime-websocket-subtitles.html new file mode 100644 index 0000000000..cf4815174f --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-v1-3-28-realtime-websocket-subtitles.html @@ -0,0 +1,113 @@ + + + + + + FunASR v1.3.28：实时识别可靠性与字幕对齐修复 | FunASR + + + + + + + + + + + + + + + + + + +
+

FunASR v1.3.28：实时识别可靠性与字幕对齐修复

+

2026-07-24 · FunASR 每周发布

+ FunASR 工业级语音识别视觉图 +

FunASR v1.3.28 是一次面向实时转写和字幕生成的可靠性热修复。实时 WebSocket 最终解码退化时会保留连续完整的最佳结果，SenseVoice 字幕也会保持文本、标签和时间戳对齐。

+

签名发布页同时提供 Python wheel、源码包和九个平台的 llama.cpp / GGUF 自包含运行时。

+ +

这次修复了什么

+ + + + + + + + +
场景v1.3.28 行为用户收益
VAD 锁句后的终稿退化为短前缀、重复文本或异常服务保留覆盖当前语音段的干净连续 partial实时终稿不再丢失句尾，也不会被重复幻觉替换
短音频尾部随后收到 STOP待处理采样统一进入最终解码路径短句结尾不会被静默丢弃
WebSocket 断线或处理异常显式导入连接异常，其他错误稳定进入通用处理异常处理不再依赖 websockets 的延迟属性和导入顺序
SenseVoice 字幕分句富标签、标点和词/BPE 时间戳保持对齐中文不会被压成一个字幕块，英文原文也不会受损
+ +

1. 升级 Python 包

+

实时识别和字幕部署都建议固定到本次热修复版本：

+ 
python -m pip install -U "funasr==1.3.28"
+funasr --version
+

包版本、命令行版本和服务启动横幅都会显示 1.3.28；已有模型缓存和 API 请求格式无需迁移。

+ +

2. 实时识别与字幕入口

+

Fun-ASR-Nano 实时会话可使用包内入口并参照仓库 vLLM 指南配置。客户端 COMMIT 与服务端 VAD 模式现在共享修复后的最终收尾逻辑。

+ 
funasr-realtime-server --help
+# 模型和设备参数见 docs/vllm_guide_zh.md
+
精确合并源码通过 118 项聚焦回归测试；实时 WebSocket 文件还独立通过 60 项测试，覆盖 STOP 最终解码和不依赖导入顺序的异常处理。
+

SenseVoice 用户通过同一次包升级获得字幕对齐修复，无需迁移输出结构。

+ +

3. 下载 llama.cpp / GGUF 自包含运行时

+

runtime-llamacpp-v0.1.9 同时提供九个 Linux x64/ARM64、macOS ARM64 和 Windows x64 包，其中 Linux 支持 AVX2/Vulkan，Windows 支持 AVX2/Vulkan/CUDA，并提供 funasr-llamacpp-windows-x64-vulkan.zip。每个压缩包都带自包含的 llama-funasr-* 二进制入口。

+
    +
  • 不想维护 Python 环境:选标准 CPU 包。
    • +
  • 桌面 GPU:按平台选择 Vulkan 或 CUDA 包。
    • +
  • 边缘设备与 Apple Silicon:选择 ARM64 包。
    • +
+

模型、命令与硬件选择见 llama.cpp / GGUF 专页，发布资产和模型版本保持对应，避免用户在多个 release 中寻找文件。

+ +

四个仓库分别解决什么

+ + + + + + + + +
仓库适合关注的内容
FunASR工具包、服务、OpenAI API、vLLM、llama.cpp 与部署文档
Fun-ASRFun-ASR-Nano / MLT 模型、能力、评测和模型级集成
SenseVoice多语言 ASR、情感、语言和音频事件识别
FunClip由语音识别驱动的视频理解、检索与剪辑
+ +
+

从最适合你的入口跑通一个真实音频。遇到问题可在 GitHub 提交可复现 issue；如果项目对你有用，也欢迎 Star 你实际使用的仓库。

+ 查看 v1.3.28 发布与下载 +
+
+ + + diff --git a/web-pages/product-site/legacy/blog/funasr-vs-faster-whisper-chinese.html b/web-pages/product-site/legacy/blog/funasr-vs-faster-whisper-chinese.html new file mode 100644 index 0000000000..3509a6a7c8 --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-vs-faster-whisper-chinese.html @@ -0,0 +1,131 @@ + + + + + +FunASR vs faster-whisper:中文与粤语语音识别实测对比 | FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

FunASR vs faster-whisper:中文与粤语语音识别实测对比

+

2026-06-21 · FunASR 团队

+ +

faster-whisper 是目前最流行的 Whisper 加速实现(CTranslate2),英语和通用多语种都很强。但在中文、尤其是粤语和方言上,它有明显短板。下面是同样三段音频上 faster-whisper(small)和 FunASR SenseVoice 的逐句实测对比。

+ +

实测对比(同样音频,逐字结果)

+ + + + + +
音频faster-whisper (small)FunASR SenseVoice
普通话开放时间早上九点至下午五点。开放时间早上9点至下午5点。
粤语语言误判为 zh;
這幾個字都表達不到我想講的意思		正确识别 yue;
呢几个字都表达唔到,我想讲嘅意思
日语うちの中学は弁当性で…うちの中学は弁当制で…
+

三个关键差异:

+
    +
  • 粤语:faster-whisper 把它当普通话处理——语言检测成 zh,并把口语粤语转写成普通话书面语(丢掉 呢 / 唔到 / 嘅 这些粤语特征字)。SenseVoice 原生支持粤语,正确识别 yue 并保留粤语原貌。
    • +
  • 日语同音字错:faster-whisper 把「弁当制」听成「弁当性」(同音误识),SenseVoice 正确。
    • +
  • 普通话简单句两者都对——差异主要在难样本、方言、专名上显现。
    • +
+ +

整体准确率(184 段中文)

+

同机 CPU、字符级 CER(越低越准):FunASR SenseVoice 8.0% / Paraformer 9.9% / Fun-ASR-Nano 8.3%;Whisper 系(small/base/large-v3-turbo)约 22–31%。中文上 FunASR 的 CER 低约 2.7 倍,原因=大规模中文训练数据 + 非自回归架构(更快)。完整方法学见 BENCHMARKS.md。

+ +

FunASR 还多给你什么

+
    +
  • 语种识别:zh / en / yue(粤语)/ ja / ko 自动判别(faster-whisper 把粤语判成 zh)
    • +
  • 情感 + 音频事件:HAPPY/SAD/ANGRY、BGM/掌声/笑声(详见)
    • +
  • 非自回归更快:SenseVoice/Paraformer 一次前向,CPU 上 ~20× 实时
    • +
  • 端侧:还有 llama.cpp/GGUF 运行时,单二进制零 Python 跑
    • +
+ +

该用哪个?

+

诚实地说:faster-whisper 在英语、通用 99 语种、翻译任务上依然很强,生态也成熟。但如果你的场景是中文、粤语、方言,或需要情感/事件/语种信息,FunASR 更准也更全。两者都开源、都能本地跑。

+ +

3 行试 FunASR

+
pip install funasr
+from funasr import AutoModel
+m = AutoModel(model="iic/SenseVoiceSmall")
+print(m.generate(input="audio.wav", language="auto", use_itn=True)[0]["text"])
+ +

FunASR 全家桶开源——ASR / VAD / 标点 / 说话人 / 情感事件 / LLM-ASR / llama.cpp 端侧。觉得有用点个 Star 👇

⭐ Star FunASR

也欢迎 Star:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/funasr-vs-whisper-benchmark.html b/web-pages/product-site/legacy/blog/funasr-vs-whisper-benchmark.html new file mode 100644 index 0000000000..a9249046cd --- /dev/null +++ b/web-pages/product-site/legacy/blog/funasr-vs-whisper-benchmark.html @@ -0,0 +1,95 @@ + + +FunASR vs Whisper 实测对比:中文语音识别谁更快更准 | FunASR 博客 + + + + + + + +
+

FunASR vs Whisper 实测对比:中文语音识别谁更快更准?

+

FunASR 技术博客 · 2026-06-16 · Benchmark

+

我们在 184 个中文长音频文件(共 11,539 秒 ≈ 192 分钟) 上、同一块 NVIDIA H100 上,实测了 FunASR 系列模型与 OpenAI Whisper 的速度(RTFx)和准确率(CER)。结论很直接:在中文场景,FunASR 又快又准。

+ + + + + + + +
模型设备RTFx(越高越快)CER(越低越准)
SenseVoice-SmallGPU169.6x7.81%
Paraformer-LargeGPU119.6x10.18%
Fun-ASR-NanoGPU340x (vLLM)8.20%
Whisper-large-v3-turboGPU46.1x21.71%
Whisper-large-v3GPU13.4x20.02%
SenseVoice-SmallCPU17.2x7.81%
+

速度

+

SenseVoice-Small 达到 169.6x 实时速度,是 Whisper-large-v3(13.4x)的约 12 倍;即使在 CPU 上,SenseVoice 也有 17.2x,比 Whisper 的 GPU 还快。Paraformer-Large 119.6x,同样远超 Whisper。

+

准确率

+

中文 CER:SenseVoice 7.81%、Paraformer 10.18%,而 Whisper-large-v3 是 20.02%、turbo 版 21.71%。FunASR 的中文错字率只有 Whisper 的一半甚至更低。

+

为什么 FunASR 更快

+

SenseVoice / Paraformer 是非自回归模型:一次前向就出全部结果,不像 Whisper 逐 token 自回归解码。再加上针对中文与亚洲语言的训练数据,中文场景兼顾速度与准确率。

+

开始使用 FunASR

开源、可商用、支持 CPU/GPU 部署。觉得有用就点个 Star ⭐

FunASR GitHub ★
+

延伸阅读:SenseVoice 部署指南 · Fun-ASR-Nano 指南 · 对比 Whisper

+

测试条件:184 文件 / 11,539 秒中文音频,NVIDIA H100,RTF=推理时间/音频时长,Speed=1/RTF,CER 为去标点后字错误率。

+ +

相关文章

+ +
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/funclip-v2-1-0-video-clipping-release.html b/web-pages/product-site/legacy/blog/funclip-v2-1-0-video-clipping-release.html new file mode 100644 index 0000000000..7ffabd984e --- /dev/null +++ b/web-pages/product-site/legacy/blog/funclip-v2-1-0-video-clipping-release.html @@ -0,0 +1,118 @@ + + + + + + FunClip v2.1.0：本地 AI 视频剪辑与字幕驱动剪辑 | FunASR + + + + + + + + + + + + + + + + + + +
+

FunClip v2.1.0：本地 AI 视频剪辑的首个版本化发布

+

2026-07-24 · FunClip Release

+ FunClip 本地视频输入、字幕识别和大模型智能剪辑界面 +

FunClip v2.1.0 是项目首个带版本号的 GitHub Release。它把当前本地视频剪辑应用固定为一个可下载、可校验、可回退的版本：先用 FunASR 系列模型识别视频，再按字幕、说话人或大模型选出的片段完成剪辑。

+

你可以直接使用公开 Space，也可以下载源码在自己的机器上运行；视频、音频和模型权重不需要打包进发布归档。

+ +

选择适合剪辑任务的识别路径

+ + + + + + + + +
路径适合场景启动方式
Paraformer需要精确时间戳、按文本或说话人剪辑，以及中文热词python funclip/launch.py
Fun-ASR-Nano普通话、英语、日语、中文方言与口音的高精度转写；精确按字剪辑仍建议用 Paraformerpython funclip/launch.py -m fun-asr-nano
SenseVoice多语种识别，同时保留情感和音频事件标签python funclip/launch.py -m sensevoice
英文 Paraformer需要英文识别与时间戳剪辑python funclip/launch.py -l en
+ +

1. 先在线体验

+

FunClip Hugging Face Space 运行同一套公开应用，适合先验证视频上传、ASR、字幕选择和智能剪辑流程。需要处理私有视频或固定依赖时，再部署本地版本。

+ +

2. 下载并校验 v2.1.0

+

Linux 和 macOS 可以下载 tar.gz；Windows 或通用解压场景可以下载 zip。下面的校验命令只检查当前目录中已经下载的归档：

+ 
curl -LO https://github.com/modelscope/FunClip/releases/download/v2.1.0/FunClip-2.1.0.tar.gz
+curl -LO https://github.com/modelscope/FunClip/releases/download/v2.1.0/SHA256SUMS
+sha256sum -c SHA256SUMS --ignore-missing
+
+tar -xzf FunClip-2.1.0.tar.gz
+cd FunClip-2.1.0
+python -m pip install -r requirements.txt
+python -m pip install -U "funasr>=1.3.29"
+python funclip/launch.py
+

Windows 用户可运行 Get-FileHash -Algorithm SHA256 FunClip-2.1.0.zip，再与 SHA256SUMS 对照。

+ +

3. 从字幕剪辑到内容理解剪辑

+

默认工作流先将视频转成带时间信息的字幕，再让用户按文本或说话人选段。LLM 智能剪辑会把字幕和提示词交给已配置的大模型，返回可直接交给现有剪辑按钮的时间段。

+
    +
  • 字幕足以判断内容时，使用已支持的字幕驱动大模型提供商。
    • +
  • 动作、画面变化或屏幕事件比字幕更重要时，可选 TwelveLabs Pegasus，让模型同时理解视觉和音频。
    • +
  • 需要人工精确控制时，直接选择识别文本或说话人，输出目标视频和对应 SRT 字幕。
    • +
+ +

为什么发布的是源码归档，而不是 wheel

+

FunClip 是可运行应用，而不是已声明为 Python 库的包；仓库没有用于构建 wheel 的 setup.py 或 pyproject.toml。因此 v2.1.0 提供两个诚实的应用源码归档和一份校验文件：

+ +

归档包含 Git 已跟踪的应用源码、文档和依赖清单，不捆绑运行依赖或模型权重；不同模型继续遵循各自的许可证与分发方式。

+ +
v2.1.0 的精确合并源码通过 50 项测试，另有 1 项跳过；干净 Python 3.12 环境中的 6 项发布契约测试覆盖可复现归档、跨文件系统输出、错误 ref 拒绝与发布幂等性。GitHub Actions 首次发布和主动重跑均成功，重跑验证已发布资产一致后不做破坏性修改。
+ +

与 FunASR 1.3.29 的版本关系

+

FunClip v2.1.0 要求 funasr>=1.3.29。该版本在 SenseVoice 缺少 token 时间戳时，通过 sentence_info 返回每个 VAD 语音区域，让字幕和剪辑客户端获得真实分段边界；同时包含 1.3.28 的实时终稿与短尾语音修复。详情见 FunASR v1.3.29 Release。

+ +
+

先在 Space 跑通一个视频，或下载固定版本部署到自己的机器。遇到问题可提交可复现 issue；如果 FunClip 对你的工作流有帮助，欢迎 Star FunClip 仓库。

+ 查看 FunClip v2.1.0 发布与下载 +
+
+ + + diff --git a/web-pages/product-site/legacy/blog/generate-subtitles-srt-vtt-from-audio-video.html b/web-pages/product-site/legacy/blog/generate-subtitles-srt-vtt-from-audio-video.html new file mode 100644 index 0000000000..7811dad2ad --- /dev/null +++ b/web-pages/product-site/legacy/blog/generate-subtitles-srt-vtt-from-audio-video.html @@ -0,0 +1,134 @@ + + +用 FunASR 自动生成字幕:从音频/视频一键导出 SRT 和 VTT | FunASR + + + + + + + +
+

用 FunASR 自动生成字幕:从音频/视频一键导出 SRT 和 VTT

+

FunASR 技术博客 · 2026-06-18 · 教程

+

要给视频或音频配字幕,不一定要上传到云端或买 API。FunASR 可以在本地、免费地把语音转成带时间戳的 SRT 或 VTT 字幕,还能标注说话人。中文尤其强,同时支持 50+ 语言。下面的命令和代码都已实测。

+ +

第一步(视频):先抽出音频

+

如果源文件是视频,用 ffmpeg 抽出 16kHz 单声道音频(FunASR 的标准输入):

+
ffmpeg -i video.mp4 -ar 16000 -ac 1 audio.wav
+

纯音频文件(wav/mp3/m4a 等)可跳过这步。

+ +

最快:一行命令出 SRT

+
pip install -U funasr
+funasr audio.wav -f srt -o ./subs
+

在 ./subs/audio.srt 生成标准 SRT 字幕,时间戳真实可用:

+
1
+00:00:00,000 --> 00:00:05,546
+欢迎大家来体验达摩院推出的语音识别模型
+

加 --spk 可以按说话人分句(适合访谈、会议):

+
funasr meeting.wav --spk -f srt -o ./subs
+ +

用 Python:同时导出 SRT 和 VTT(带说话人)

+

需要更多控制(比如同时要网页用的 VTT、自定义说话人前缀)时,用 Python 直接从识别结果里的 sentence_info 拼字幕:

+
from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", spk_model="cam++", device="cuda")
+res = model.generate(input="audio.wav")
+segments = res[0]["sentence_info"]   # 每段含 start/end(毫秒)、spk、sentence
+
+def ts(ms, sep):                     # 毫秒 -> HH:MM:SS,mmm(SRT 用 "," / VTT 用 ".")
+    h, mm, ss, mmm = ms//3600000, (ms%3600000)//60000, (ms%60000)//1000, ms%1000
+    return "%02d:%02d:%02d%s%03d" % (h, mm, ss, sep, mmm)
+
+with open("subs.srt", "w", encoding="utf-8") as f:
+    for i, s in enumerate(segments):
+        text = rich_transcription_postprocess(s["sentence"])
+        f.write("%d\n%s --> %s\nSpeaker %s: %s\n\n" % (i+1, ts(s["start"], ","), ts(s["end"], ","), s["spk"], text))
+
+with open("subs.vtt", "w", encoding="utf-8") as f:
+    f.write("WEBVTT\n\n")
+    for s in segments:
+        text = rich_transcription_postprocess(s["sentence"])
+        f.write("%s --> %s\n<v Speaker %s>%s\n\n" % (ts(s["start"], "."), ts(s["end"], "."), s["spk"], text))
+

实测输出(SRT):

+
1
+00:00:00,610 --> 00:00:05,530
+Speaker 0: 欢迎大家来体验达摩院推出的语音识别模型
+

注意一定要用 rich_transcription_postprocess 清掉 SenseVoice 的 <|zh|> 等标签,否则标签会混进字幕。

+ +

最后(可选):把字幕烧进视频

+
ffmpeg -i video.mp4 -vf subtitles=subs.srt output.mp4
+ +

为什么用 FunASR 做字幕

+
    +
  • 本地、免费、隐私:无需上传、无 API key、无时长费用。
    • +
  • 快:SenseVoice 非自回归,远快于 Whisper(实测对比),长视频也能很快出字幕。
    • +
  • 中文更准 + 50+ 语言;自带 VAD 分句、说话人标注、真实时间戳。
    • +
+

FunASR 是通义实验室开源的工业级语音识别工具包。

在 GitHub 上 Star FunASR ★
+ +

相关文章

+ +
+
FunASR · 通义实验室开源语音识别 · GitHub
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/index.html b/web-pages/product-site/legacy/blog/index.html new file mode 100644 index 0000000000..99c6d173fd --- /dev/null +++ b/web-pages/product-site/legacy/blog/index.html @@ -0,0 +1,54 @@ + + +FunASR 技术博客 — 语音识别部署/对比/教程 + + + + + + + +
2026-07-24 · 新 Release

FunClip v2.1.0：本地 AI 视频剪辑的首个版本化发布

Paraformer、Fun-ASR-Nano、SenseVoice 与大模型剪辑，提供可校验、可回退的源码下载。

查看发布指南 →
+
2026-07-24

FunASR v1.3.28：实时识别可靠性与字幕对齐修复

连续完整的实时终稿、可靠的 STOP 与断线处理、对齐的 SenseVoice 字幕，以及 11 个直接下载。

+

FunASR 技术博客

语音识别部署、教程与最佳实践

2026-06-23

语音识别带时间戳(字级 timestamps)Python 实战:每个字精确到毫秒

FunASR Paraformer 原生字级时间戳:每个字带 [起始毫秒,结束毫秒],一次调用即有。可做逐字高亮、点击跳转、字幕对齐。附真实实测+配对代码。

2026-06-23

标点恢复 Python 实战:给无标点文本/ASR 结果自动加标点

FunASR ct-punc 开源标点恢复,中英双语,3 行 Python 补全 ，。？(英文还做句首大写),也能一行挂到 ASR 上。附真实实测。

2026-06-22

中文语音识别(普通话)Python 实战:用 FunASR 又快又准

FunASR 专为中文打造:默认旗舰 Fun-ASR-Nano(CER 8.06%),CPU 用 SenseVoice(7.81%)/ Paraformer(10.18%,时间戳/热词),都远好于 Whisper(~20%)。3 行 Python。

2026-06-22

自托管语音转文字:Google / AWS / Azure 云语音 API 的免费开源替代

开源免费(MIT)、本地推理、不按分钟计费、数据不出内网、中文强,OpenAI 兼容改 base_url 即可迁移。附实测代码 + FunASR vs 云 API 对比 + 成本分析。

2026-06-22

Python 语音活动检测(VAD):检测语音、去静音、按停顿切分音频

FunASR fsmn-vad 3 行代码返回每段语音的起止毫秒。实测 13s 录音 0.12s 切成 2 段、去除 18% 静音;可去静音、切分长录音、给 Whisper 等做预处理防幻觉。

2026-06-22

日语语音识别:SenseVoice 一个模型搞定日语转写+标点+情感

同一段日语音频实测:SenseVoice 写对同音字 転売、自动加标点,Whisper-small 误为 天売、无标点。原生 ja 支持 + 自动语种识别 + 情感事件,3 行 Python。

2026-06-23

自托管替代 Deepgram / AssemblyAI

开源免费、自托管、音频不出本地、中文领先,自带 OpenAI 兼容 API——客户端只改 base_url 即可替代按分钟付费的云 STT。

2026-06-23

选哪个 FunASR 模型?Nano vs MLT-Nano vs SenseVoice vs Paraformer

模型选型表 + 场景代码:中英日及中文方言/口音选旗舰 Nano,31 语种选独立 MLT-Nano,CPU 再选 SenseVoice 或 Paraformer。

2026-06-22

轻量语音识别:CPU 上约 250MB 跑中文 ASR

单二进制 + 254MB q8 模型,无需 GPU/Python,CPU 0.16s,中文 CER 7.99%——比 whisper.cpp small 还小且准 3 倍。

2026-06-21

粤语语音识别:SenseVoice 原生支持粤语口语(Whisper 会转成普通话)

同一段粤语音频实测:SenseVoice 保留 呢/唔/嘅,Whisper 转成普通话书面语。原生 yue 支持 + 自动语种识别,3 行 Python。

2026-06-21

FunASR vs faster-whisper:中文与粤语实测对比

粤语被 faster-whisper 误判为普通话、日语同音字错;SenseVoice 原生支持粤语+语种识别,中文 CER 低约 2.7 倍。实测。

2026-06-20

FunASR 跑进 llama.cpp:中文 ASR 的 whisper.cpp 替代品(CPU/零 Python)

单个自包含二进制、内置 VAD、吃任意音频,下载即用转写中文;中文 CPU 上比 whisper.cpp 准约 2.7 倍。3 步实测。

2026-06-18

Python 语音转文字:用 FunASR 本地免费转写音频

几行 Python 把音频转成文本,带时间戳/说话人/批量;本地、免费、无 API key、中文强。

2026-06-18

用 FunASR 自动生成字幕:音频/视频一键出 SRT 和 VTT

一行命令出 SRT,Python 同时导出 VTT,带说话人和真实时间戳;本地、免费、中文强。

2026-06-18

自托管 OpenAI Whisper API 替代:FunASR 起兼容 /v1/audio/transcriptions 服务

funasr-server 暴露 OpenAI 兼容接口,OpenAI SDK 只改 base_url 就能用;本地、免费、隐私、中文更准。

2026-06-18

用 FunASR 命令行转写音频:文本/JSON/SRT 字幕

一行命令出文字/字幕/JSON,--spk 带说话人;还能 funasr-server 起 OpenAI 兼容 API。

2026-06-17

用 FunASR 转写超长音频:1 小时一次搞定

Whisper 限 30 秒,FunASR 内置 VAD 一次吃下任意时长;实测 13 分钟 4.3 秒转完(186x)。

2026-06-17

用 FunASR 实现实时流式语音识别(边说边出字)

600ms 级低延迟流式 ASR:分块+cache 边说边出字,含 2-pass(流式+离线)最佳实践。

2026-06-17

超越转写:用 SenseVoice 识别语言、情感与声学事件

一次非自回归前向同时输出转写+语种+情感+音频事件,Whisper 做不到的四合一。

2026-06-17

用 FunASR 做说话人分离:谁在何时说了什么

一次 generate 调用同时输出转写+说话人标签+时间戳,替代 pyannote+Whisper,无需 HF 授权。

2026-06-16

FunASR vs Whisper 实测对比:谁更快更准

184 中文文件 H100 实测:SenseVoice 169.6x、CER 7.81%,完整速度+准确率数据。

2026-06-16

Fun-ASR-Nano 使用指南:800M 端到端语音识别大模型

主力旗舰,中英日 + 7 大中文方言/26 种口音,热词/流式/说话人分离;31 语种请用 MLT-Nano。

2026-06-16

SenseVoice 部署指南:比 Whisper 快 15 倍

3 行代码跑通多语言识别,含语种/情感/事件检测、VAD、GPU/CPU。

更多:快速上手 · 模型

+ diff --git a/web-pages/product-site/legacy/blog/japanese-speech-recognition.html b/web-pages/product-site/legacy/blog/japanese-speech-recognition.html new file mode 100644 index 0000000000..c6a8b2c985 --- /dev/null +++ b/web-pages/product-site/legacy/blog/japanese-speech-recognition.html @@ -0,0 +1,144 @@ + + + + + +日语语音识别:SenseVoice 一个模型搞定日语转写+标点+情感(附 Whisper 真实对比)| FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

日语语音识别:SenseVoice 一个模型搞定日语转写 + 标点 + 情感(附与 Whisper 的真实对比)

+

2026-06-22 · FunASR 团队

+ +

做日语语音识别,大多数人第一反应是 Whisper。但如果你要的是能在 CPU 上跑、又快又准、还自带标点和情感的开源方案,SenseVoice(FunAudioLLM 团队开源的多语种语音理解模型)是个被低估的选择:它原生支持日语(ja),自动识别语种,一次非自回归前向就输出带标点的文本,顺带还给出情感和音频事件。

+

下面是同一段日语音频上,SenseVoice 与同量级 Whisper-small 的真实输出对比(都在我们的服务器上实跑,未做任何挑选)。

+ +

真实对比:同一段日语音频

+

测试音频是一段口语化的日语发言。两个模型的真实输出:

+ + + + +
模型识别语种输出
SenseVoice自动判 ja供給量が減る と ある程度は仕方ないんじゃね、転売の価格は論外だけど。
Whisper-smallja供給量が減るとある程度は仕方ないんじゃね天売の価格は論外だけど
+

关键差别在 転売 这个词:

+
    +
  • SenseVoice 写对了 転売(てんばい,意为"倒卖/转卖")——这正是这句话的本意(在聊倒卖价格)。
    • +
  • Whisper-small 写成了同音的 天売——读音一样(tenbai),但不是一个真实存在的词,属于典型的日语同音异字识别错误。
    • +
  • 另外,SenseVoice 自动加了标点(、 。),Whisper-small 的输出没有任何标点。
    • +
+

说明一下口径:这里对比的是同量级的 Whisper-small(适合 CPU 部署的常见选择)。更大的 Whisper-large 通常能纠正这类同音字,但模型大得多、也慢得多;而 SenseVoice 在小模型体量下就把这个词写对了,还顺手给了标点和情感。

+ +

3 行代码跑通日语识别

+
pip install funasr
+
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)
+res = model.generate(input="japanese.wav", language="auto", use_itn=True)
+
+print(rich_transcription_postprocess(res[0]["text"]))
+# 供給量が減る と ある程度は仕方ないんじゃね、転売の価格は論外だけど。
+

language 可以写 "ja" 明确指定,也可以像上面写 "auto" 让模型自动判语种——实测这段音频在 auto 下被正确判为 ja,结果与显式指定完全一致。

+ +

原始输出里还带了什么

+

SenseVoice 的原始输出每段开头有一串标签:

+
<|ja|><|NEUTRAL|><|Speech|><|withitn|>供給量が減る と ある程度は仕方ないんじゃね、転売の価格は論外だけど。
+

含义:<|ja|> = 语种是日语;<|NEUTRAL|> = 情感(中性);<|Speech|> = 音频事件(纯人声,可识别 BGM/笑声/掌声等);<|withitn|> = 已做逆文本归一化。也就是说,转写日语的同时,语种 / 情感 / 音频事件全都免费拿到了。用 rich_transcription_postprocess() 一行就能清洗成纯文本。

+ +

为什么日语识别值得试试 SenseVoice

+ + + + + + + + + +
SenseVoiceWhisper-small
日语同音字(転売)✅ 写对❌ 误为 天売
自动标点✅ 内置❌ 无
情感 / 音频事件✅ 一次出❌
逆文本归一化(ITN)✅ 内置部分
速度非自回归,实测 RTF≈0.04(GPU)自回归,更慢
多语种一个模型覆盖 50+ 语种(ja/ko/zh/yue/en…)多语种
许可证开源,可商用开源
+

如果你的场景是日语 + 中文 + 英文混合(跨境电商客服、字幕、会议),SenseVoice 一个模型就能全覆盖,还自带情感分析。想了解它的情感/事件检测能力,看 SenseVoice 情感与音频事件识别;不确定选哪个模型,看 FunASR 模型选型指南;中文准确率对比见 FunASR vs Whisper benchmark。

+ +

FunASR 全家桶都是开源的——工业级 ASR / VAD / 标点 / 说话人 / 情感事件 / LLM-ASR,日语等 50+ 语种开箱即用。觉得有用就点个 Star 支持一下 👇

⭐ Star SenseVoice

也欢迎 Star:FunASR · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/lightweight-speech-recognition-cpu.html b/web-pages/product-site/legacy/blog/lightweight-speech-recognition-cpu.html new file mode 100644 index 0000000000..915468d9ea --- /dev/null +++ b/web-pages/product-site/legacy/blog/lightweight-speech-recognition-cpu.html @@ -0,0 +1,116 @@ + + +轻量语音识别:CPU 上约 250MB 跑中文 ASR(无需 GPU/Python)| FunASR 博客 + + + + + + + +
+

轻量语音识别:在 CPU 上用约 250MB 跑中文 ASR(无需 GPU、无需 Python)

+

2026-06-22 · FunASR 团队

+

大多数语音识别要 GPU、要 Python 环境、模型动辄上 GB。如果你只想在一台普通笔记本、小服务器或边缘设备上把中文转成文字,这些都太重了。

+

FunASR 的 llama.cpp / GGUF 运行时把这件事压到极简:**一个自包含二进制 + 一个量化模型文件**,纯 CPU、零 Python。SenseVoice 的 q8 量化模型只有 **254MB**,精度几乎不变。

+ +

实测:254MB 模型,CPU 上 0.16 秒(真实输出)

+

下载预编译二进制(Linux/macOS/Windows)+ q8 模型,直接跑:

+
# 下二进制见 Releases;再拉模型
+bash download-funasr-model.sh sensevoice ./gguf
+llama-funasr-sensevoice -m ./gguf/sensevoice-small-q8.gguf --vad ./gguf/fsmn-vad.gguf -a audio.wav
+# → 欢迎大家来体验达摩院推出的语音识别模型。   (CPU, 0.16s)
+

模型 243–254MB、VAD 仅 1.7MB,内置在二进制里,无需 Python detok。

+ +

又小又准:对比 whisper.cpp

+

同样跑在 CPU 上(中文 184 集 micro-CER,越低越准):

+ + + + + + +
模型体积中文 CER ↓
FunASR SenseVoice q8254 MB7.99%
FunASR Paraformer q8237 MB9.78%
whisper.cpp small466 MB22.12%
whisper.cpp large-v3-turbo1.6 GB23.15%
+

FunASR 的 q8 模型**比 whisper.cpp small 还小**,中文准确率却高约 3 倍。

+ +

更小的档:量化矩阵

+ + + + + +
模型档体积CER
SenseVoice encoderq8254 MB7.99%
Paraformer encoderq8237 MB9.78%
Fun-ASR-Nano LLM(另需 encoder 470MB)q4_K_M484 MB8.35%
+

提供 Linux x64/arm64、macOS arm64、Windows x64 预编译二进制——arm64 适合树莓派/边缘盒子等。

+ +

从哪拿

+ +

FunASR 全家桶开源、可商用。觉得有用就点个 Star 👇

⭐ Star FunASR

也欢迎:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/punctuation-restoration-python.html b/web-pages/product-site/legacy/blog/punctuation-restoration-python.html new file mode 100644 index 0000000000..f87e3bd7e5 --- /dev/null +++ b/web-pages/product-site/legacy/blog/punctuation-restoration-python.html @@ -0,0 +1,136 @@ + + + + + +标点恢复(Punctuation Restoration)Python 实战:给无标点文本/ASR 结果自动加标点 | FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

标点恢复(Punctuation Restoration)Python 实战:给无标点文本/ASR 结果自动加标点

+

2026-06-23 · FunASR 团队

+ +

很多语音识别(ASR)模型输出的是没有标点的纯文本——一长串字读起来很费劲。标点恢复就是把"。""，""？"这些标点自动补回去,让转写结果可读。FunASR 的 ct-punc 是一个开源的标点恢复模型,中英文都支持,3 行 Python 就能给任意文本加标点。下面全是真实实测输出。

+ +

3 行代码做标点恢复(实测输出)

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="ct-punc", disable_update=True)
+print(model.generate(input="我们都是木头人不许说话不许动")[0]["text"])
+# 我们都是木头人，不许说话，不许动。
+

输入是没有标点的连续文本,输出自动补上了逗号和句号。

+ +

中英文真实示例

+ + + + + +
输入(无标点)输出(ct-punc 加标点)
我们都是木头人不许说话不许动我们都是木头人，不许说话，不许动。
今天天气怎么样我想出去走走你要一起吗今天天气怎么样，我想出去走走，你要一起吗？
the meeting is at 3 pm please bring your laptop and the reportThe meeting is at 3 pm, please bring your laptop and the report.
+

可以看到:中文补「，。？」(问句还会自动判断用问号);英文补逗号、句号,还会把句首字母大写。同一个模型双语通吃。

+ +

最常见用法:给 ASR 结果加标点

+

标点恢复最大的用处是清洗语音识别的输出。FunASR 的 ASR 模型可以一行挂载 ct-punc,识别完直接带标点:

+
from funasr import AutoModel
+
+# ASR + VAD + 标点,一次完成(以中文 Paraformer 为例)
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc")
+result = model.generate(input="audio.wav")
+print(result[0]["text"])   # 输出已带标点
+

当然你也可以像最上面那样单独用 ct-punc 处理任意文本——不限于语音,任何无标点文本(老字幕、OCR 结果、聊天记录)都能加标点。

+ +

为什么用 ct-punc

+ + + + + + +
ct-punc(FunASR)
语言中文 + 英文(单模型双语)
用法3 行 Python,输入文本即可;也能一行挂到 ASR 上
标点，。？等;英文还做句首大写
许可证开源,可商用
+

想要完整的语音流程,看 中文语音识别实战、VAD/去静音 和 说话人分离;不确定选哪个 ASR 模型看 模型选型指南(默认推荐旗舰 Fun-ASR-Nano)。

+ +

FunASR 全家桶开源(MIT)——标点恢复、ASR、VAD、说话人、情感、LLM-ASR(旗舰 Fun-ASR-Nano),拿来即用。觉得有用就点个 Star 👇

⭐ Star FunASR

也欢迎 Star:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/self-hosted-deepgram-assemblyai-alternative.html b/web-pages/product-site/legacy/blog/self-hosted-deepgram-assemblyai-alternative.html new file mode 100644 index 0000000000..bdd5ddc964 --- /dev/null +++ b/web-pages/product-site/legacy/blog/self-hosted-deepgram-assemblyai-alternative.html @@ -0,0 +1,106 @@ + + +自托管替代 Deepgram / AssemblyAI:开源免费的语音识别 API | FunASR 博客 + + + + + + +
+

自托管替代 Deepgram / AssemblyAI:开源、免费、数据不出本地

+

2026-06-23 · FunASR 团队

+

Deepgram、AssemblyAI、Google/Azure Speech 这类云端语音识别 API 按分钟计费,音频还得上传到第三方。如果你想自己托管、零按量费用、音频不出本地——尤其中文场景——FunASR 是一个直接的开源替代:它自带 OpenAI 兼容的转写 API,客户端只改 base_url 即可。

+ +

对比

+ + + + + + + + +
FunASR(自托管)Deepgram / AssemblyAI
价格免费(MIT),无按分钟计费按分钟付费
部署自托管(本地/私有云/边缘)云 API(托管)
数据隐私音频不出本地上传到供应商
中文/粤语业界领先以英文为主
接口OpenAI 兼容(drop-in)各自专有 SDK
离线/CPU✅(含 llama.cpp 单二进制)❌ 仅云
+

云 API 的优势是免运维、弹性扩缩、英文与增值功能成熟;FunASR 的优势是免费、自托管、隐私、中文。按需求取舍。

+ +

起一个 OpenAI 兼容的本地 STT 服务

+
pip install funasr
+funasr-server --model sensevoice --device cuda      # 或 --device cpu
+# → POST http://localhost:8000/v1/audio/transcriptions
+

然后任何 OpenAI 客户端只改 base_url 即可——无需改业务代码:

+
from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
+text = client.audio.transcriptions.create(model="sensevoice", file=open("audio.wav","rb")).text
+

从 Deepgram/AssemblyAI 或 OpenAI Whisper API 迁移:把请求指向你自己的 funasr-server 即可,按量账单归零、数据留在本地。

+ +

选模型

+

多语种 + 情感选 SenseVoice;中文 + 时间戳/热词选 Paraformer;中英日及中文方言/口音的旗舰识别选 Fun-ASR-Nano;需要 31 语种选独立的 Fun-ASR-MLT-Nano。详见 选型指南。

+

FunASR 全家桶开源、可商用、可自托管。觉得有用点个 Star 👇

⭐ Star FunASR

也欢迎:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + diff --git a/web-pages/product-site/legacy/blog/self-hosted-google-aws-azure-speech-to-text.html b/web-pages/product-site/legacy/blog/self-hosted-google-aws-azure-speech-to-text.html new file mode 100644 index 0000000000..d52228be9a --- /dev/null +++ b/web-pages/product-site/legacy/blog/self-hosted-google-aws-azure-speech-to-text.html @@ -0,0 +1,147 @@ + + + + + +自托管语音转文字:Google / AWS / Azure 云语音 API 的免费开源替代(FunASR)| FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

自托管语音转文字:Google / AWS / Azure 云语音 API 的免费开源替代

+

2026-06-22 · FunASR 团队

+ +

Google Cloud Speech-to-Text、AWS Transcribe、Azure Speech 这些云语音 API 很好用——全托管、自动扩容、免运维。但当用量上去、或对数据合规有要求时,它们的短板也很明显:按分钟计费(规模化后很贵)、音频要上传到厂商云(隐私/合规/数据驻留问题)、有速率限制、离不开网络、难以定制。

+

FunASR(通义实验室开源)是一个成熟的自托管替代:开源免费(MIT,可商用)、跑在你自己的机器上(可完全离线)、不按分钟计费、数据不出内网,中文与亚洲语种尤其强,并提供 OpenAI 兼容接口,迁移成本极低。下面是真实可跑的上手代码。

+ +

4 行代码,本地转写(实测输出)

+
pip install funasr
+
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)  # 没 GPU 自动用 CPU
+res = model.generate(input="audio.wav", language="auto", use_itn=True)
+
+print(rich_transcription_postprocess(res[0]["text"]))
+# 欢迎大家来体验达摩院推出的语音识别模型。
+

模型首次运行自动下载,之后全程在本地推理——没有 API key、没有按分钟账单、音频不上传任何云。

+ +

已经在用云 API?换 base_url 即可迁移

+

FunASR 自带 OpenAI 兼容的转写服务,如果你的应用已经在调云厂商 SDK,通常只需把 base_url 指向自己的服务:

+
# 起一个本地服务(OpenAI 兼容 /v1/audio/transcriptions)
+funasr-server --model sensevoice --device cuda
+
+# 客户端:OpenAI SDK 直接连本地,改一行 base_url
+from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
+text = client.audio.transcriptions.create(model="sensevoice", file=open("audio.wav","rb")).text
+ +

FunASR vs 云语音 API

+ + + + + + + + + + + +
FunASR(自托管)云 STT(Google/AWS/Azure)
计费开源免费,只有固定机器成本按分钟计费,随用量线性增长
数据留在你自己服务器(可离线)音频上传到厂商云
部署自己跑(pip / Docker)全托管,零运维
语种50+,中文/亚洲语种很强多语种(各厂不同)
说话人分离✅ 内置(cam++)✅(额外计费/配置)
情感 / 音频事件✅(SenseVoice)多数 ❌
定制 / 微调✅ 完整模型权限有限
离线 / 内网隔离✅❌
许可证开源,可商用闭源
+

需要说句公道话:如果你要的是零运维、按需弹性、不想管机器,托管云 API 仍然是省心之选。FunASR 的取舍是——你用一台自己的机器,换来可控成本、数据私有、可离线、可定制。

+ +

什么时候自托管更划算

+

云 STT 一般按音频分钟计费(各厂/各档大致 每音频小时 $0.6 ~ $1.5)。也就是说处理 1000 小时/月大约 $600 ~ $1500/月,且随用量线性上涨;而一台自托管 FunASR 实例是固定的机器成本,与转写量基本无关。当你满足以下任一条,自托管通常更优:

+
    +
  • 用量大:每月音频时长高,按分钟计费的账单远超一台机器的成本;
    • +
  • 数据敏感:医疗/金融/政企,音频不能离开内网;
    • +
  • 需离线 / 内网隔离的部署环境;
    • +
  • 中文/方言/亚洲语种为主,想要更强的本土效果与可定制性。
    • +
+ +

想进一步看:与 OpenAI Whisper API 的对比见 自托管 Whisper API 替代;与 Deepgram/AssemblyAI 的对比见 Deepgram/AssemblyAI 替代;中文准确率见 FunASR vs Whisper benchmark;不确定选哪个模型看 模型选型指南。

+ +

FunASR 全家桶都是开源的(MIT)——工业级 ASR / VAD / 标点 / 说话人 / 情感事件 / LLM-ASR,自托管、可商用。觉得有用就点个 Star 支持一下 👇

⭐ Star FunASR

也欢迎 Star:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/self-hosted-openai-whisper-api-alternative.html b/web-pages/product-site/legacy/blog/self-hosted-openai-whisper-api-alternative.html new file mode 100644 index 0000000000..e639a72a0b --- /dev/null +++ b/web-pages/product-site/legacy/blog/self-hosted-openai-whisper-api-alternative.html @@ -0,0 +1,151 @@ + + +自托管 OpenAI Whisper API 替代方案:FunASR 一行起兼容 /v1/audio/transcriptions 服务 | FunASR + + + + + + + +
+

自托管 OpenAI Whisper API 替代方案:FunASR 一行起兼容 /v1/audio/transcriptions 服务

+

FunASR 技术博客 · 2026-06-18 · 教程

+

OpenAI 的 /v1/audio/transcriptions(Whisper API)按分钟计费、要把音频上传到云端、还有速率限制。如果你只是想把音频转成文字,完全可以在自己机器上起一个接口完全一致的服务——已有代码只改一个 base_url 就能切过来。FunASR 自带的 funasr-server 就是干这个的。下面每段代码都实测过。

+ +

一行启动 OpenAI 兼容服务

+
pip install -U funasr
+funasr-server --model sensevoice --device cuda     # 监听 localhost:8000
+

启动后就有了和 OpenAI 一致的两个端点:POST /v1/audio/transcriptions 和 GET /v1/models。

+ +

用 OpenAI 官方 SDK 直接调(只改 base_url)

+

你原来调 OpenAI 的代码,几乎一字不改,只把 base_url 指到本地服务、api_key 随便填:

+
from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
+
+with open("audio.wav", "rb") as f:
+    r = client.audio.transcriptions.create(model="sensevoice", file=f)
+print(r.text)
+# -> 欢迎大家来体验达摩院推出的语音识别模型
+

列出可用模型也和 OpenAI 一样:

+
print([m.id for m in client.models.list().data])
+# -> ['fun-asr-nano', 'sensevoice', 'paraformer']
+ +

或者用 curl / 任意 HTTP 客户端

+
curl http://localhost:8000/v1/audio/transcriptions \
+  -F "file=@audio.wav" \
+  -F "model=sensevoice"
+# -> {"text": "欢迎大家来体验达摩院推出的语音识别模型"}
+

因为接口是 OpenAI 兼容的,Node 的 openai 包、LangChain、各种现成 SDK 都能直接连。

+ +

对比:OpenAI Whisper API vs FunASR 自托管

+ + + + + + + +
OpenAI Whisper APIFunASR 自托管
费用$0.006 / 分钟免费(自己的机器)
数据隐私音频上传到云端不出本地
速率限制有无
中文准确率一般更高(实测对比)
速度—SenseVoice 非自回归,远快于 Whisper
接口/v1/audio/transcriptions完全一致
+ +

选哪个模型

+
    +
  • sensevoice —— 默认,非自回归,极快,50+ 语言,带情感/事件;日常首选。
    • +
  • fun-asr-nano —— LLM 解码,面向中英日及中文方言/口音的旗舰模型(需要 vLLM)。
    • +
  • 需要 31 语种 —— 部署独立的 FunAudioLLM/Fun-ASR-MLT-Nano-2512 checkpoint。
    • +
  • paraformer —— 经典中文生产级,稳。
    • +
+

请求里把 model= 换成对应名字即可,无需重启服务。

+ +

从 OpenAI 迁移要改什么

+
    +
  • base_url:指到你的 funasr-server。
    • +
  • api_key:本地服务不校验,随便填一个非空值。
    • +
  • 其余不变:client.audio.transcriptions.create(...) 的调用方式一致。
    • +
+ +

在 Open-WebUI 里把语音输入接到 FunASR

+

Open-WebUI(15 万星的自托管 LLM 界面)的语音转文字(STT)支持任意 OpenAI 兼容端点。既然 funasr-server 本身就是 OpenAI 兼容的,直接指过去即可——无需插件、无需改 Open-WebUI 代码,中文识别比内置 Whisper 更准,而且本地、免费、隐私。

+

① 起 FunASR 服务(同上):

+
funasr-server --model sensevoice --device cuda   # 监听 localhost:8000
+

② Open-WebUI 里:管理面板 → 设置 → 音频 → 语音转文本,引擎选 OpenAI,填:

+
    +
  • API Base URL:http://localhost:8000/v1(Open-WebUI 在 Docker、FunASR 在宿主机时用 http://host.docker.internal:8000/v1)
    • +
  • API Key:任意非空(funasr-server 不校验)
    • +
  • STT Model:sensevoice(或 paraformer / fun-asr-nano)
    • +
+

用 Docker Compose 部署等价于这几个环境变量:

+
AUDIO_STT_ENGINE=openai
+AUDIO_STT_OPENAI_API_BASE_URL=http://host.docker.internal:8000/v1
+AUDIO_STT_OPENAI_API_KEY=funasr
+AUDIO_STT_MODEL=sensevoice
+

之后 Open-WebUI 的麦克风语音输入就走 FunASR 了——中文转写更准、数据不出本地。同样的配置适用于任何"支持自定义 OpenAI STT 端点"的工具(LibreChat、AnythingLLM 等)。

+

FunASR 是通义实验室开源的工业级语音识别工具包。

在 GitHub 上 Star FunASR ★
+ +

相关文章

+ +
+
FunASR · 通义实验室开源语音识别 · GitHub
+ diff --git a/web-pages/product-site/legacy/blog/sensevoice-deployment-guide.html b/web-pages/product-site/legacy/blog/sensevoice-deployment-guide.html new file mode 100644 index 0000000000..0bfb1303a1 --- /dev/null +++ b/web-pages/product-site/legacy/blog/sensevoice-deployment-guide.html @@ -0,0 +1,159 @@ + + + + + +SenseVoice 部署指南：比 Whisper 快 15 倍的多语言语音识别 | FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

SenseVoice 部署指南：比 Whisper 快 15 倍的多语言语音识别

+

FunASR 技术博客 · 2026-06-16 · 部署教程

+

SenseVoice 是 FunAudioLLM 团队开源的多语言语音理解大模型。它不仅做语音识别（ASR），还能在一次前向推理中同时输出语种识别、情感识别、音频事件检测。模型采用非自回归结构，推理速度比 Whisper-Large 快约 15 倍，单条 10 秒音频的识别延迟可低至几十毫秒，非常适合工业级实时与批量场景。

+

本文用可直接复制运行的代码，带你 3 步把 SenseVoice 跑起来。

+ +

1. 安装

+

SenseVoice 通过 funasr 包提供，一行安装：

+
pip install -U funasr torch torchaudio
+ +

2. 三行代码跑通识别

+

下面的代码会自动下载 SenseVoiceSmall 模型并完成识别：

+
from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", device="cuda:0")  # CPU 用 device="cpu"
+res = model.generate(input="audio.wav", language="auto", use_itn=True)
+print(rich_transcription_postprocess(res[0]["text"]))
+

模型 ID：ModelScope 为 iic/SenseVoiceSmall，HuggingFace 为 FunAudioLLM/SenseVoiceSmall。use_itn=True 表示输出带标点和逆文本归一化（数字、日期规整）。

+ +

3. 读懂输出：语种 + 情感 + 音频事件

+

SenseVoice 的原始输出带有富文本标签，例如：

+
<|zh|><|NEUTRAL|><|Speech|><|withitn|>欢迎大家来体验达摩院推出的语音识别模型。
+
    +
  • <|zh|>：语种（zh 中文、en 英文、yue 粤语、ja 日语、ko 韩语）
    • +
  • <|NEUTRAL|>：情感（HAPPY / SAD / ANGRY / NEUTRAL 等）
    • +
  • <|Speech|>：音频事件（Speech / BGM / Applause / Laughter 等）
    • +
+

用 rich_transcription_postprocess() 可一键得到干净文本；如果你想保留这些标签做下游分析，直接读取 res[0]["text"] 即可。

+ +

4. 长音频：加 VAD 自动分段

+

处理几分钟以上的长音频时，搭配 FSMN-VAD 做语音端点检测、自动分段，效果与速度都更好：

+
model = AutoModel(
+    model="iic/SenseVoiceSmall",
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0",
+)
+res = model.generate(input="long_audio.wav", language="auto", use_itn=True, batch_size_s=60)
+ +

5. GPU 与 CPU 怎么选

+ + + + + + + +
场景建议
高并发 / 批量转写GPU（device="cuda:0"），吞吐最高
轻量 / 边缘 / 离线CPU（device="cpu"）；SenseVoice 非自回归，CPU 上也很快
生产服务FunASR runtime（C++ / Docker 一键部署，多并发）
+ +

6. 性能一览

+ + + + + + + + + +
维度SenseVoice-Small
相对 Whisper-Large 速度约 15×
支持语言中文 / 粤语 / 英文 / 日文 / 韩文 等
附加能力语种识别 · 情感识别 · 音频事件检测
架构非自回归（低延迟）
许可证开源（可商用）
+ +
+

开始使用 SenseVoice

+

如果这篇指南对你有帮助，欢迎到 GitHub 点个 Star ⭐ 支持开源。

+SenseVoice GitHub ★ +
+ +

延伸阅读：FunASR vs Whisper 对比 · FunASR 快速上手 · 全部模型

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/sensevoice-emotion-language-detection.html b/web-pages/product-site/legacy/blog/sensevoice-emotion-language-detection.html new file mode 100644 index 0000000000..8371733eca --- /dev/null +++ b/web-pages/product-site/legacy/blog/sensevoice-emotion-language-detection.html @@ -0,0 +1,158 @@ + + + + + +Python 语音情感识别:一个模型搞定语种 + 情感 + 音频事件(SenseVoice)| FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

Python 语音情感识别:一个模型同时输出语种、情感与音频事件(SenseVoice)

+

2026-06-19 · FunASR 团队

+ +

大多数语音识别(ASR)模型只给你文字。但真实场景里,你往往还想知道:说话人是什么情绪?这段是哪种语言?背景是纯人声还是带音乐?用 Whisper 你得叠一堆东西——语种检测 + 单独的情感模型 + 音频事件分类器,既慢又难维护。

+

SenseVoice 是 FunAudioLLM 团队开源的多语种语音理解模型。它在一次非自回归前向里就同时返回:转写文本、语种识别、情感识别、音频事件检测,外加逆文本归一化(ITN)。架构非自回归,比 Whisper-Large 快约 15 倍,适合实时也适合大批量。

+ +

一次推理能拿到什么

+ + + + + + + +
能力说明
语音识别(ASR)50+ 语种,中文准确率领先
语种识别(LID)自动判别 zh / en / ja / ko / yue …
情感识别HAPPY 😊 / SAD 😔 / ANGRY 😡 / NEUTRAL / FEARFUL / DISGUSTED / SURPRISED
音频事件Speech / BGM 🎵 / Applause 👏 / Laughter / Cry
逆文本归一化(ITN)“九点”→“9点”、“fifty”→“50”
+ +

3 行代码跑通

+
pip install funasr
+
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)
+res = model.generate(input="audio.wav", cache={}, language="auto", use_itn=True)
+
+print(res[0]["text"])                                    # 原始带标签输出
+print(rich_transcription_postprocess(res[0]["text"]))    # 清洗后的纯文本
+ +

真实输出:5 种语言自动识别

+

用 SenseVoice 自带的多语种示例音频实测(zh/en/ja/ko/yue),原始输出每段开头都带标签:

+
<|zh|><|NEUTRAL|><|Speech|><|withitn|>开放时间早上9点至下午5点。
+<|en|><|NEUTRAL|><|Speech|><|withitn|>The tribal chieftain called for the boy and presented him with 50 pieces of gold.
+<|ja|><|NEUTRAL|><|Speech|><|withitn|>うちの中学は弁当制で持っていけない場合は、50 円の学校販売のパンを買う。
+<|ko|><|NEUTRAL|><|Speech|><|withitn|>조금만 생각을 하면서 살면 훨씬 편할 거야.
+<|yue|><|NEUTRAL|><|Speech|><|withitn|>呢几个字都表达唔到，我想讲嘅意思。
+

注意每段开头的标签:<|zh|> 语种(连粤语 yue 都能分出来)、<|NEUTRAL|> 情感、<|Speech|> 音频事件、<|withitn|> 表示已做 ITN(“9点至下午5点”“50 pieces”)。

+ +

情感与音频事件:真实音频实测

+

把 SenseVoice 跑在 60 段真实网络音频上:其中 56 段被正确标为 BGM(带背景音乐)——这正是 Whisper 会硬转写、容易幻觉的场景;同时检测出 HAPPY / ANGRY / NEUTRAL 等情感。例如一段被标为 ANGRY 的语音:

+
<|zh|><|ANGRY|><|Speech|><|withitn|>哎,不要看不起那些理想主义者,你脚下的每一步都是他们走出来的。
+

完整标签集——情感:HAPPY / SAD / ANGRY / NEUTRAL / FEARFUL / DISGUSTED / SURPRISED;事件:Speech / BGM / Applause / Laughter / Cry。

+ +

如何解析这些标签

+
import re
+
+raw = res[0]["text"]
+tags = re.findall(r"<\|([^|]+)\|>", raw)
+language = tags[0] if tags else None                       # 'zh'
+emotion  = next((t for t in tags if t in
+            {"HAPPY","SAD","ANGRY","NEUTRAL","FEARFUL","DISGUSTED","SURPRISED"}), None)
+event    = next((t for t in tags if t in
+            {"Speech","BGM","Applause","Laughter","Cry"}), None)
+text     = re.sub(r"<\|[^|]+\|>", "", raw)         # 纯文本
+print(language, emotion, event, text)
+ +

对比 Whisper

+ + + + + + + +
SenseVoiceWhisper
转写 + 语种✅ 一次搞定✅
情感识别✅ 内置❌ 需另接模型
音频事件(BGM/掌声/笑声)✅ 内置❌
逆文本归一化✅ 内置部分
速度非自回归,约快 15×自回归基准
+

要的不只是文字,而是对音频的理解时,SenseVoice 一个模型就够了——无需再拼语种检测 + 情感模型 + 事件分类器。

+ +

FunASR 全家桶都是开源的——工业级 ASR / VAD / 标点 / 说话人 / 情感事件 / LLM-ASR。觉得有用就点个 Star 支持一下 👇

⭐ Star FunASR

也欢迎 Star:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/speech-to-text-python-transcribe-audio.html b/web-pages/product-site/legacy/blog/speech-to-text-python-transcribe-audio.html new file mode 100644 index 0000000000..3830c160ff --- /dev/null +++ b/web-pages/product-site/legacy/blog/speech-to-text-python-transcribe-audio.html @@ -0,0 +1,134 @@ + + +Python 语音转文字:用 FunASR 本地、免费地把音频转成文本 | FunASR + + + + + + + +
+

Python 语音转文字:用 FunASR 本地、免费地把音频转成文本

+

FunASR 技术博客 · 2026-06-18 · 教程

+

在 Python 里把音频转成文字,不一定要用云 API 或庞大的 Whisper。FunASR 几行代码就能在本地、免费地转写,还自带时间戳、说话人分离和批量处理。中文尤其强,同时支持 50+ 语言。下面每段代码都已实测。

+ +

安装

+
pip install -U torch torchaudio
+pip install -U funasr
+ +

最简单:几行转写一个文件

+
from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", device="cuda")
+result = model.generate(input="audio.wav")
+print(rich_transcription_postprocess(result[0]["text"]))
+# -> 欢迎大家来体验达摩院推出的语音识别模型
+

默认用 SenseVoice(非自回归、极快)。rich_transcription_postprocess 用来清掉 SenseVoice 的 <|zh|> 等标签,得到干净文本——别漏掉这一步。

+ +

直接从 URL 转写

+
result = model.generate(
+    input="https://example.com/audio.wav"   # 也支持本地路径、numpy、bytes
+)
+ +

要时间戳和说话人

+

结果里的 sentence_info 每段都带 start/end(毫秒)、spk(说话人)、sentence:

+
model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", spk_model="cam++", device="cuda")
+result = model.generate(input="audio.wav")
+
+for seg in result[0]["sentence_info"]:
+    start = seg["start"] / 1000
+    text = rich_transcription_postprocess(seg["sentence"])
+    print("[%.1fs] Speaker %s: %s" % (start, seg["spk"], text))
+# -> [0.6s] Speaker 0: 欢迎大家来体验达摩院推出的语音识别模型
+ +

批量转写多个文件

+

把文件列表传给 input,一次返回每个文件的结果:

+
results = model.generate(input=["a.wav", "b.wav", "c.wav"])
+for r in results:
+    print(rich_transcription_postprocess(r["text"]))
+ +

选哪个模型

+ + + + + +
model=适合
iic/SenseVoiceSmall默认,极快,50+ 语言,带情感/事件
paraformer-zh经典中文生产级,稳
FunAudioLLM/Fun-ASR-Nano-2512旗舰 LLM 解码,中英日 + 中文方言/口音
FunAudioLLM/Fun-ASR-MLT-Nano-2512独立多语 checkpoint,覆盖 31 语种
+

换模型只改 model= 即可,其余代码不变。

+ +

为什么用 FunASR

+
    +
  • 本地、免费、隐私:无需联网、无 API key、无时长费用。
    • +
  • 快:SenseVoice 非自回归,远快于 Whisper(实测对比),CPU 也能实时。
    • +
  • 中文更准 + 50+ 语言;时间戳、说话人、热词、批量开箱即用。
    • +
+

FunASR 是通义实验室开源的工业级语音识别工具包。

在 GitHub 上 Star FunASR ★
+ +

相关文章

+ +
+
FunASR · 通义实验室开源语音识别 · GitHub
+ diff --git a/web-pages/product-site/legacy/blog/speech-to-text-timestamps-python.html b/web-pages/product-site/legacy/blog/speech-to-text-timestamps-python.html new file mode 100644 index 0000000000..e601b9d021 --- /dev/null +++ b/web-pages/product-site/legacy/blog/speech-to-text-timestamps-python.html @@ -0,0 +1,144 @@ + + + + + +语音识别带时间戳(字级 timestamps)Python 实战:每个字精确到毫秒 | FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

语音识别带时间戳(字级 timestamps)Python 实战:每个字精确到毫秒

+

2026-06-23 · FunASR 团队

+ +

很多场景不只要"识别出说了什么",还要知道每个字是在什么时间说的——做卡拉OK 逐字高亮、点击文字跳转音频、字幕对齐、视频剪辑定位。Whisper 的 word timestamps 是后加的(需要 DTW、精度一般)。FunASR 的 Paraformer 原生输出字级时间戳:每个字带 [起始毫秒, 结束毫秒],一次调用就有。下面是真实实测输出。

+ +

带时间戳识别(实测输出)

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", disable_update=True)
+res = model.generate(input="audio.wav")
+
+text = res[0]["text"]           # 欢 迎 大 家 来 体 验 ...
+timestamp = res[0]["timestamp"] # [[880, 1120], [1120, 1360], ...]  每个字的起止毫秒
+

timestamp 是一个 [start_ms, end_ms] 列表,和文本里的字一一对应。

+ +

把字和时间戳配对(实测)

+
chars = text.split()          # Paraformer 中文输出是空格分隔的字
+for ch, (start, end) in zip(chars, timestamp):
+    print(f"{ch}  {start}-{end}ms")
+

真实输出:

+ + + + + + + + + +
字起止(毫秒)
欢880 – 1120
迎1120 – 1360
大1380 – 1540
家1540 – 1780
来1780 – 2020
体2020 – 2180
验2180 – 2420
+

整句"欢迎大家来体验达摩院推出的语音识别模型"的 19 个字,每个都有精确到毫秒的起止时间。

+ +

能用来做什么

+
    +
  • 逐字高亮 / 卡拉OK:音频播放到某毫秒时,高亮对应的字。
    • +
  • 点击文字跳转音频:点某个字,用它的 start_ms seek 到音频对应位置。
    • +
  • 字幕精确对齐:按时间戳切句、生成逐字时间轴的字幕。完整做字幕见 生成 SRT/VTT 字幕。
    • +
  • 视频剪辑定位:按关键词找到它在音频里的精确时间点。
    • +
+ +

为什么用 Paraformer 拿时间戳

+ + + + + + +
FunASR ParaformerWhisper
字/词级时间戳✅ 原生,一次调用后加(--word_timestamps,靠 DTW)
精度非自回归 + CIF 对齐,稳定视实现而定
中文字级、CER 10.18%~20%
许可证开源,可商用开源
+

要更高精度的中文识别,默认用旗舰 Fun-ASR-Nano;完整中文实战见 中文语音识别;长音频分段见 VAD。

+ +

FunASR 全家桶开源(MIT)——字级时间戳、ASR、VAD、标点、说话人、LLM-ASR(旗舰 Fun-ASR-Nano),拿来即用。觉得有用就点个 Star 👇

⭐ Star FunASR

也欢迎 Star:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/voice-activity-detection-python.html b/web-pages/product-site/legacy/blog/voice-activity-detection-python.html new file mode 100644 index 0000000000..48870b7530 --- /dev/null +++ b/web-pages/product-site/legacy/blog/voice-activity-detection-python.html @@ -0,0 +1,155 @@ + + + + + +Python 语音活动检测(VAD):检测语音、去静音、按停顿切分音频 | FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

Python 语音活动检测(VAD):检测语音、去静音、按停顿切分音频

+

2026-06-22 · FunASR 团队

+ +

语音活动检测(VAD,Voice Activity Detection)是音频处理的基础一环:从一段录音里找出"哪里有人在说话、哪里是静音/噪声"。它是几乎所有语音流程的第一步——去掉静音省算力、把长录音按停顿切成小段、给下游 ASR 做预处理(包括给 Whisper 喂干净音频以省钱、防它在静音处胡乱"幻觉"出文字)。

+

FunASR 的 fsmn-vad 是一个工业级、轻量、极快的开源 VAD 模型,3 行 Python 就能用,返回每段语音的起止毫秒。下面全部是真实实测输出。

+ +

3 行代码做 VAD

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="fsmn-vad", disable_update=True)
+segments = model.generate(input="audio.wav")[0]["value"]
+
+print(segments)
+# [[610, 5530]]   # 单位:毫秒 —— 语音从 0.61s 到 5.53s,开头 610ms 静音被自动剔除
+

返回值是 [[start_ms, end_ms], ...] 的列表,每个区间是一段连续语音。上例里一段 5.55s 的录音,开头的 0.61s 静音被自动去掉了。

+ +

真实示例:13 秒录音切成 2 段

+

把两段话中间插 1.5s 停顿拼成一段 13.31s 的录音(模拟真实带停顿的录音),VAD 的真实输出:

+ + + + +
语音段起止时长
seg00.61s → 5.45s4.84s
seg17.28s → 13.29s6.01s
+

VAD 准确切出两段语音、把中间 1.5s 的停顿(5.45s~7.28s)和开头静音都识别为非语音。整段 13.31s 里语音占 82%,剔除了 18% 的静音。这一步在 GPU 上只花了 0.12 秒(约 110× 实时)——VAD 本身极轻,几乎不增加流程开销。

+ +

三个实用场景

+

1. 去静音 / 只保留语音

+
import soundfile as sf
+import numpy as np
+
+audio, sr = sf.read("audio.wav")
+segments = model.generate(input="audio.wav")[0]["value"]
+
+# 拼接所有语音段,丢弃静音
+speech = np.concatenate([audio[int(s/1000*sr):int(e/1000*sr)] for s, e in segments])
+sf.write("speech_only.wav", speech, sr)
+ +

2. 按停顿把长录音切成多个文件(便于批量识别)

+
for i, (s, e) in enumerate(segments):
+    chunk = audio[int(s/1000*sr):int(e/1000*sr)]
+    sf.write(f"chunk_{i}.wav", chunk, sr)
+# chunk_0.wav, chunk_1.wav, ...  每个文件是一段独立语音
+ +

3. 给任何 ASR(含 Whisper)做预处理

+

在跑识别之前先做 VAD,有两个实打实的好处:① 不把静音/噪声送进模型 = 省算力、降成本;② Whisper 在长静音处容易"幻觉"出重复文字,先用 VAD 切掉静音能显著减少幻觉。FunASR 的 ASR 模型(SenseVoice / Paraformer)更是可以直接 vad_model="fsmn-vad" 一行接上,内部自动分段。

+
from funasr import AutoModel
+# ASR 直接挂载 VAD,长音频自动分段识别
+asr = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad")
+result = asr.generate(input="long_audio.wav")
+ +

为什么用 fsmn-vad

+ + + + + + + +
fsmn-vad(FunASR)
用法3 行 Python,返回毫秒级语音区间
速度实测 13s 音频 0.12s(约 110× 实时)
体量轻量 FSMN 结构,CPU 也能实时跑
生态可独立用,也能一行挂到 FunASR 的 ASR 上自动分段
许可证开源,可商用
+

如果你已经在用 FunASR 做识别,VAD 是同一套工具、零额外依赖;如果你在用 Whisper 或别的 ASR,也可以把 fsmn-vad 当成独立的预处理器。想看完整的语音流程,可参考 转写超长音频 和 说话人分离。

+ +

FunASR 全家桶都是开源的——fsmn-vad、工业级 ASR、标点、说话人、情感事件、LLM-ASR,拿来即用。觉得有用就点个 Star 支持一下 👇

⭐ Star FunASR

也欢迎 Star:SenseVoice · Fun-ASR · FunClip

+ +

相关文章

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/blog/which-funasr-model.html b/web-pages/product-site/legacy/blog/which-funasr-model.html new file mode 100644 index 0000000000..f57fd89705 --- /dev/null +++ b/web-pages/product-site/legacy/blog/which-funasr-model.html @@ -0,0 +1,157 @@ + + + + + +选哪个 FunASR 模型?Nano vs MLT-Nano vs SenseVoice vs Paraformer(2026 指南)| FunASR 博客 + + + + + + + + + + + + + + + + + + +
+

选哪个 FunASR 模型?Nano vs MLT-Nano vs SenseVoice vs Paraformer（2026 选型指南）

+

2026-07-15 更新 · FunASR 团队

+ +

FunASR 提供三个主力模型族,Fun-ASR 另有独立的 MLT-Nano 多语 checkpoint。一句话选型:有 GPU、识别中英日及中文方言/口音就用旗舰 Fun-ASR-Nano;需要 31 语种用 Fun-ASR-MLT-Nano;只有 CPU 用 SenseVoice;CPU 且纯中文、要时间戳/热词用 Paraformer。

+ +

一张表选型

+ + + + + + +
你的情况选为什么
有 GPU / 中英日及中文方言/口音 / 要最强效果⭐ Fun-ASR-NanoLLM-ASR 旗舰,上下文/难例最强,中文 CER 8.06%
需要广泛多语种覆盖Fun-ASR-MLT-Nano独立 checkpoint,覆盖 31 种语言
只有 CPU / 边缘 / 要最快SenseVoice非自回归极快,CPU 也能实时,多语种+情感,CER 7.81%
CPU + 纯中文 + 要时间戳/热词Paraformer最轻量,字级时间戳,热词定制,CER 10.18%
+

表中的中文 CER 数据比较 Nano、SenseVoice 和 Paraformer,三者都远好于 Whisper(~20%)。CER 上 SenseVoice 略低,但 Fun-ASR-Nano 是基于 LLM 的旗舰模型——在真实复杂音频、上下文和专名上更鲁棒。MLT-Nano 用于 31 语种覆盖,不要把它的语言范围写到 Nano 上。

+ +

Fun-ASR-Nano —— LLM-ASR 旗舰,默认首选(GPU)

+

SenseVoice 编码器 + Qwen3-0.6B 解码,支持中文、英文、日语,以及 7 种中文方言和 26 种地域口音;具备 LLM 的上下文理解,难例与长尾词表现最好。

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", disable_update=True, device="cuda")
+res = model.generate(input="audio.wav")
+
+print(res[0]["text"])
+# 欢迎大家来体验达摩院推出的语音识别模型。
+

模型:Hugging Face · ModelScope。大规模/高并发可用 vLLM 加速(实测约 340× 实时)。详见 Fun-ASR-Nano 使用指南。

+ +

Fun-ASR-MLT-Nano —— 独立的 31 语种 checkpoint(GPU)

+

需要跨 31 种语言识别时选择 MLT-Nano。它与旗舰 Nano 是两个独立 checkpoint,模型 ID 和语言范围都不同。

+

模型:Hugging Face · ModelScope。

+ +

SenseVoice —— CPU/边缘首选

+

非自回归、轻量极快,CPU 也能实时,一遍出语种+情感+音频事件;q8 量化仅约 250MB。CPU 部署或要最低延迟时首选。

+
from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)  # CPU 默认
+res = model.generate(input="audio.wav", language="auto", use_itn=True)
+
+print(rich_transcription_postprocess(res[0]["text"]))
+# 欢迎大家来体验达摩院推出的语音识别模型。
+ +

Paraformer —— CPU + 纯中文(时间戳/热词)

+

最轻量的中文识别,提供字级时间戳和热词定制,做字幕或要逐字对齐时用它。

+
from funasr import AutoModel
+
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc")
+res = model.generate(input="audio.wav", batch_size_s=300)
+
+print(res[0]["text"])
+# 欢迎大家来体验达摩院推出的语音识别模型。
+# res[0]["timestamp"] -> [[880, 1120], [1120, 1360], ...] 每个字的起止毫秒
+ +

快速决策

+
    +
  • 要覆盖 31 语种? → Fun-ASR-MLT-Nano。
    • +
  • 有 GPU,主要识别中英日及中文方言/口音? → Fun-ASR-Nano(默认旗舰)。
    • +
  • 只有 CPU? 纯中文且要时间戳/热词 → Paraformer;否则(含多语种/情感) → SenseVoice。
    • +
  • 要粤语? SenseVoice 原生支持,见 粤语语音识别。
    • +
+

更多:中文语音识别实战 · FunASR vs Whisper benchmark · Fun-ASR-Nano 指南。

+ +

FunASR 全家桶开源(MIT)——Fun-ASR-Nano / MLT-Nano + SenseVoice + Paraformer + VAD/标点/说话人。觉得有用就点个 Star 👇

⭐ Star Fun-ASR

也欢迎 Star:FunASR · SenseVoice · FunClip

+ +

相关文章

+ +
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i=t.mount.mounts.indexOf(n);F(-1!==i),t.mount.mounts.splice(i,1)},lookup:(e,r)=>e.node_ops.lookup(e,r),mknod:(e,r,t)=>{var n=Ie.lookupPath(e,{parent:!0}).node,o=Ae.basename(e);if(!o||"."===o||".."===o)throw new Ie.ErrnoError(28);var i=Ie.mayCreate(n,o);if(i)throw new Ie.ErrnoError(i);if(!n.node_ops.mknod)throw new Ie.ErrnoError(63);return n.node_ops.mknod(n,o,r,t)},create:(e,r)=>(r=void 0!==r?r:438,r&=4095,r|=32768,Ie.mknod(e,r,0)),mkdir:(e,r)=>(r=void 0!==r?r:511,r&=1023,r|=16384,Ie.mknod(e,r,0)),mkdirTree:(e,r)=>{for(var t=e.split("/"),n="",o=0;o(void 0===t&&(t=r,r=438),r|=8192,Ie.mknod(e,r,t)),symlink:(e,r)=>{if(!Fe.resolve(e))throw new Ie.ErrnoError(44);var t=Ie.lookupPath(r,{parent:!0}).node;if(!t)throw new Ie.ErrnoError(44);var n=Ae.basename(r),o=Ie.mayCreate(t,n);if(o)throw new Ie.ErrnoError(o);if(!t.node_ops.symlink)throw new Ie.ErrnoError(63);return t.node_ops.symlink(t,n,e)},rename:(e,r)=>{var t,n,o=Ae.dirname(e),i=Ae.dirname(r),a=Ae.basename(e),s=Ae.basename(r);if(t=Ie.lookupPath(e,{parent:!0}).node,n=Ie.lookupPath(r,{parent:!0}).node,!t||!n)throw new Ie.ErrnoError(44);if(t.mount!==n.mount)throw new Ie.ErrnoError(75);var l,u=Ie.lookupNode(t,a),c=Fe.relative(e,i);if("."!==c.charAt(0))throw new Ie.ErrnoError(28);if("."!==(c=Fe.relative(r,o)).charAt(0))throw new Ie.ErrnoError(55);try{l=Ie.lookupNode(n,s)}catch(e){}if(u!==l){var d=Ie.isDir(u.mode),f=Ie.mayDelete(t,a,d);if(f)throw new Ie.ErrnoError(f);if(f=l?Ie.mayDelete(n,s,d):Ie.mayCreate(n,s))throw new Ie.ErrnoError(f);if(!t.node_ops.rename)throw new Ie.ErrnoError(63);if(Ie.isMountpoint(u)||l&&Ie.isMountpoint(l))throw new Ie.ErrnoError(10);if(n!==t&&(f=Ie.nodePermissions(t,"w")))throw new Ie.ErrnoError(f);Ie.hashRemoveNode(u);try{t.node_ops.rename(u,n,s)}catch(e){throw e}finally{Ie.hashAddNode(u)}}},rmdir:e=>{var r=Ie.lookupPath(e,{parent:!0}).node,t=Ae.basename(e),n=Ie.lookupNode(r,t),o=Ie.mayDelete(r,t,!0);if(o)throw new Ie.ErrnoError(o);if(!r.node_ops.rmdir)throw new Ie.ErrnoError(63);if(Ie.isMountpoint(n))throw new Ie.ErrnoError(10);r.node_ops.rmdir(r,t),Ie.destroyNode(n)},readdir:e=>{var r=Ie.lookupPath(e,{follow:!0}).node;if(!r.node_ops.readdir)throw new Ie.ErrnoError(54);return r.node_ops.readdir(r)},unlink:e=>{var r=Ie.lookupPath(e,{parent:!0}).node;if(!r)throw new Ie.ErrnoError(44);var t=Ae.basename(e),n=Ie.lookupNode(r,t),o=Ie.mayDelete(r,t,!1);if(o)throw new Ie.ErrnoError(o);if(!r.node_ops.unlink)throw new Ie.ErrnoError(63);if(Ie.isMountpoint(n))throw new Ie.ErrnoError(10);r.node_ops.unlink(r,t),Ie.destroyNode(n)},readlink:e=>{var r=Ie.lookupPath(e).node;if(!r)throw new Ie.ErrnoError(44);if(!r.node_ops.readlink)throw new Ie.ErrnoError(28);return Fe.resolve(Ie.getPath(r.parent),r.node_ops.readlink(r))},stat:(e,r)=>{var t=Ie.lookupPath(e,{follow:!r}).node;if(!t)throw new Ie.ErrnoError(44);if(!t.node_ops.getattr)throw new Ie.ErrnoError(63);return t.node_ops.getattr(t)},lstat:e=>Ie.stat(e,!0),chmod:(e,r,t)=>{var n;"string"==typeof e?n=Ie.lookupPath(e,{follow:!t}).node:n=e;if(!n.node_ops.setattr)throw new Ie.ErrnoError(63);n.node_ops.setattr(n,{mode:4095&r|-4096&n.mode,timestamp:Date.now()})},lchmod:(e,r)=>{Ie.chmod(e,r,!0)},fchmod:(e,r)=>{var t=Ie.getStream(e);if(!t)throw new Ie.ErrnoError(8);Ie.chmod(t.node,r)},chown:(e,r,t,n)=>{var o;"string"==typeof e?o=Ie.lookupPath(e,{follow:!n}).node:o=e;if(!o.node_ops.setattr)throw new Ie.ErrnoError(63);o.node_ops.setattr(o,{timestamp:Date.now()})},lchown:(e,r,t)=>{Ie.chown(e,r,t,!0)},fchown:(e,r,t)=>{var n=Ie.getStream(e);if(!n)throw new Ie.ErrnoError(8);Ie.chown(n.node,r,t)},truncate:(e,r)=>{if(r<0)throw new Ie.ErrnoError(28);var t;"string"==typeof e?t=Ie.lookupPath(e,{follow:!0}).node:t=e;if(!t.node_ops.setattr)throw new Ie.ErrnoError(63);if(Ie.isDir(t.mode))throw new Ie.ErrnoError(31);if(!Ie.isFile(t.mode))throw new Ie.ErrnoError(28);var n=Ie.nodePermissions(t,"w");if(n)throw new Ie.ErrnoError(n);t.node_ops.setattr(t,{size:r,timestamp:Date.now()})},ftruncate:(e,r)=>{var t=Ie.getStream(e);if(!t)throw new Ie.ErrnoError(8);if(0==(2097155&t.flags))throw new Ie.ErrnoError(28);Ie.truncate(t.node,r)},utime:(e,r,t)=>{var n=Ie.lookupPath(e,{follow:!0}).node;n.node_ops.setattr(n,{timestamp:Math.max(r,t)})},open:(e,t,n,o,i)=>{if(""===e)throw new Ie.ErrnoError(44);var a;if(n=void 0===n?438:n,n=64&(t="string"==typeof t?Ie.modeStringToFlags(t):t)?4095&n|32768:0,"object"==typeof e)a=e;else{e=Ae.normalize(e);try{a=Ie.lookupPath(e,{follow:!(131072&t)}).node}catch(e){}}var s=!1;if(64&t)if(a){if(128&t)throw new Ie.ErrnoError(20)}else a=Ie.mknod(e,n,0),s=!0;if(!a)throw new Ie.ErrnoError(44);if(Ie.isChrdev(a.mode)&&(t&=-513),65536&t&&!Ie.isDir(a.mode))throw new Ie.ErrnoError(54);if(!s){var l=Ie.mayOpen(a,t);if(l)throw new Ie.ErrnoError(l)}512&t&&Ie.truncate(a,0),t&=-131713;var u=Ie.createStream({node:a,path:Ie.getPath(a),flags:t,seekable:!0,position:0,stream_ops:a.stream_ops,ungotten:[],error:!1},o,i);return u.stream_ops.open&&u.stream_ops.open(u),!r.logReadFiles||1&t||(Ie.readFiles||(Ie.readFiles={}),e in Ie.readFiles||(Ie.readFiles[e]=1)),u},close:e=>{if(Ie.isClosed(e))throw new Ie.ErrnoError(8);e.getdents&&(e.getdents=null);try{e.stream_ops.close&&e.stream_ops.close(e)}catch(e){throw e}finally{Ie.closeStream(e.fd)}e.fd=null},isClosed:e=>null===e.fd,llseek:(e,r,t)=>{if(Ie.isClosed(e))throw new Ie.ErrnoError(8);if(!e.seekable||!e.stream_ops.llseek)throw new Ie.ErrnoError(70);if(0!=t&&1!=t&&2!=t)throw new Ie.ErrnoError(28);return e.position=e.stream_ops.llseek(e,r,t),e.ungotten=[],e.position},read:(e,r,t,n,o)=>{if(n<0||o<0)throw new Ie.ErrnoError(28);if(Ie.isClosed(e))throw new Ie.ErrnoError(8);if(1==(2097155&e.flags))throw new Ie.ErrnoError(8);if(Ie.isDir(e.node.mode))throw new Ie.ErrnoError(31);if(!e.stream_ops.read)throw new Ie.ErrnoError(28);var i=void 0!==o;if(i){if(!e.seekable)throw new Ie.ErrnoError(70)}else o=e.position;var a=e.stream_ops.read(e,r,t,n,o);return i||(e.position+=a),a},write:(e,r,t,n,o,i)=>{if(n<0||o<0)throw new Ie.ErrnoError(28);if(Ie.isClosed(e))throw new Ie.ErrnoError(8);if(0==(2097155&e.flags))throw new Ie.ErrnoError(8);if(Ie.isDir(e.node.mode))throw new Ie.ErrnoError(31);if(!e.stream_ops.write)throw new Ie.ErrnoError(28);e.seekable&&1024&e.flags&&Ie.llseek(e,0,2);var a=void 0!==o;if(a){if(!e.seekable)throw new Ie.ErrnoError(70)}else o=e.position;var s=e.stream_ops.write(e,r,t,n,o,i);return a||(e.position+=s),s},allocate:(e,r,t)=>{if(Ie.isClosed(e))throw new Ie.ErrnoError(8);if(r<0||t<=0)throw new Ie.ErrnoError(28);if(0==(2097155&e.flags))throw new Ie.ErrnoError(8);if(!Ie.isFile(e.node.mode)&&!Ie.isDir(e.node.mode))throw new Ie.ErrnoError(43);if(!e.stream_ops.allocate)throw new Ie.ErrnoError(138);e.stream_ops.allocate(e,r,t)},mmap:(e,r,t,n,o,i)=>{if(0!=(2&o)&&0==(2&i)&&2!=(2097155&e.flags))throw new Ie.ErrnoError(2);if(1==(2097155&e.flags))throw new Ie.ErrnoError(2);if(!e.stream_ops.mmap)throw new Ie.ErrnoError(43);return e.stream_ops.mmap(e,r,t,n,o,i)},msync:(e,r,t,n,o)=>e&&e.stream_ops.msync?e.stream_ops.msync(e,r,t,n,o):0,munmap:e=>0,ioctl:(e,r,t)=>{if(!e.stream_ops.ioctl)throw new Ie.ErrnoError(59);return e.stream_ops.ioctl(e,r,t)},readFile:function(e){let r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(r.flags=r.flags||0,r.encoding=r.encoding||"binary","utf8"!==r.encoding&&"binary"!==r.encoding)throw new Error('Invalid encoding type "'+r.encoding+'"');var t,n=Ie.open(e,r.flags),o=Ie.stat(e),i=o.size,a=new Uint8Array(i);return Ie.read(n,a,0,i,0),"utf8"===r.encoding?t=O(a,0):"binary"===r.encoding&&(t=a),Ie.close(n),t},writeFile:function(e,r){let t=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};t.flags=t.flags||577;var n=Ie.open(e,t.flags,t.mode);if("string"==typeof r){var o=new Uint8Array(I(r)+1),i=M(r,o,0,o.length);Ie.write(n,o,0,i,void 0,t.canOwn)}else{if(!ArrayBuffer.isView(r))throw new Error("Unsupported data type");Ie.write(n,r,0,r.byteLength,void 0,t.canOwn)}Ie.close(n)},cwd:()=>Ie.currentPath,chdir:e=>{var r=Ie.lookupPath(e,{follow:!0});if(null===r.node)throw new Ie.ErrnoError(44);if(!Ie.isDir(r.node.mode))throw new Ie.ErrnoError(54);var t=Ie.nodePermissions(r.node,"x");if(t)throw new Ie.ErrnoError(t);Ie.currentPath=r.path},createDefaultDirectories:()=>{Ie.mkdir("/tmp"),Ie.mkdir("/home"),Ie.mkdir("/home/web_user")},createDefaultDevices:()=>{Ie.mkdir("/dev"),Ie.registerDevice(Ie.makedev(1,3),{read:()=>0,write:(e,r,t,n,o)=>n}),Ie.mkdev("/dev/null",Ie.makedev(1,3)),De.register(Ie.makedev(5,0),De.default_tty_ops),De.register(Ie.makedev(6,0),De.default_tty1_ops),Ie.mkdev("/dev/tty",Ie.makedev(5,0)),Ie.mkdev("/dev/tty1",Ie.makedev(6,0));var e=function(){if("object"==typeof crypto&&"function"==typeof crypto.getRandomValues){var e=new Uint8Array(1);return function(){return crypto.getRandomValues(e),e[0]}}if(u)try{var r=a.default;return function(){return r.randomBytes(1)[0]}}catch(e){}return function(){ge("no cryptographic support found for randomDevice. consider polyfilling it if you want to use something insecure like Math.random(), e.g. put this in a --pre-js: var crypto = { getRandomValues: function(array) { for (var i = 0; i < array.length; i++) array[i] = (Math.random()*256)|0 } };")}}();Ie.createDevice("/dev","random",e),Ie.createDevice("/dev","urandom",e),Ie.mkdir("/dev/shm"),Ie.mkdir("/dev/shm/tmp")},createSpecialDirectories:()=>{Ie.mkdir("/proc");var e=Ie.mkdir("/proc/self");Ie.mkdir("/proc/self/fd"),Ie.mount({mount:()=>{var r=Ie.createNode(e,"fd",16895,73);return r.node_ops={lookup:(e,r)=>{var t=+r,n=Ie.getStream(t);if(!n)throw new Ie.ErrnoError(8);var o={parent:null,mount:{mountpoint:"fake"},node_ops:{readlink:()=>n.path}};return o.parent=o,o}},r}},{},"/proc/self/fd")},createStandardStreams:()=>{r.stdin?Ie.createDevice("/dev","stdin",r.stdin):Ie.symlink("/dev/tty","/dev/stdin"),r.stdout?Ie.createDevice("/dev","stdout",null,r.stdout):Ie.symlink("/dev/tty","/dev/stdout"),r.stderr?Ie.createDevice("/dev","stderr",null,r.stderr):Ie.symlink("/dev/tty1","/dev/stderr");var e=Ie.open("/dev/stdin",0),t=Ie.open("/dev/stdout",1),n=Ie.open("/dev/stderr",1);F(0===e.fd,"invalid handle for stdin ("+e.fd+")"),F(1===t.fd,"invalid handle for stdout ("+t.fd+")"),F(2===n.fd,"invalid handle for stderr ("+n.fd+")")},ensureErrnoError:()=>{Ie.ErrnoError||(Ie.ErrnoError=function(e,r){this.node=r,this.setErrno=function(e){for(var r in this.errno=e,Ne)if(Ne[r]===e){this.code=r;break}},this.setErrno(e),this.message=Me[e],this.stack&&(Object.defineProperty(this,"stack",{value:(new Error).stack,writable:!0}),this.stack=Se(this.stack))},Ie.ErrnoError.prototype=new Error,Ie.ErrnoError.prototype.constructor=Ie.ErrnoError,[44].forEach((e=>{Ie.genericErrors[e]=new Ie.ErrnoError(e),Ie.genericErrors[e].stack=""})))},staticInit:()=>{Ie.ensureErrnoError(),Ie.nameTable=new Array(4096),Ie.mount(Re,{},"/"),Ie.createDefaultDirectories(),Ie.createDefaultDevices(),Ie.createSpecialDirectories(),Ie.filesystems={MEMFS:Re}},init:(e,t,n)=>{F(!Ie.init.initialized,"FS.init was previously called. 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Lazy loading only works in web workers. Use --embed-file or --preload-file in emcc on the main thread.");if(!d)throw new Error("Cannot load without read() or XMLHttpRequest.");try{e.contents=pt(d(e.url),!0),e.usedBytes=e.contents.length}catch(e){throw new Ie.ErrnoError(29)}},createLazyFile:(e,r,t,n,o)=>{function i(){this.lengthKnown=!1,this.chunks=[]}if(i.prototype.get=function(e){if(!(e>this.length-1||e<0)){var r=e%this.chunkSize,t=e/this.chunkSize|0;return this.getter(t)[r]}},i.prototype.setDataGetter=function(e){this.getter=e},i.prototype.cacheLength=function(){var e=new XMLHttpRequest;if(e.open("HEAD",t,!1),e.send(null),!(e.status>=200&&e.status<300||304===e.status))throw new Error("Couldn't load "+t+". 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e.bindTexture(e.TEXTURE_2D,n),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.bindTexture(e.TEXTURE_2D,null),e.uniform1i(e.getUniformLocation(a,r),t),n}e.enableVertexAttribArray(c),e.vertexAttribPointer(c,2,e.FLOAT,!1,0,0);var f=d("ySampler",0),p=d("uSampler",1),m=d("vSampler",2);return{render:function(r,t,n,o,i){e.viewport(0,0,r,t),e.activeTexture(e.TEXTURE0),e.bindTexture(e.TEXTURE_2D,f),e.texImage2D(e.TEXTURE_2D,0,e.LUMINANCE,r,t,0,e.LUMINANCE,e.UNSIGNED_BYTE,n),e.activeTexture(e.TEXTURE1),e.bindTexture(e.TEXTURE_2D,p),e.texImage2D(e.TEXTURE_2D,0,e.LUMINANCE,r/2,t/2,0,e.LUMINANCE,e.UNSIGNED_BYTE,o),e.activeTexture(e.TEXTURE2),e.bindTexture(e.TEXTURE_2D,m),e.texImage2D(e.TEXTURE_2D,0,e.LUMINANCE,r/2,t/2,0,e.LUMINANCE,e.UNSIGNED_BYTE,i),e.drawArrays(e.TRIANGLE_STRIP,0,4)},destroy:function(){try{e.deleteProgram(a),e.deleteBuffer(s),e.deleteBuffer(u),e.deleteTexture(f),e.deleteTexture(p),e.deleteBuffer(m)}catch(e){}}}})(this.offscreenCanvasGL,n.opt.openWebglAlignment),this.draw=function(n,i,a,s){const u=l.HEAPU8.subarray(i,i+t),c=l.HEAPU8.subarray(a,a+o),d=l.HEAPU8.subarray(s,s+o);this.webglObj.render(e,r,u,c,d);let f=this.offscreenCanvas.transferToImageBitmap();postMessage({cmd:g,buffer:f,delay:this.delay,ts:n},[f])}):this.draw=function(e,r,n,i){const a=[Uint8Array.from(l.HEAPU8.subarray(r,r+t)),Uint8Array.from(l.HEAPU8.subarray(n,n+o)),Uint8Array.from(l.HEAPU8.subarray(i,i+o))];postMessage({cmd:g,output:a,delay:this.delay,ts:e},a.map((e=>e.buffer)))}},getDelay:function(e){if(!e)return-1;if(this.firstTimestamp){if(e){const r=Date.now()-this.startTimestamp,t=e-this.firstTimestamp;this.delay=r>=t?r-t:t-r}}else this.firstTimestamp=e,this.startTimestamp=Date.now(),this.delay=-1;return this.delay},resetDelay:function(){this.firstTimestamp=null,this.startTimestamp=null,this.delay=-1},init:function(){n.opt.debug&&console.log("Jessibuca: [worker] init");const r=e=>{n.opt.useWCS&&n.useOffscreen()&&e.type===_&&t.decode?t.decode(e.payload,e.ts):e.decoder.decode(e.payload,e.ts)};this.stopId=setInterval((()=>{if(e.length)if(this.dropping){for((t=e.shift()).type===b&&0===t.payload[1]&&r(t);!t.isIFrame&&e.length;)(t=e.shift()).type===b&&0===t.payload[1]&&r(t);t.isIFrame&&(this.dropping=!1,r(t))}else{var t=e[0];if(-1===this.getDelay(t.ts))e.shift(),r(t);else if(this.delay>n.opt.videoBuffer+n.opt.videoBufferDelay)this.resetDelay(),this.dropping=!0;else for(;e.length&&(t=e[0],this.getDelay(t.ts)>n.opt.videoBuffer);)e.shift(),r(t)}}),10)},close:function(){n.opt.debug&&console.log("Jessibuca: [worker]: close"),clearInterval(this.stopId),this.stopId=null,o.clear&&o.clear(),i.clear&&i.clear(),t.reset&&t.reset(),this.firstTimestamp=null,this.startTimestamp=null,this.delay=-1,this.dropping=!1,this.webglObj&&(this.webglObj.destroy(),this.offscreenCanvas=null,this.offscreenCanvasGL=null,this.offscreenCanvasCtx=null),e=[],r=[],delete this.playAudioPlanar,delete this.draw},pushBuffer:function(r,t){t.type===b?e.push({ts:t.ts,payload:r,decoder:o,type:b}):t.type===_&&e.push({ts:t.ts,payload:r,decoder:i,type:_,isIFrame:t.isIFrame})}},o=new l.AudioDecoder(n),i=new l.VideoDecoder(n);postMessage({cmd:T}),self.onmessage=function(e){var r=e.data;switch(r.cmd){case T:try{n.opt=Object.assign(n.opt,JSON.parse(r.opt))}catch(e){}o.sample_rate=r.sampleRate,n.init();break;case k:n.pushBuffer(r.buffer,r.options);break;case S:o.decode(r.buffer,r.ts);break;case C:i.decode(r.buffer,r.ts);break;case P:n.close();break;case A:n.opt[r.key]=r.value}}}})); diff --git a/web-pages/product-site/legacy/donors.html b/web-pages/product-site/legacy/donors.html new file mode 100644 index 0000000000..762e50c73c --- /dev/null +++ b/web-pages/product-site/legacy/donors.html @@ -0,0 +1,57 @@ + + +FunASR 社区功德榜 + + + + + + + +
+
Community
+

FunASR 社区功德榜

+

感谢每一位支持 FunASR 开源社区的朋友。捐赠资金用于社区基础设施建设，包括购买和维护服务器，以及购买、续费和维护 www.funasr.com 域名，保障官网、文档和社区服务稳定运行。下列名单整理自历史官网首页，仅作为社区致谢保留。

+
+
刘连响300 元
+
程俊陶300 元
+
黄明236 元
+
高志付235 元
+
陈翔200 元
+
夏侯杰200 元
+
黄药师198 元
+
肖炜100 元
+
刘柱100 元
+
黄水杯85 元
+
子龙85 元
+
+

感谢大家让 FunASR 的开源社区可以持续向前。若名单或金额需要更正，请通过 GitHub issue 联系维护者。

+
+ + + diff --git a/web-pages/product-site/legacy/ecosystem.html b/web-pages/product-site/legacy/ecosystem.html new file mode 100644 index 0000000000..1bca6463c0 --- /dev/null +++ b/web-pages/product-site/legacy/ecosystem.html @@ -0,0 +1,400 @@ + + + + + +FunASR 生态 — 集成 FunASR 的开源项目 + + + + + + + + + + + + + + + + + + +
+
+ +

FunASR 生态系统

+

基于 FunASR、SenseVoice、Paraformer 构建的开源项目与集成方案。

+ +
+
50+
集成项目
+
50+
支持语种
+
35K+
GitHub Stars
+
100万+
月安装量
+
+ +

视频与媒体工具

+
+
+
FunClip
+
6K+ stars
+
基于语音识别的智能视频剪辑。自动生成字幕，支持按关键词和说话人剪辑视频片段。下载 v2.1.0
+
视频官方
+
+
+
pyVideoTrans
+
17.6K stars
+
视频翻译工具。使用 FunASR 进行中文语音识别，自动生成和翻译字幕。
+
视频翻译
+
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Voice-Pro
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10.4K stars
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Gradio WebUI 音频处理工具箱，集成 TTS、语音克隆与 ASR 能力。
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Linly-Dubbing
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AI 视频配音工具包。自动语音识别、翻译和语音克隆，实现多语言视频配音。
+
配音视频
+
+
+ +

语音输入与桌面应用

+
+
+
CapsWriter-Offline
+
5.5K stars
+
PC 端语音输入工具。按住 CapsLock 说话，松开自动上屏。基于 FunASR Paraformer 离线识别。
+
桌面语音输入
+
+
+
OpenLess
+
1.8K stars
+
macOS/Windows 语音输入工具。按住快捷键说话，松开即得润色后的文字。使用 SenseVoice 模型。
+
桌面macOS
+
+
+
ququ
+
2.2K stars
+
开源 Wispr Flow 替代方案。集成 FunASR 本地模型和可配置大语言模型的桌面语音工作流。
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桌面语音输入
+
+
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MTools
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1.2K stars
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多功能桌面应用程序，集成音视频处理、图片编辑和 AI 增强语音转写。
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桌面工具箱
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VocoType
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704 stars
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本地端侧隐私安全语音输入工具。通过快捷键将语音实时转文字并自动输入。支持 MCP 集成。
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语音输入隐私
+
+
+
LiveTranslate
+
325 stars
+
实时音频翻译工具。捕获系统声音和麦克风，使用 SenseVoice 识别后 LLM 流式翻译显示。
+
翻译实时
+
+
+
VocoType-linux
+
139 stars
+
高性能 Linux 离线中文语音输入法。基于 FunASR，0.1 秒瞬时上屏，支持 IBus/Fcitx5。
+
Linux输入法
+
+
+
Murmur
+
103 stars
+
macOS 离线语音转文字工具。按键说话即用，支持任意应用内使用，完全本地处理。
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macOS语音输入
+
+
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AriaType
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74 stars
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语音驱动的桌面写作工具。支持语音输入、文本润色和跨应用协作。
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桌面写作
+
+
+
VoiceSnap
+
73 stars
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纯离线语音输入工具，Typeless 免费平替。SenseVoice + DirectML，数据永不出设备，适合涉密场景。
+
离线安全
+
+
+ +

语音助手与智能体

+
+
+
Fay 数字人
+
12.8K stars
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数字人 Agent 框架，连接 2.5D/3D 虚拟人与大语言模型。使用 FunASR 实时语音识别。
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数字人智能体
+
+
+
Duix 数字人
+
13.4K stars
+
开源 AI 数字人工具包，支持离线视频生成和实时交互。使用 FunASR 进行语音识别。
+
数字人视频生成
+
+
+
悟空机器人
+
7.1K stars
+
中文语音对话机器人/智能音箱，支持树莓派部署，内置 FunASR 语音识别引擎。
+
IoT助手
+
+
+
Paseo
+
6.9K stars
+
手机端编程助手，通过 Sherpa ONNX 使用 Paraformer 和 SenseVoice 进行语音识别。
+
编程智能体
+
+
+
Linly-Talker
+
3.3K stars
+
数字人对话系统，融合 ASR、LLM 和 TTS，实现与虚拟形象自然对话。使用 FunASR 语音识别。
+
数字人对话
+
+
+
AudioNotes
+
2.1K stars
+
音视频内容提取工具，自动整理为结构化 Markdown 笔记。使用 FunASR 高精度转写。
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笔记效率
+
+
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百聆
+
1.7K stars
+
类 GPT-4o 语音聊天机器人，完整 ASR + LLM + TTS 语音对话方案。使用 FunASR 语音识别。
+
语音对话GPT-4o
+
+
+ +

AI 平台与框架

+
+
+
GPT-SoVITS
+
58K stars
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一分钟语音数据即可训练 TTS。使用 FunASR 进行训练数据标注。
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TTS训练
+
+
+
LocalAI
+
33K stars
+
自托管 OpenAI 替代方案。FunASR 作为语音识别后端（PR 审核中）。
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LLM自托管
+
+
+
LiteLLM
+
54.3K stars
+
OpenAI 兼容 AI Gateway。FunASR / SenseVoice 可通过 custom_openai 作为自托管语音转写端点接入。
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AI GatewayOpenAI 兼容
+
+
+
Pipecat
+
12.5K stars
+
语音与多模态对话 AI 框架。FunASR 作为社区 STT 集成。
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对话 AI
+
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Amphion
+
9.8K stars
+
OpenMMLab 开源音频、音乐和语音生成工具包。使用 FunASR 进行 TTS 评测与数据处理。
+
音频工具OpenMMLab
+
+
+
Dify · FunASR 官方插件
+
150K stars · 352 installs
+
FunASR 官方插件 0.1.1 已上线 Dify Marketplace，内置 SenseVoice、Fun-ASR-Nano 与 Paraformer，支持最大 25 MB 音频上传。
+
官方插件语音转写
+
+
+
Xinference
+
9.3K stars
+
分布式推理框架。内置 FunASR 语音识别后端，支持一键部署 ASR 模型服务。
+
推理框架分布式
+
+
+
AIGCPanel
+
5K stars
+
一站式 AI 数字人系统，支持视频合成、声音合成与克隆。集成 FunASR 语音识别。
+
数字人AIGC
+
+
+
MiniMind-O
+
1.6K stars
+
轻量级多模态模型，融合视觉、音频和语言理解能力。使用 FunASR 作为语音识别模块。
+
多模态LLM
+
+
+
ComfyUI-FunAudioLLM
+
95 stars
+
ComfyUI 自定义节点，集成 SenseVoice 和 CosyVoice。可视化搭建语音识别与合成工作流。
+
ComfyUI工作流
+
+
+ +

SenseVoice 社区扩展

+
+
+
OmniSenseVoice
+
894 stars
+
增强版 SenseVoice，支持高精度单词级时间戳输出。速度与原版一致。
+
时间戳SenseVoice
+
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+
api4sensevoice
+
541 stars
+
SenseVoice API 和 WebSocket 服务器。支持 VAD 检测、实时流式识别和说话人验证。
+
APIWebSocket
+
+
+
streaming-sensevoice
+
451 stars
+
伪流式 SenseVoice 实现，支持热词增强。实现低延迟准实时语音识别。
+
流式热词
+
+
+
SenseVoice-python
+
111 stars
+
基于 ONNX Runtime 的 SenseVoice 推理库。企业级部署方案，无需 PyTorch 依赖。
+
ONNX部署
+
+
+
SenseVoice-Api
+
109 stars
+
SenseVoice 的 FastAPI 封装，ONNX 推理，体积更小。附带量化模型，支持 GPU 加速。
+
FastAPI量化
+
+
+
SenseVoice-OneApi
+
93 stars
+
SenseVoice API 服务，可无缝对接 OneAPI。统一接口管理多个语音识别模型。
+
OneAPIAPI
+
+
+ +

跨平台推理

+
+
+
Sherpa-ONNX
+
5K+ stars
+
跨平台语音处理框架。可在 iOS、Android、树莓派、浏览器上运行 SenseVoice 和 Paraformer。
+
移动端边缘计算
+
+
+
RapidASR
+
608 stars
+
跨平台 ASR 推理库，基于 ONNX Runtime 和 FunASR。开箱即用，支持中英文混合识别。
+
ONNX跨平台
+
+
+
SenseVoice.cpp
+
550 stars
+
SenseVoice 模型的 C/C++ 实现。无需 Python 依赖，纯 C++ 推理。
+
C++嵌入式
+
+
+
Transformers
+
142K stars
+
Hugging Face Transformers 库。Fun-ASR-Nano 集成（PR 审核中）。
+
ML 框架
+
+
+
Vox-Box
+
211 stars
+
OpenAI 兼容的语音服务器，支持 FunASR、Whisper、Bark、CosyVoice 后端。
+
API 服务OpenAI 兼容
+
+
+
FunSpeech
+
136 stars
+
开箱即用的本地私有化语音服务。微服务架构，兼容阿里云语音 API 与 OpenAI TTS API，集成 FunASR 和 CosyVoice。
+
API 服务私有部署
+
+
+
FunASR-GGML
+
113 stars
+
基于 GGML 的 C++ 推理引擎。支持 CPU/CUDA，实时麦克风流式识别，单 GGUF 文件部署。
+
GGMLC++
+
+
+
ManySpeech
+
79 stars
+
多模型 ASR 推理方案，支持 Paraformer、SenseVoice、Whisper 等。ONNX 推理，适配多场景。
+
多模型ONNX
+
+
+ +

快速上手

+ +
pip install funasr
+
+# Python API
+from funasr import AutoModel
+model = AutoModel(model="iic/SenseVoiceSmall")
+result = model.generate(input="audio.wav")
+
+# 或启动 OpenAI 兼容 API 服务
+pip install vllm fastapi uvicorn python-multipart
+funasr-server --device cuda
+ +
+

使用 FunASR 构建了项目？欢迎提交到此列表。

+GitHub +提交 Issue +
+ +
+
+ + + + diff --git a/web-pages/product-site/legacy/en/blog/cantonese-speech-recognition.html b/web-pages/product-site/legacy/en/blog/cantonese-speech-recognition.html new file mode 100644 index 0000000000..5b2b6f0423 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/cantonese-speech-recognition.html @@ -0,0 +1,137 @@ + + + + + +Cantonese Speech Recognition in Python — SenseVoice Keeps Real Cantonese (Whisper Doesn't) | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Cantonese Speech Recognition in Python — SenseVoice Keeps Real Cantonese (Whisper Turns It Into Mandarin)

+

2026-06-21 · FunASR Team

+ +

Cantonese has around 85 million speakers, yet open-source ASR support for it is weak. Whisper nominally lists Cantonese, but in practice it treats Cantonese as Chinese (zh) and rewrites it into Standard Written Mandarin — 唔→不, 嘅→的, 呢→這 — so the actual Cantonese is lost.

+

SenseVoice (an open-source multilingual speech-understanding model from the FunAudioLLM team) natively supports Cantonese (yue): it auto-detects the language and preserves genuine colloquial Cantonese characters, all in one non-autoregressive pass. Here is a real side-by-side on the same Cantonese clip.

+ +

Real comparison: the same Cantonese audio

+

Tested on SenseVoice's bundled Cantonese sample yue.mp3, actual outputs from both models:

+ + + + +
ModelDetected languageOutput
SenseVoiceyue (Cantonese)呢几个字都表达唔到,我想讲嘅意思。
Whisper-smallzh (Chinese)這幾個字都表達不到我想講的意思
+

The difference is clear: SenseVoice keeps the hallmark Cantonese words 呢 / 唔 / 嘅; Whisper has no separate Cantonese, recognizes it as Chinese and converts it to written Mandarin (呢→這, 唔→不, 嘅→的) — roughly the same meaning, but no longer Cantonese.

+ +

Cantonese ASR in three lines

+
pip install funasr
+
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)
+res = model.generate(input="cantonese.wav", cache={}, language="yue", use_itn=True)
+
+print(rich_transcription_postprocess(res[0]["text"]))
+# 呢几个字都表达唔到，我想讲嘅意思。
+

Set language to "yue" directly, or use "auto" for automatic language ID — on this clip auto also correctly resolves to yue with identical output.

+ +

What else the raw output carries

+

SenseVoice's raw output begins with a set of tags:

+
<|yue|><|NEUTRAL|><|Speech|><|withitn|>呢几个字都表达唔到，我想讲嘅意思。
+

Meaning: <|yue|> = language is Cantonese, <|NEUTRAL|> = emotion, <|Speech|> = audio event (clean speech), <|withitn|> = inverse text normalization applied. So while transcribing Cantonese you also get language, emotion and audio events for free. One call to rich_transcription_postprocess() strips the tags to plain text.

+ +

Why SenseVoice for Cantonese

+ + + + + + + + +
SenseVoiceWhisper
Colloquial Cantonese (呢/唔/嘅)✅ preserved❌ rewritten to Mandarin
Automatic language ID✅ resolves to yuecollapses to zh
Emotion / audio events✅ in one pass❌
Inverse text normalization✅ built-inpartial
Speednon-autoregressive, ~15× faster than Whisper-Largeautoregressive baseline
Licenseopen-source, commercial-friendlyopen-source
+

If your use case mixes Mandarin and Cantonese (HK/Macau apps, call centers, media subtitles), one SenseVoice model covers both. For higher-accuracy offline transcription, see the FunASR-family Fun-ASR-Nano, and the full Chinese accuracy comparison in the FunASR vs Whisper benchmark.

+ +

The whole FunASR stack is open-source — industrial-grade ASR / VAD / punctuation / speaker / emotion & events / LLM-ASR, with Cantonese out of the box. If it helps, a GitHub Star really supports the project 👇

⭐ Star SenseVoice

Also star:FunASR · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/chinese-speech-recognition.html b/web-pages/product-site/legacy/en/blog/chinese-speech-recognition.html new file mode 100644 index 0000000000..bbfcb584aa --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/chinese-speech-recognition.html @@ -0,0 +1,160 @@ + + + + + +Chinese (Mandarin) Speech Recognition in Python — Fast & Accurate with FunASR | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Chinese (Mandarin) Speech Recognition in Python — Fast & Accurate with FunASR

+

2026-06-22 · FunASR Team

+ +

Chinese is hard for general ASR — OpenAI Whisper's character error rate (CER) on Chinese is around 20%, with frequent homophone and proper-noun mistakes. FunASR (open-sourced by Tongyi Lab) is purpose-built for Chinese, with CER far below Whisper. The default recommendation is the flagship Fun-ASR-Nano (LLM-ASR); on CPU use SenseVoice / Paraformer. Here's the practical how-to, with real measured output.

+ +

Chinese ASR in 3 lines (flagship Fun-ASR-Nano, real output)

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", disable_update=True, device="cuda")
+res = model.generate(input="audio.wav")
+
+print(res[0]["text"])
+# 欢迎大家来体验达摩院推出的语音识别模型。  ("Welcome everyone to try the speech model from DAMO Academy")
+

Fun-ASR-Nano is the LLM-based flagship recognizer (SenseVoice encoder + a Qwen3 decoder) for zh/en/ja plus Chinese dialects/accents, with stronger context, hard-case, and proper-noun handling — the default for Chinese. Use the separate Fun-ASR-MLT-Nano for 31 languages. For scale, use vLLM (~340× real-time); see the Fun-ASR-Nano guide.

+ +

Pick the right Chinese model (by scenario)

+ + + + + +
Your situationPickChinese CER
Have a GPU / want the best⭐ Fun-ASR-Nano8.06%
CPU / edge / want fastestSenseVoice7.81%
CPU + Chinese-only + timestamps/hotwordsParaformer10.18%
+

All three beat Whisper (~20%) by a wide margin. Full guidance in the FunASR model selection guide. The two lightweight CPU options:

+
# SenseVoice — the CPU choice, non-autoregressive & fast, multilingual + emotion
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)
+res = model.generate(input="audio.wav", language="auto", use_itn=True)
+print(rich_transcription_postprocess(res[0]["text"]))
+# 欢迎大家来体验达摩院推出的语音识别模型。
+
+# Paraformer — Chinese-only + character timestamps + hotwords
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc")
+res = model.generate(input="audio.wav", batch_size_s=300)
+print(res[0]["text"])           # 欢迎大家来体验达摩院推出的语音识别模型。
+# res[0]["timestamp"] -> [[880, 1120], [1120, 1360], ...] per-character start/end ms
+ +

Why FunASR for Chinese (instead of Whisper)

+ + + + + + +
Chinese test-set CER (lower is better)CER
FunASR · Fun-ASR-Nano (flagship)8.06%
FunASR · SenseVoice7.81%
FunASR · Paraformer10.18%
Whisper-large-v320.02%
+

Beyond accuracy, FunASR ships a full Chinese toolkit: inverse text normalization (ITN), punctuation restoration, hotword / proper-noun customization, character-level timestamps, and speaker diarization. Full comparisons in the FunASR vs Whisper benchmark and vs faster-whisper.

+ +

Common add-ons (each is one line)

+ + +

Command line / server

+
# transcribe from the CLI, export srt / json, with speaker labels
+funasr audio.wav --model paraformer-zh -f srt --spk
+
+# OpenAI-compatible transcription server (flagship fun-asr-nano by default)
+funasr-server --device cuda
+

See command-line transcription and the self-hosted cloud-STT alternative.

+ +

FunASR is the go-to open-source choice for Chinese ASR (MIT) — flagship Fun-ASR-Nano + SenseVoice + Paraformer. If it helps, a GitHub Star really supports the project 👇

⭐ Star Fun-ASR

Also star:FunASR · SenseVoice · FunClip

+ +

Related posts

+ +
+ + + diff --git a/web-pages/product-site/legacy/en/blog/fun-asr-nano-guide.html b/web-pages/product-site/legacy/en/blog/fun-asr-nano-guide.html new file mode 100644 index 0000000000..ae180935ab --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/fun-asr-nano-guide.html @@ -0,0 +1,168 @@ + + + + + +Fun-ASR-Nano Guide: 800M ASR LLM for Chinese, English, Japanese and Chinese Dialects | FunASR Blog + + + + + + + + + + + + + + + +
+

Fun-ASR-Nano Guide: An 800M ASR LLM for Chinese, English, Japanese and Chinese Dialects

+

FunASR Blog · Updated 2026-07-15 · Flagship model

+

Fun-ASR-Nano is an end-to-end speech recognition large model from the FunAudioLLM team, built on an "Audio Encoder + Adaptor + LLM (Qwen)" architecture with ~800M parameters, trained on tens of millions of hours of real speech. It is our current flagship recommended ASR model.

+

Highlights:

+
    +
  • Supports Chinese, English, and Japanese; Chinese covers 7 dialect groups (Wu, Cantonese, Min, Hakka, Gan, Xiang, Jin) and 26 regional accents
    • +
  • Hotwords and a low-latency streaming service
    • +
  • The FunASR pipeline can combine separate VAD + cam++ + punctuation models for speaker diarization (not a native Nano checkpoint output)
    • +
  • Even lyric and rap speech recognition
    • +
+

Timestamp boundary: The released model.pt checkpoint lacks trained CTC alignment weights, so it does not provide reliable checkpoint-native character timestamps. In the offline service, VAD returns reliable segment start and end times; use Paraformer when you need accurate character-level timestamps. See issue #106.

+

For recognition across 31 languages, use the separate Fun-ASR-MLT-Nano-2512 checkpoint. Its language scope must not be attributed to flagship Nano.

+ +

1. Install

+
pip install -U funasr torch torchaudio
+ +

2. Basic inference (with hotwords)

+

A minimal, tested example (downloads the model automatically):

+
from funasr import AutoModel
+
+model = AutoModel(
+    model="FunAudioLLM/Fun-ASR-Nano-2512",
+    trust_remote_code=True,
+    remote_code="./model.py",
+    device="cuda:0",
+    hub="hf",          # use hub="ms" for ModelScope
+)
+wav = f"{model.model_path}/example/zh.mp3"   # bundled sample audio
+res = model.generate(
+    input=[wav], cache={}, batch_size=1,
+    hotwords=["开放时间"],   # boosts recall of domain terms
+    language="中文",
+    itn=True,
+)
+print(res[0]["text"])
+

Verified output: 开放时间早上九点至下午五点。 — the hotword was recognized correctly.

+ +

3. Long audio: add VAD

+
model = AutoModel(
+    model="FunAudioLLM/Fun-ASR-Nano-2512",
+    trust_remote_code=True, remote_code="./model.py",
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0", hub="hf",
+)
+res = model.generate(input=[wav], cache={}, batch_size=1, language="中文")
+ +

4. Speaker diarization

+

Speaker diarization is not a native Nano checkpoint output. The FunASR pipeline below combines VAD + speaker embedding (cam++) + punctuation for per-sentence speaker labels:

+
model = AutoModel(
+    model="FunAudioLLM/Fun-ASR-Nano-2512",
+    trust_remote_code=True, remote_code="./model.py",
+    vad_model="fsmn-vad", vad_kwargs={"max_single_segment_time": 30000},
+    spk_model="cam++", punc_model="ct-punc",
+    device="cuda:0", hub="hf",
+)
+res = model.generate(input=[wav], cache={}, batch_size=1, language="中文")
+for sent in res[0]["sentence_info"]:
+    print(sent["spk"], sent["text"])
+

Diarization needs FunASR from source: pip install git+https://github.com/modelscope/FunASR.git.

+ +

5. High-throughput / streaming

+ + + + + + + +
ScenarioRecommended
Large-scale offline batchAutoModelVLLM (vLLM backend)
Real-time low latencyFunASRNanoStreamingVLLM (chunk streaming)
Single machine / quick tryThe AutoModel path above
+

The vLLM path is version-sensitive; vLLM 0.12.0 + torch 2.9.0 is recommended. Full examples in the Fun-ASR repo.

+ +
+

Get started with Fun-ASR-Nano

+

Our flagship model for zh/en/ja plus Chinese dialects and accents. Choose MLT-Nano for 31 languages. If it helps, please star it on GitHub ⭐

+Fun-ASR GitHub ★ +
+

Read more: SenseVoice Deployment Guide · Quickstart

+ +

Related posts

+ +
+ + + diff --git a/web-pages/product-site/legacy/en/blog/funasr-cli-transcribe-command-line.html b/web-pages/product-site/legacy/en/blog/funasr-cli-transcribe-command-line.html new file mode 100644 index 0000000000..9b6670a514 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-cli-transcribe-command-line.html @@ -0,0 +1,129 @@ + + +Transcribe Audio from the Command Line with FunASR (text / JSON / SRT) | FunASR + + + + + + + +
+

Transcribe Audio from the Command Line with FunASR (text / JSON / SRT)

+

FunASR Blog · 2026-06-18 · Tutorial

+

Don't want to write Python? FunASR ships a command-line tool that turns audio into text, JSON, or SRT subtitles right in your terminal. Local, free, and especially strong on Chinese. Every command below is tested.

+

Install

+
pip install -U torch torchaudio
+pip install -U funasr   # recommended funasr >= 1.3.26
+

Simplest: print text

+
funasr audio.wav
+

Prints the transcript to the terminal. Defaults to SenseVoice (non-autoregressive, very fast, 50+ languages).

+

Generate SRT subtitles

+
funasr audio.wav -f srt -o ./subs
+

Writes ./subs/audio.srt. Add --spk for speaker-split cues with real timestamps:

+
funasr meeting.wav --spk -f srt -o ./subs
+
1
+00:00:02,919 --> 00:00:08,169
+Hi everyone, let's talk about the Q3 plan.
+
+2
+00:00:10,029 --> 00:00:18,550
+Quick status update - core features are 80% done.
+

Structured JSON

+
funasr audio.wav -f json
+
{
+  "text": "Hi everyone let's talk about the Q3 plan...",
+  "file": "audio.wav",
+  "model": "sensevoice",
+  "language": "auto",
+  "audio_duration_s": 59.52,
+  "processing_s": 2.17
+}
+

Common options

+ + + + + + + +
CommandWhat it does
-f text/json/srt/tsvOutput format (default text)
--spkSpeaker diarization (who spoke when)
--model sensevoice/paraformer/fun-asr-nanoPick a model (fun-asr-nano = highest accuracy)
--hotwords "term,jargon"Hotwords to boost rare terms
-o ./outOutput directory
funasr a.wav b.wavTranscribe multiple files at once
+

Deploy as an API server (OpenAI-compatible)

+
funasr-server --device cuda     # localhost:8000, POST /v1/audio/transcriptions
+

Call it with any OpenAI SDK:

+
from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="x")
+r = client.audio.transcriptions.create(model="sensevoice", file=open("audio.wav","rb"))
+print(r.text)
+

Why the FunASR CLI

+
    +
  • Local, free, private - no internet, no API key.
    • +
  • Fast: SenseVoice is non-autoregressive, far faster than Whisper (benchmark) - real-time even on CPU.
    • +
  • Stronger on Chinese + 50+ languages; subtitles, speakers, hotwords, JSON out of the box.
    • +
+

FunASR is Tongyi Lab's open-source, industrial-grade speech recognition toolkit.

Star FunASR on GitHub ★
+ +

Related posts

+ +
+
FunASR · Open-source speech recognition by Tongyi Lab · GitHub
+ diff --git a/web-pages/product-site/legacy/en/blog/funasr-llama-cpp-whisper-cpp-alternative.html b/web-pages/product-site/legacy/en/blog/funasr-llama-cpp-whisper-cpp-alternative.html new file mode 100644 index 0000000000..e521130099 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-llama-cpp-whisper-cpp-alternative.html @@ -0,0 +1,145 @@ + + + + + +FunASR on llama.cpp — a whisper.cpp Alternative for Chinese ASR (CPU, no Python) | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

FunASR on llama.cpp — a whisper.cpp Alternative for Chinese ASR (CPU, no Python)

+

2026-06-20 · FunASR Team

+ +

whisper.cpp is the de-facto on-device ASR runtime — a single self-contained binary, runs on CPU, zero dependencies. But Whisper is comparatively weak on Chinese. Now FunASR has a llama.cpp / GGUF runtime: the same download-and-run experience — one static binary, no Python, built-in VAD, any audio format — and it is about 2.7× more accurate than whisper.cpp on Chinese CPU.

+ +

👉 Just want the download? Prebuilt binaries for Linux/macOS/Windows plus a one-page quickstart and benchmarks are on the Run FunASR on CPU (llama.cpp) page.

+

Run it in 3 steps (all verified)

+
# 1. Download a prebuilt binary (linux-x64 / linux-arm64 / macos-arm64 / windows-x64)
+wget https://github.com/modelscope/FunASR/releases/download/runtime-llamacpp-v0.1.9/funasr-llamacpp-linux-x64-vulkan.tar.gz
+tar xzf funasr-llamacpp-linux-x64-vulkan.tar.gz
+# Windows Vulkan asset: funasr-llamacpp-windows-x64-vulkan.zip (run with --backend vulkan)
+
+# 2. Download a model with one command (no torch / funasr env)
+bash download-funasr-model.sh sensevoice
+
+# 3. Transcribe — prints text directly
+./llama-funasr-sensevoice -m funasr-gguf/sensevoice-small-f16.gguf \
+    --vad funasr-gguf/fsmn-vad.gguf -a audio.wav
+# -> 开放时间早上九点至下午五点
+

No Python, no build, no dependency hell — download the binary, download the model, run, get text. Exactly the whisper.cpp feel.

+ +

What you get

+ + + + + +
ModelStrengthSpeed (CPU)
SenseVoicemultilingual + language / emotion / event tags~20× real-time
Paraformernon-autoregressive, fastest~22× real-time
Fun-ASR-NanoLLM-ASR, most accurateLLM decode
+
    +
  • Built-in FSMN-VAD: automatic long-audio segmentation (`--vad fsmn-vad.gguf`); the bare binary reaches reference accuracy with no Python front end.
    • +
  • Any audio: wav / mp3 / flac, any sample rate / channels — resampled inside the binary.
    • +
  • Multi-platform prebuilts: Linux x64/arm64, macOS arm64, Windows x64, including Linux/Windows Vulkan and Windows CUDA acceleration packages (Release); one CMake command to build for others.
    • +
+ +

vs whisper.cpp on Chinese CPU

+

184 Mandarin clips, same machine, CPU 8 threads, character error rate (CER, lower is better):

+ + + + + + + + +
SystemCER ↓Size
FunASR SenseVoice8.01 %449 MB
FunASR Paraformer9.85 %401 MB
FunASR Fun-ASR-Nano8.30 %enc + Qwen3-0.6B
whisper.cpp base31.33 %142 MB
whisper.cpp small22.12 %466 MB
whisper.cpp large-v3-turbo23.15 %1.6 GB
+

At comparable size (SenseVoice 449 MB ≈ whisper small 466 MB), FunASR's CER is about 2.7× lower — and faster. Full methodology in the repo's BENCHMARKS.md.

+ +

Why it wins on Chinese

+

(1) Training data — SenseVoice / Paraformer / Fun-ASR-Nano are trained primarily on large-scale Mandarin; Whisper is a general multilingual model where Chinese is a small slice. (2) Architecture — Paraformer is non-autoregressive (CIF, one forward pass) and SenseVoice is encoder + CTC (one forward pass), faster than Whisper's autoregressive per-token decoding.

+ +

Model GGUFs

+

Pre-converted GGUF on Hugging Face: SenseVoiceSmall-GGUF · Paraformer-GGUF · Fun-ASR-Nano-GGUF · fsmn-vad-GGUF. `download-funasr-model.sh` fetches them automatically.

+ +

The whole FunASR stack is open-source — ASR / VAD / punctuation / speaker / emotion & events / LLM-ASR, now with an on-device llama.cpp runtime. A GitHub Star really helps 👇

⭐ Star FunASR

Also star:SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + diff --git a/web-pages/product-site/legacy/en/blog/funasr-realtime-streaming-asr.html b/web-pages/product-site/legacy/en/blog/funasr-realtime-streaming-asr.html new file mode 100644 index 0000000000..c80d01d9a2 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-realtime-streaming-asr.html @@ -0,0 +1,136 @@ + + +Real-Time Streaming Speech-to-Text with FunASR | FunASR + + + + + + + +
+

Real-Time Streaming Speech-to-Text with FunASR

+

FunASR Blog · 2026-06-17 · Tutorial

+

Live captions, voice assistants, meeting transcription — these don't want "record then transcribe", they want text as you speak: words scrolling on screen before the sentence even ends. That is streaming ASR.

+

FunASR's streaming Paraformer decodes with chunks + a cache, delivering real-time text at ~600 ms latency — and it runs on CPU.

+

Install

+
pip install -U funasr modelscope
+

Full, runnable code

+
from funasr import AutoModel
+import soundfile as sf
+
+chunk_size = [0, 10, 5]          # 600 ms chunks (10 * 60 ms center)
+encoder_chunk_look_back = 4      # encoder look-back chunks
+decoder_chunk_look_back = 1      # decoder look-back chunks
+
+model = AutoModel(model="paraformer-zh-streaming")
+
+audio, sr = sf.read("speech.wav", dtype="float32")   # 16 kHz mono
+chunk_stride = chunk_size[1] * 960                    # 600 ms @ 16 kHz
+
+cache = {}
+n_chunks = (len(audio) - 1) // chunk_stride + 1
+for i in range(n_chunks):
+    chunk = audio[i * chunk_stride : (i + 1) * chunk_stride]
+    is_final = i == n_chunks - 1
+    res = model.generate(
+        input=chunk, cache=cache, is_final=is_final,
+        chunk_size=chunk_size,
+        encoder_chunk_look_back=encoder_chunk_look_back,
+        decoder_chunk_look_back=decoder_chunk_look_back,
+    )
+    if res[0]["text"]:
+        print(res[0]["text"], end="", flush=True)   # emit partial text
+

The output grows incrementally (example)

+
Today          # ~600 ms after speech starts
+Today the
+Today the weather
+Today the weather is great    # is_final=True
+

For each 600 ms audio chunk, the model emits the newly recognized text, so the on-screen sentence grows token by token until is_final=True. In testing, a 12 s clip was split into 20 chunks, each returning an incremental result.

+

Key parameters

+ + + + + + +
ParameterMeaning
chunk_size=[0,10,5][left, center, right] chunk sizes in 60 ms units. center 10 → 600 ms per chunk (the latency/accuracy sweet spot)
encoder_chunk_look_back=4History chunks the encoder attends to (more = more context, slightly more latency)
decoder_chunk_look_back=1History chunks for the decoder
cache={}State carried across chunks — reuse the same dict across calls
is_finalSet True on the last chunk to flush the final decoding
+

Best practice: 2-pass (streaming + offline)

+

To keep latency low, the streaming model is slightly less accurate than offline. Production systems often use a 2-pass design:

+
    +
  • Pass 1 (streaming): streaming Paraformer for instant on-screen feedback;
    • +
  • Pass 2 (offline): once a sentence ends (VAD endpointing), re-transcribe the full segment with SenseVoice / offline Paraformer for a more accurate final result that replaces the provisional text.
    • +
+

FunASR's official WebSocket service (funasr-runtime-sdk-online-cpu) ships this 2-pass streaming protocol ready to deploy.

+

Typical use cases

+
    +
  • Live captions: instant subtitles for streams, meetings, classrooms.
    • +
  • Voice assistants: understand while listening, lowering end-to-end latency.
    • +
  • Call-center QA: live transcription + real-time keyword alerts.
    • +
+

FunASR is Tongyi Lab's open-source, industrial-grade speech recognition toolkit — offline + streaming, fast and accurate on Chinese.

Star FunASR on GitHub ★
+ +

Related posts

+ +
+
FunASR · Open-source speech recognition by Tongyi Lab · GitHub
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/funasr-speaker-diarization.html b/web-pages/product-site/legacy/en/blog/funasr-speaker-diarization.html new file mode 100644 index 0000000000..232d9c4fc8 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-speaker-diarization.html @@ -0,0 +1,125 @@ + + +Speaker Diarization with FunASR: Who Spoke When, in One Call | FunASR + + + + + + + +
+

Speaker Diarization with FunASR: Who Spoke When, in One Call

+

FunASR Blog · 2026-06-17 · Tutorial

+

For meeting minutes, call-center QA or interview transcripts, you don't just want to know what was said — you want to know who said each line. That task is called speaker diarization.

+

The usual approach glues two tools together: pyannote.audio for speaker segmentation + Whisper for transcription, then you align the timelines yourself. That means requesting HuggingFace gated access, installing a stack of dependencies, and writing your own alignment logic — and it isn't especially strong on Chinese.

+

With FunASR, a single generate() call is enough. VAD, ASR, punctuation and speaker embedding (CAM++) are chained into one pipeline that returns the speaker id, timestamps and text for every sentence.

+

Install

+
pip install -U funasr modelscope
+

Full code

+
from funasr import AutoModel
+
+# One model handle = VAD + ASR + punctuation + speaker embedding (CAM++)
+model = AutoModel(
+    model="paraformer-zh",   # or "iic/SenseVoiceSmall"
+    vad_model="fsmn-vad",
+    punc_model="ct-punc",
+    spk_model="cam++",       # speaker diarization
+)
+
+res = model.generate(input="meeting.wav", batch_size_s=300)
+
+for s in res[0]["sentence_info"]:
+    print(f'[{s["start"]/1000:.1f}s-{s["end"]/1000:.1f}s] '
+          f'speaker {s["spk"]}: {s["sentence"]}')
+

What the output looks like (example)

+
[0.0s-3.2s] speaker 0: Hi everyone, today's meeting is about the Q3 product plan.
+[3.5s-7.1s] speaker 1: Quick status update — the core features are about 80% done.
+[7.4s-9.9s] speaker 2: QA may need two more weeks on our side.
+[10.2s-13.6s] speaker 0: Got it, let's push the launch back by one week.
+

Each sentence_info item has four fields:

+ + + + +
FieldMeaning
spkSpeaker id, auto-clustered by CAM++ — no need to specify the number of speakers
start / endSentence start/end time (milliseconds)
sentenceSentence text (punctuated)
+

Tested on a real recording

+

On a 227-second multi-speaker meeting recording, FunASR automatically split the audio into 77 sentences and distinguished 11 different speakers — without being told the speaker count in advance. The whole pipeline is one generate() call and a single Python dependency.

+

Why FunASR instead of pyannote + Whisper

+
    +
  • One call, no alignment: transcription and speaker labels are aligned by construction.
    • +
  • No HuggingFace gated access: pyannote's speaker models are gated; FunASR models are openly downloadable.
    • +
  • Stronger on Chinese: Paraformer / SenseVoice have far lower CER than Whisper on Chinese (benchmark).
    • +
  • Runs on CPU: get results offline without a GPU.
    • +
+

Going further

+
    +
  • Swap model="paraformer-zh" for "iic/SenseVoiceSmall" to use SenseVoice (with emotion/event tags).
    • +
  • Iterate over sentence_info to export SRT subtitles or aggregate per speaker into meeting minutes.
    • +
+

FunASR is Tongyi Lab's open-source, industrial-grade speech recognition toolkit — fast and accurate, especially on Chinese.

Star FunASR on GitHub ★
+ +

Related posts

+ +
+
FunASR · Open-source speech recognition by Tongyi Lab · GitHub
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/funasr-transcribe-long-audio.html b/web-pages/product-site/legacy/en/blog/funasr-transcribe-long-audio.html new file mode 100644 index 0000000000..5e133bc7b5 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-transcribe-long-audio.html @@ -0,0 +1,121 @@ + + +Transcribe Long Audio with FunASR: Hours in a Single Call | FunASR + + + + + + + +
+

Transcribe Long Audio with FunASR: Hours in a Single Call

+

FunASR Blog · 2026-06-17 · Tutorial

+

Transcribing a 1-hour podcast, lecture or meeting with Whisper is painful: it processes only 30 seconds at a time, so you have to chunk the audio, transcribe each piece, and stitch the results back together - while handling words cut off at chunk boundaries.

+

FunASR makes this trivial. With built-in VAD (voice activity detection), a single generate() call ingests audio of any length - it segments, batch-decodes and reassembles the full text for you, with zero chunking code.

+

Install

+
pip install -U funasr modelscope
+

Full, runnable code

+
from funasr import AutoModel
+
+model = AutoModel(
+    model="iic/SenseVoiceSmall",   # or "paraformer-zh"
+    vad_model="fsmn-vad",          # built-in voice activity detection
+    vad_kwargs={"max_single_segment_time": 30000},
+)
+
+# One call on a full 1-hour file - VAD segments and batches it internally.
+res = model.generate(
+    input="podcast_1hour.wav",
+    batch_size_s=300,   # dynamic batching of VAD segments (throughput)
+)
+print(res[0]["text"])
+

Tested: 13 minutes transcribed in 4.3s

+
long clip: 791s = 13.2 min
+transcribed 791s in 4.3s -> RTFx=186
+output chars: 2104   # full transcript, head to tail
+

We fed a 13.2-minute (791s) recording into a single generate() call - done in 4.3s (186x realtime), with output covering the full file head to tail. A 1-hour file works exactly the same way, no code changes.

+

Why it handles any length

+
    +
  • VAD auto-segmentation: fsmn-vad splits the long audio into speech segments at pauses; max_single_segment_time caps the longest segment.
    • +
  • Dynamic batching: batch_size_s=300 packs segments by total duration for high-throughput decoding.
    • +
  • Bounded memory: because it processes segments, GPU memory is independent of total file length - 1 hour uses about as much as 1 minute.
    • +
+

Going further

+ +

Typical use cases

+
    +
  • Podcasts / audiobooks: transcribe a whole episode at once.
    • +
  • Meetings / lectures: batch long recordings to text + archive/search.
    • +
  • Call recordings: full-call transcription for QA and mining.
    • +
+

FunASR is Tongyi Lab's open-source, industrial-grade speech recognition toolkit - long audio, streaming and multilingual all covered.

Star FunASR on GitHub ★
+ +

Related posts

+ +
+
FunASR · Open-source speech recognition by Tongyi Lab · GitHub
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/funasr-v1-3-26-openai-vllm-llama-cpp.html b/web-pages/product-site/legacy/en/blog/funasr-v1-3-26-openai-vllm-llama-cpp.html new file mode 100644 index 0000000000..dc32acac31 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-v1-3-26-openai-vllm-llama-cpp.html @@ -0,0 +1,121 @@ + + + + + + FunASR v1.3.26:OpenAI-Compatible API, vLLM, and llama.cpp | FunASR + + + + + + + + + + + + + + + + + + +
+

FunASR v1.3.26:OpenAI-Compatible API, vLLM, and llama.cpp

+

July 23, 2026 · FunASR release guide

+ FunASR industrial speech recognition visual +

FunASR v1.3.26 organizes the same open-source speech stack into three clear entry points:Python and an OpenAI-compatible API, vLLM for high-throughput GPU inference, and Python-free llama.cpp / GGUF runtimes.

+

This is more than a wheel-only release. The Python package, serving examples, GPU inference guide, and eight platform runtime assets target different deployment needs while remaining part of one FunASR ecosystem.

+ +

Choose the path that matches your workload

+ + + + + + + +
GoalRecommended pathStart here
Connect an OpenAI SDK, agent, or workflow/v1/audio/transcriptionsOpenAI-compatible API example
Batch transcription and high-throughput GPU servingFun-ASR-Nano + vLLMvLLM guide
CPU, edge, or zero-Python deploymentllama.cpp / GGUFv0.1.9 runtime assets
+ +

1. Start the OpenAI-compatible transcription service

+

This path uses the same server implementation as the repository smoke tests. Pin the current PyPI release, then start the service:

+ 
git clone https://github.com/modelscope/FunASR.git
+cd FunASR/examples/openai_api
+python -m pip install "funasr==1.3.26" fastapi uvicorn python-multipart
+python server.py --model sensevoice --device cuda --port 8000
+

In another terminal, verify health and transcription with the public sample:

+ 
curl -L https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav -o sample.wav
+curl http://localhost:8000/health
+curl http://localhost:8000/v1/audio/transcriptions \
+  -F file=@sample.wav \
+  -F model=sensevoice \
+  -F response_format=verbose_json
+

If you already use the OpenAI Python client, point base_url to http://localhost:8000/v1. The examples also include model listing, Swagger, Postman, OpenAPI, Gradio, and Kubernetes paths.

+ +

2. Scale Fun-ASR-Nano throughput with vLLM

+

Fun-ASR-Nano runs its audio encoder and adaptor in PyTorch, then sends continuous audio embeddings into the Qwen3 decoder managed by vLLM. The repository benchmark records RTFx 340 for vLLM batch inference while keeping CER close to the PyTorch baseline.

+
The maintainer environment also completed a real-checkpoint smoke on one H100:vLLM 0.19.1 loaded successfully, transcribed the bundled sample, and returned token timestamps. The vLLM guide documents installation, memory, batching, WebSocket, and serving options.
+ 
python -m pip install "vllm==0.19.1"
+python -m pip install "funasr>=1.3.26" safetensors tiktoken websockets
+# Follow docs/vllm_guide.md for offline, streaming, or server mode
+ +

3. Download the self-contained llama.cpp / GGUF runtime

+

runtime-llamacpp-v0.1.9 ships Linux x64/ARM64, macOS ARM64, and Windows x64 packages. Linux includes AVX2 and Vulkan options; Windows includes AVX2, Vulkan, and CUDA, including funasr-llamacpp-windows-x64-vulkan.zip. Each archive provides a self-contained llama-funasr-* binary entry point.

+
    +
  • Choose the standard CPU build when you do not want to maintain Python.
    • +
  • Choose Vulkan or CUDA for desktop GPU acceleration.
    • +
  • Choose ARM64 for edge devices and Apple Silicon.
    • +
+

See the llama.cpp / GGUF page for models, commands, and hardware selection. Runtime assets and model versions are linked together so users do not have to hunt across releases.

+ +

What each repository owns

+ + + + + + + + +
RepositoryWhat to follow
FunASRToolkit, serving, OpenAI API, vLLM, llama.cpp, and deployment docs
Fun-ASRFun-ASR-Nano / MLT models, capabilities, benchmarks, and model integrations
SenseVoiceMultilingual ASR plus emotion, language, and audio-event recognition
FunClipSpeech-driven video understanding, retrieval, and editing
+ +
+

Run one real audio file through the path that fits your deployment. File a reproducible GitHub issue when something blocks you, and star the repository you actually use.

+ Read the v1.3.26 release notes +
+
+ + + diff --git a/web-pages/product-site/legacy/en/blog/funasr-v1-3-27-language-metadata-vllm-fallback.html b/web-pages/product-site/legacy/en/blog/funasr-v1-3-27-language-metadata-vllm-fallback.html new file mode 100644 index 0000000000..9244909e45 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-v1-3-27-language-metadata-vllm-fallback.html @@ -0,0 +1,124 @@ + + + + + + FunASR v1.3.27: Detected Language and Reliable vLLM Fallback | FunASR + + + + + + + + + + + + + + + + + + +
+

FunASR v1.3.27: Detected Language and Reliable vLLM Fallback

+

July 24, 2026 · FunASR weekly release

+ FunASR industrial speech recognition visual +

FunASR v1.3.27 focuses on correctness and reliability in the OpenAI-compatible server: SenseVoice returns the detected language, Fun-ASR-Nano reuses its fallback after vLLM fails, and every server surface reports the installed package version.

+

The Python wheel and source distribution are published together. The same release page also lists nine self-contained llama.cpp / GGUF runtimes, so users do not have to hunt across separate releases.

+ +

What changed

+ + + + + + + + +
Scenariov1.3.27 behaviorWhy it matters
SenseVoice automatic language detectionverbose_json.language returns detected zh/en/yue/ja/koSubtitle, routing, and analytics clients no longer receive a false Chinese default
Fun-ASR-Nano cannot start vLLMThe process reuses its cached AutoModel fallbackLater requests do not construct the same model again
vLLM/VAD setup and fallback both failHalf-initialized state is cleared and a later request can retryA transient setup failure does not leave a falsely loaded service
Version diagnosticsOpenAPI metadata and the startup banner read the package versionLogs and /docs accurately show 1.3.27
+ +

1. Start the OpenAI-compatible transcription service

+

This path uses the same server implementation as the repository smoke tests. Pin the current PyPI release, then start the service:

+ 
git clone https://github.com/modelscope/FunASR.git
+cd FunASR/examples/openai_api
+python -m pip install "funasr==1.3.27" fastapi uvicorn python-multipart
+python server.py --model sensevoice --device cuda --port 8000
+

In another terminal, verify health and transcription with the public sample:

+ 
curl -L https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/BAC009S0764W0121.wav -o sample.wav
+curl http://localhost:8000/health
+curl http://localhost:8000/v1/audio/transcriptions \
+  -F file=@sample.wav \
+  -F model=sensevoice \
+  -F response_format=verbose_json
+

SenseVoice now preserves its raw language tag in the OpenAI-compatible response. An English clip, for example, returns:

+ 
{"task":"transcribe","language":"en","duration":4.6,"text":"hello", "segments":[...]}
+

If you already use the OpenAI Python client, point base_url to http://localhost:8000/v1. The examples also include model listing, Swagger, Postman, OpenAPI, Gradio, and Kubernetes paths.

+ +

2. Scale Fun-ASR-Nano throughput with vLLM

+

Fun-ASR-Nano runs its audio encoder and adaptor in PyTorch, then sends continuous audio embeddings into the Qwen3 decoder managed by vLLM. The repository benchmark records RTFx 340 for vLLM batch inference while keeping CER close to the PyTorch baseline.

+
The maintainer environment also completed a real-checkpoint smoke on one H100:vLLM 0.19.1 loaded successfully, transcribed the bundled sample, and returned token timestamps. The vLLM guide documents installation, memory, batching, WebSocket, and serving options.
+ 
python -m pip install "vllm==0.19.1"
+python -m pip install "funasr>=1.3.27" safetensors tiktoken websockets
+# Follow docs/vllm_guide.md for offline, streaming, or server mode
+ +

3. Download the self-contained llama.cpp / GGUF runtime

+

runtime-llamacpp-v0.1.9 ships nine Linux x64/ARM64, macOS ARM64, and Windows x64 packages. Linux includes AVX2 and Vulkan options; Windows includes AVX2, Vulkan, and CUDA, including funasr-llamacpp-windows-x64-vulkan.zip. Each archive provides a self-contained llama-funasr-* binary entry point.

+
    +
  • Choose the standard CPU build when you do not want to maintain Python.
    • +
  • Choose Vulkan or CUDA for desktop GPU acceleration.
    • +
  • Choose ARM64 for edge devices and Apple Silicon.
    • +
+

See the llama.cpp / GGUF page for models, commands, and hardware selection. Runtime assets and model versions are linked together so users do not have to hunt across releases.

+ +

What each repository owns

+ + + + + + + + +
RepositoryWhat to follow
FunASRToolkit, serving, OpenAI API, vLLM, llama.cpp, and deployment docs
Fun-ASRFun-ASR-Nano / MLT models, capabilities, benchmarks, and model integrations
SenseVoiceMultilingual ASR plus emotion, language, and audio-event recognition
FunClipSpeech-driven video understanding, retrieval, and editing
+ +
+

Run one real audio file through the path that fits your deployment. File a reproducible GitHub issue when something blocks you, and star the repository you actually use.

+ Open the v1.3.27 release and downloads +
+
+ + + diff --git a/web-pages/product-site/legacy/en/blog/funasr-v1-3-28-realtime-websocket-subtitles.html b/web-pages/product-site/legacy/en/blog/funasr-v1-3-28-realtime-websocket-subtitles.html new file mode 100644 index 0000000000..548fc578c9 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-v1-3-28-realtime-websocket-subtitles.html @@ -0,0 +1,113 @@ + + + + + + FunASR v1.3.28: Reliable Realtime ASR and Better Subtitles | FunASR + + + + + + + + + + + + + + + + + + +
+

FunASR v1.3.28: Reliable Realtime ASR and Better Subtitles

+

July 24, 2026 · FunASR weekly release

+ FunASR industrial speech recognition visual +

FunASR v1.3.28 is a focused reliability release for live transcription and subtitle generation. Realtime WebSocket sessions now keep the best continuous transcript when a final decode regresses, while SenseVoice subtitle cues preserve text and timestamp alignment.

+

The signed release includes the Python wheel, source distribution, and nine self-contained llama.cpp / GGUF runtimes on one download page.

+ +

What changed

+ + + + + + + + +
Scenariov1.3.28 behaviorWhy it matters
VAD-locked final decode becomes a short prefix, repetition, or exceptionThe server keeps the clean continuous partial covering the utteranceFinal text no longer loses the end of a live sentence or replaces it with a repeated phrase
Short audio tail followed by STOPThe pending samples use the same final decode pathShort endings are returned instead of being silently dropped
WebSocket disconnect or handler errorConnection exceptions are imported explicitly and unexpected errors reach the generic handlerError handling is stable across supported websockets versions and import order
SenseVoice subtitle segmentationRich tags, punctuation, and word/BPE timestamps stay alignedChinese is not collapsed into one cue and English surface text is preserved
+ +

1. Upgrade the Python package

+

Upgrade both realtime and subtitle deployments to the exact hotfix release:

+ 
python -m pip install -U "funasr==1.3.28"
+funasr --version
+

The package version, command-line version, and server banner all report 1.3.28. Existing model caches and API request formats remain compatible.

+ +

2. Realtime and subtitle paths

+

For live Fun-ASR-Nano sessions, use the packaged realtime entry point and the repository's vLLM guide. Client-driven COMMIT and server VAD modes share the corrected finalization logic.

+ 
funasr-realtime-server --help
+# See docs/vllm_guide.md for model and device options
+
The exact merged source passed 118 focused regression tests. The realtime WebSocket file also passed all 60 tests independently, including STOP final decode and import-order-independent exception handling.
+

SenseVoice users receive the subtitle alignment fix through the same package upgrade; no output-schema migration is required.

+ +

3. Download the self-contained llama.cpp / GGUF runtime

+

runtime-llamacpp-v0.1.9 ships nine Linux x64/ARM64, macOS ARM64, and Windows x64 packages. Linux includes AVX2 and Vulkan options; Windows includes AVX2, Vulkan, and CUDA, including funasr-llamacpp-windows-x64-vulkan.zip. Each archive provides a self-contained llama-funasr-* binary entry point.

+
    +
  • Choose the standard CPU build when you do not want to maintain Python.
    • +
  • Choose Vulkan or CUDA for desktop GPU acceleration.
    • +
  • Choose ARM64 for edge devices and Apple Silicon.
    • +
+

See the llama.cpp / GGUF page for models, commands, and hardware selection. Runtime assets and model versions are linked together so users do not have to hunt across releases.

+ +

What each repository owns

+ + + + + + + + +
RepositoryWhat to follow
FunASRToolkit, serving, OpenAI API, vLLM, llama.cpp, and deployment docs
Fun-ASRFun-ASR-Nano / MLT models, capabilities, benchmarks, and model integrations
SenseVoiceMultilingual ASR plus emotion, language, and audio-event recognition
FunClipSpeech-driven video understanding, retrieval, and editing
+ +
+

Run one real audio file through the path that fits your deployment. File a reproducible GitHub issue when something blocks you, and star the repository you actually use.

+ Open the v1.3.28 release and downloads +
+
+ + + diff --git a/web-pages/product-site/legacy/en/blog/funasr-vs-faster-whisper-chinese.html b/web-pages/product-site/legacy/en/blog/funasr-vs-faster-whisper-chinese.html new file mode 100644 index 0000000000..6b26210d1d --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-vs-faster-whisper-chinese.html @@ -0,0 +1,130 @@ + + + + + +FunASR vs faster-whisper: Chinese & Cantonese Speech Recognition Compared | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

FunASR vs faster-whisper: Chinese & Cantonese Speech Recognition Compared

+

2026-06-21 · FunASR Team

+ +

faster-whisper is the most popular fast Whisper implementation (CTranslate2), and it is strong on English and general multilingual audio. But on Chinese — especially Cantonese and dialects — it has clear gaps. Below is a sentence-level comparison of faster-whisper (small) vs FunASR SenseVoice on the same three clips.

+ +

Side by side (same audio, verbatim)

+ + + + + +
Audiofaster-whisper (small)FunASR SenseVoice
Mandarin开放时间早上九点至下午五点。开放时间早上9点至下午5点。
Cantoneselang mis-detected as zh;
這幾個字都表達不到我想講的意思		correctly detects yue;
呢几个字都表达唔到,我想讲嘅意思
Japaneseうちの中学は弁当性で…うちの中学は弁当制で…
+
    +
  • Cantonese: faster-whisper treats it as Mandarin — detects zh and rewrites spoken Cantonese into standard written Mandarin (dropping Cantonese-specific characters 呢 / 唔到 / 嘅). SenseVoice natively handles Cantonese, detects yue, and keeps the Cantonese form.
    • +
  • Japanese homophone error: faster-whisper hears 弁当性 instead of 弁当制; SenseVoice is correct.
    • +
  • On a simple Mandarin sentence both are correct — the gap shows on hard samples, dialects, and proper nouns.
    • +
+ +

Overall accuracy (184 Mandarin clips)

+

Same machine, CPU, character error rate (lower is better): FunASR SenseVoice 8.0% / Paraformer 9.9% / Fun-ASR-Nano 8.3%; Whisper-class models (small/base/large-v3-turbo) ~22–31%. On Chinese, FunASR's CER is ~2.7× lower — due to large-scale Mandarin training data and a non-autoregressive architecture (also faster). Full methodology in BENCHMARKS.md.

+ +

What else FunASR gives you

+
    +
  • Language ID: zh / en / yue (Cantonese) / ja / ko (faster-whisper labels Cantonese as zh)
    • +
  • Emotion + audio events: HAPPY/SAD/ANGRY, BGM/applause/laughter (details)
    • +
  • Non-autoregressive = faster: SenseVoice/Paraformer single forward pass, ~20× real-time on CPU
    • +
  • On-device: a llama.cpp/GGUF runtime, single binary, zero Python
    • +
+ +

Which should you use?

+

Honestly: faster-whisper is still excellent for English, general 99-language coverage, and translation, with a mature ecosystem. But if your audio is Chinese, Cantonese, dialects, or you need emotion / event / language info, FunASR is more accurate and more complete. Both are open-source and run locally.

+ +

Try FunASR in 3 lines

+
pip install funasr
+from funasr import AutoModel
+m = AutoModel(model="iic/SenseVoiceSmall")
+print(m.generate(input="audio.wav", language="auto", use_itn=True)[0]["text"])
+ +

The whole FunASR stack is open-source — ASR / VAD / punctuation / speaker / emotion & events / LLM-ASR / on-device llama.cpp. A GitHub Star helps 👇

⭐ Star FunASR

Also star:SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/funasr-vs-whisper-benchmark.html b/web-pages/product-site/legacy/en/blog/funasr-vs-whisper-benchmark.html new file mode 100644 index 0000000000..013339fb5e --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funasr-vs-whisper-benchmark.html @@ -0,0 +1,95 @@ + + +FunASR vs Whisper: Real Chinese ASR Benchmark | FunASR Blog + + + + + + + +
+

FunASR vs Whisper: A Real Benchmark on Chinese Speech Recognition

+

FunASR Blog · 2026-06-16 · Benchmark

+

We benchmarked the FunASR model family against OpenAI Whisper on 184 long-form Chinese audio files (11,539s ≈ 192 min) on a single NVIDIA H100, measuring speed (RTFx) and accuracy (CER). The takeaway: for Chinese, FunASR is both faster and more accurate.

+ + + + + + + +
ModelDeviceRTFx(higher=faster)CER(lower=better)
SenseVoice-SmallGPU169.6x7.81%
Paraformer-LargeGPU119.6x10.18%
Fun-ASR-NanoGPU340x (vLLM)8.20%
Whisper-large-v3-turboGPU46.1x21.71%
Whisper-large-v3GPU13.4x20.02%
SenseVoice-SmallCPU17.2x7.81%
+

Speed

+

SenseVoice-Small reaches 169.6x realtime — about 12x faster than Whisper-large-v3 (13.4x). Even on CPU, SenseVoice hits 17.2x, faster than Whisper on GPU. Paraformer-Large does 119.6x.

+

Accuracy

+

Chinese CER: SenseVoice 7.81%, Paraformer 10.18%, versus Whisper-large-v3 at 20.02% (turbo 21.71%) — roughly half the error rate or better.

+

Why FunASR is faster

+

SenseVoice / Paraformer are non-autoregressive: one forward pass yields the full transcript, unlike Whisper's token-by-token autoregressive decoding. Combined with training data tuned for Chinese and Asian languages, FunASR wins on both speed and accuracy in Chinese.

+

Get started with FunASR

Open-source, commercial-friendly, CPU/GPU. If it helps, star it ⭐

FunASR GitHub ★
+

Read more: SenseVoice Guide · Fun-ASR-Nano Guide

+

Setup: 184 files / 11,539s Chinese audio, NVIDIA H100. RTF=infer time/audio duration, Speed=1/RTF, CER computed after punctuation removal.

+ +

Related posts

+ +
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/funclip-v2-1-0-video-clipping-release.html b/web-pages/product-site/legacy/en/blog/funclip-v2-1-0-video-clipping-release.html new file mode 100644 index 0000000000..0fb0bf84ac --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/funclip-v2-1-0-video-clipping-release.html @@ -0,0 +1,118 @@ + + + + + + FunClip v2.1.0: Local AI Video Clipping with FunASR | FunASR + + + + + + + + + + + + + + + + + + +
+

FunClip v2.1.0: The First Versioned Local AI Video-Clipping Release

+

July 24, 2026 · FunClip Release

+ FunClip local video input, subtitle recognition, and LLM-assisted clipping interface +

FunClip v2.1.0 is the project's first versioned GitHub release. It turns the current local video-clipping application into a downloadable, verifiable, rollback-ready snapshot: transcribe video with the FunASR model family, then cut by transcript, speaker, or LLM-selected segments.

+

You can run the public Space first or download the source for private local deployment. Videos, audio, and model weights stay outside the release archives.

+ +

Choose the ASR path that fits the edit

+ + + + + + + + +
PathBest forLaunch command
ParaformerPrecise timestamps, transcript or speaker clipping, and Chinese hotwordspython funclip/launch.py
Fun-ASR-NanoHigh-accuracy Mandarin, English, Japanese, Chinese dialect, and accent transcription; use Paraformer for precise character-level clippingpython funclip/launch.py -m fun-asr-nano
SenseVoiceMultilingual ASR with emotion and audio-event tagspython funclip/launch.py -m sensevoice
English ParaformerEnglish recognition with timestamp-based clippingpython funclip/launch.py -l en
+ +

1. Try the public application first

+

The FunClip Hugging Face Space runs the same public application. Use it to validate video upload, ASR, subtitle selection, and AI clipping before moving private media or pinned dependencies to a local deployment.

+ +

2. Download and verify v2.1.0

+

Use the tar.gz archive on Linux and macOS, or the zip archive on Windows and general-purpose extraction workflows. The checksum command below verifies only archives present in the current directory:

+ 
curl -LO https://github.com/modelscope/FunClip/releases/download/v2.1.0/FunClip-2.1.0.tar.gz
+curl -LO https://github.com/modelscope/FunClip/releases/download/v2.1.0/SHA256SUMS
+sha256sum -c SHA256SUMS --ignore-missing
+
+tar -xzf FunClip-2.1.0.tar.gz
+cd FunClip-2.1.0
+python -m pip install -r requirements.txt
+python -m pip install -U "funasr>=1.3.29"
+python funclip/launch.py
+

On Windows, run Get-FileHash -Algorithm SHA256 FunClip-2.1.0.zip and compare the result with SHA256SUMS.

+ +

3. From transcript clipping to content-aware selection

+

The default workflow turns a video into timestamped subtitles and lets you cut by transcript or speaker. LLM-assisted clipping sends subtitles plus your prompt to a configured provider and returns segments that the existing clip action can consume directly.

+
    +
  • Use a supported transcript-driven LLM provider when subtitles contain the signal you need.
    • +
  • Use optional TwelveLabs Pegasus when action, scene changes, or on-screen events matter more than the transcript; Pegasus reasons over video and audio together.
    • +
  • Use direct transcript or speaker selection when a human needs exact control over the output video and SRT cues.
    • +
+ +

Why these are source archives, not a wheel

+

FunClip is a runnable application, not a declared Python library package; the repository has no wheel-producing setup.py or pyproject.toml. v2.1.0 therefore ships two honest application snapshots and one checksum manifest:

+ +

The archives contain tracked application source, documentation, and the dependency manifest. They do not bundle runtime dependencies or model weights, so every model keeps its own license and distribution boundary.

+ +
The exact v2.1.0 merge passed 50 tests with 1 skipped. Six release-contract tests also passed in a clean Python 3.12 environment, covering reproducible archives, cross-filesystem output, invalid-ref rejection, and idempotent publication. Both the initial GitHub Actions release and a deliberate rerun succeeded; the rerun verified the published assets and made no destructive changes.
+ +

Version relationship with FunASR 1.3.29

+

FunClip v2.1.0 requires funasr>=1.3.29. When SenseVoice has no token timestamps, that release exposes every VAD speech region through sentence_info, giving subtitle and clipping clients real segment boundaries. It also includes the realtime final-text and short-tail fixes from 1.3.28. See the FunASR v1.3.29 release for details.

+ +
+

Run one video in the Space or deploy the pinned version on your own machine. File a reproducible issue when something blocks you, and star the FunClip repository if it becomes part of your workflow.

+ Open the FunClip v2.1.0 release and downloads +
+
+ + + diff --git a/web-pages/product-site/legacy/en/blog/generate-subtitles-srt-vtt-from-audio-video.html b/web-pages/product-site/legacy/en/blog/generate-subtitles-srt-vtt-from-audio-video.html new file mode 100644 index 0000000000..56eafb71bb --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/generate-subtitles-srt-vtt-from-audio-video.html @@ -0,0 +1,134 @@ + + +Auto-Generate Subtitles (SRT & VTT) from Audio or Video with FunASR | FunASR + + + + + + + +
+

Auto-Generate Subtitles (SRT & VTT) from Audio or Video with FunASR

+

FunASR Blog · 2026-06-18 · Tutorial

+

You don't need a cloud service or a paid API to subtitle a video. FunASR turns speech into timestamped SRT or VTT subtitles locally and for free, and can even label speakers. It's especially strong on Chinese and supports 50+ languages. Every command and snippet below is tested.

+ +

Step 1 (video): extract the audio

+

If your source is a video, pull out 16 kHz mono audio (FunASR's standard input) with ffmpeg:

+
ffmpeg -i video.mp4 -ar 16000 -ac 1 audio.wav
+

For audio files (wav/mp3/m4a, ...) skip this step.

+ +

Fastest: one command to SRT

+
pip install -U funasr
+funasr audio.wav -f srt -o ./subs
+

Writes a standard SRT file at ./subs/audio.srt with real, usable timestamps:

+
1
+00:00:00,000 --> 00:00:05,546
+欢迎大家来体验达摩院推出的语音识别模型   (the Chinese sample)
+

Add --spk to split cues by speaker (great for interviews and meetings):

+
funasr meeting.wav --spk -f srt -o ./subs
+ +

Python: emit SRT and VTT together (with speakers)

+

When you need more control — e.g. a web-friendly VTT as well, or a custom speaker prefix — build subtitles directly from sentence_info in the result:

+
from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", spk_model="cam++", device="cuda")
+res = model.generate(input="audio.wav")
+segments = res[0]["sentence_info"]   # each has start/end (ms), spk, sentence
+
+def ts(ms, sep):                     # ms -> HH:MM:SS,mmm  (SRT uses "," / VTT uses ".")
+    h, mm, ss, mmm = ms//3600000, (ms%3600000)//60000, (ms%60000)//1000, ms%1000
+    return "%02d:%02d:%02d%s%03d" % (h, mm, ss, sep, mmm)
+
+with open("subs.srt", "w", encoding="utf-8") as f:
+    for i, s in enumerate(segments):
+        text = rich_transcription_postprocess(s["sentence"])
+        f.write("%d\n%s --> %s\nSpeaker %s: %s\n\n" % (i+1, ts(s["start"], ","), ts(s["end"], ","), s["spk"], text))
+
+with open("subs.vtt", "w", encoding="utf-8") as f:
+    f.write("WEBVTT\n\n")
+    for s in segments:
+        text = rich_transcription_postprocess(s["sentence"])
+        f.write("%s --> %s\n<v Speaker %s>%s\n\n" % (ts(s["start"], "."), ts(s["end"], "."), s["spk"], text))
+

Tested output (SRT):

+
1
+00:00:00,610 --> 00:00:05,530
+Speaker 0: 欢迎大家来体验达摩院推出的语音识别模型
+

Always run rich_transcription_postprocess to strip SenseVoice tags like <|zh|>, otherwise they leak into your subtitles.

+ +

Last (optional): burn subtitles into the video

+
ffmpeg -i video.mp4 -vf subtitles=subs.srt output.mp4
+ +

Why use FunASR for subtitles

+
    +
  • Local, free, private — no uploads, no API key, no per-minute fees.
    • +
  • Fast: SenseVoice is non-autoregressive, far faster than Whisper (benchmark) — long videos subtitle quickly.
    • +
  • Stronger on Chinese + 50+ languages; built-in VAD segmentation, speaker labels, and real timestamps.
    • +
+

FunASR is Tongyi Lab's open-source, industrial-grade speech recognition toolkit.

Star FunASR on GitHub ★
+ +

Related posts

+ +
+
FunASR · Open-source speech recognition by Tongyi Lab · GitHub
+ \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/index.html b/web-pages/product-site/legacy/en/blog/index.html new file mode 100644 index 0000000000..92427630f6 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/index.html @@ -0,0 +1,54 @@ + + +FunASR Blog — ASR Deployment, Benchmarks & Tutorials + + + + + + + +
July 24, 2026 · New release

FunClip v2.1.0: The First Versioned Local AI Video-Clipping Release

Paraformer, Fun-ASR-Nano, SenseVoice, and LLM clipping in verifiable, rollback-ready source downloads.

Read the release guide →
+
July 24, 2026

FunASR v1.3.28: Reliable Realtime ASR and Better Subtitles

Continuous realtime finals, reliable STOP and disconnect handling, aligned SenseVoice subtitles, and eleven direct downloads.

+

FunASR Blog

Speech recognition deployment, tutorials and best practices

2026-06-23

Speech-to-Text with Word/Character-Level Timestamps in Python

FunASR Paraformer gives native character timestamps: every char has [start_ms, end_ms] in one call. Build word highlighting, click-to-seek, subtitle alignment. Real output + pairing code.

2026-06-23

Punctuation Restoration in Python — Add Punctuation to Text / ASR Output

FunASR ct-punc: open-source bilingual (zh+en) punctuation restoration. 3 lines of Python to add ，。？ (and capitalize English), or attach to ASR in one line. Real output.

2026-06-22

Chinese (Mandarin) Speech Recognition in Python — Fast & Accurate with FunASR

Purpose-built for Chinese: default flagship Fun-ASR-Nano (CER 8.06%), with SenseVoice (7.81%) / Paraformer (10.18%, timestamps/hotwords) for CPU — all far better than Whisper (~20%).

2026-06-22

Self-Hosted Speech-to-Text — Free Alternative to Google / AWS / Azure Cloud Speech APIs

Open-source (MIT), local inference, no per-minute billing, audio stays on your network, strong Chinese, OpenAI-compatible (migrate via base_url). Runnable code + FunASR vs cloud comparison + cost analysis.

2026-06-22

Voice Activity Detection in Python — Detect Speech, Remove Silence, Split Audio

FunASR fsmn-vad returns millisecond speech spans in 3 lines. Measured: a 13s clip split into 2 segments in 0.12s, 18% silence removed. Trim silence, split long audio, preprocess Whisper to cut hallucinations.

2026-06-22

Japanese Speech Recognition in Python — SenseVoice: Transcription + Punctuation + Emotion in One Pass

Real side-by-side on one Japanese clip: SenseVoice writes 転売 correctly and adds punctuation; Whisper-small gives 天売 with none. Native ja support + auto language ID + emotion/events, 3 lines of Python.

2026-06-23

Self-Hosted Deepgram / AssemblyAI Alternative

Open-source, free, self-hosted, audio stays local, Chinese-leading, with an OpenAI-compatible API — replace per-minute cloud STT by changing base_url.

2026-06-23

Which FunASR Model? Nano vs MLT-Nano vs SenseVoice vs Paraformer

A decision table with code: flagship Nano for zh/en/ja plus Chinese dialects/accents, separate MLT-Nano for 31 languages, and CPU choices SenseVoice or Paraformer.

2026-06-22

Lightweight Speech Recognition: Chinese ASR in ~250MB on CPU

One binary + a 254MB q8 model, no GPU/Python, 0.16s on CPU, 7.99% CER — smaller than whisper.cpp small and ~3x more accurate.

2026-06-21

Cantonese Speech Recognition in Python — SenseVoice Keeps Real Cantonese (Whisper Doesn't)

Real side-by-side on one Cantonese clip: SenseVoice keeps 呢/唔/嘅, Whisper rewrites to Mandarin. Native yue support + auto language ID, 3 lines of Python.

2026-06-21

FunASR vs faster-whisper: Chinese & Cantonese Compared

faster-whisper mislabels Cantonese as Mandarin + Japanese homophone errors; SenseVoice handles Cantonese natively, ~2.7x lower CER on Chinese.

2026-06-20

FunASR on llama.cpp — a whisper.cpp Alternative for Chinese ASR (CPU, no Python)

One self-contained binary, built-in VAD, any audio — download and transcribe Chinese; ~2.7x more accurate than whisper.cpp on CPU.

2026-06-18

Speech to Text in Python with FunASR

Transcribe audio in a few lines of Python — timestamps, speakers, batching. Local, free, no API key.

2026-06-18

Auto-Generate Subtitles (SRT & VTT) from Audio or Video with FunASR

One command for SRT, Python for VTT too, with speaker labels and real timestamps. Local, free, strong on Chinese.

2026-06-18

Self-Hosted OpenAI Whisper API Alternative with FunASR

funasr-server exposes an OpenAI-compatible /v1/audio/transcriptions; the OpenAI SDK works by changing only base_url. Local, free, private.

2026-06-18

Transcribe Audio from the Command Line with FunASR

One command -> text/SRT/JSON, --spk for speakers; plus funasr-server for an OpenAI-compatible API.

2026-06-17

Transcribe Long Audio with FunASR: Hours in One Call

Whisper caps at 30s; FunASR ingests any length via built-in VAD - 13 min in 4.3s (186x).

2026-06-17

Real-Time Streaming Speech-to-Text with FunASR

~600ms low-latency streaming ASR with chunks + cache, plus the 2-pass (streaming + offline) best practice.

2026-06-17

Beyond Transcription: Language, Emotion & Audio Events with SenseVoice

Transcription + language + emotion + audio events in one pass — the 4-in-1 Whisper cannot do.

2026-06-17

Speaker Diarization with FunASR: Who Spoke When

Transcription + speaker labels + timestamps in one generate() call. A pyannote+Whisper alternative, no HF gated access.

2026-06-16

FunASR vs Whisper: Real Chinese ASR Benchmark

Measured on 184 Chinese files (H100): SenseVoice 169.6x, 7.81% CER — full speed & accuracy data.

2026-06-16

Fun-ASR-Nano Guide: 800M End-to-End ASR LLM

Flagship for zh/en/ja plus 7 Chinese dialect groups and 26 accents; choose MLT-Nano for 31 languages.

2026-06-16

SenseVoice Deployment Guide: 15x Faster Than Whisper

Multilingual ASR in 3 lines, with language/emotion/event tags, VAD, GPU/CPU.

More: Quickstart · Models

+ diff --git a/web-pages/product-site/legacy/en/blog/japanese-speech-recognition.html b/web-pages/product-site/legacy/en/blog/japanese-speech-recognition.html new file mode 100644 index 0000000000..81284b40f0 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/japanese-speech-recognition.html @@ -0,0 +1,144 @@ + + + + + +Japanese Speech Recognition in Python — SenseVoice: Transcription + Punctuation + Emotion in One Pass | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Japanese Speech Recognition in Python — SenseVoice Does Transcription + Punctuation + Emotion in One Pass (vs Whisper)

+

2026-06-22 · FunASR Team

+ +

For Japanese speech-to-text, most people reach for Whisper. But if you want something that runs on CPU, is fast and accurate, and ships punctuation and emotion out of the box, SenseVoice (an open-source multilingual speech-understanding model from the FunAudioLLM team) is an underrated option: it natively supports Japanese (ja), auto-detects the language, and produces punctuated text in a single non-autoregressive pass — plus emotion and audio-event tags for free.

+

Below is a real side-by-side on the same Japanese clip between SenseVoice and the same-tier Whisper-small (both run on our server, nothing cherry-picked).

+ +

Real comparison: the same Japanese audio

+

The test clip is a piece of colloquial Japanese speech. Actual outputs from both models:

+ + + + +
ModelDetected languageOutput
SenseVoiceauto → ja供給量が減る と ある程度は仕方ないんじゃね、転売の価格は論外だけど。
Whisper-smallja供給量が減るとある程度は仕方ないんじゃね天売の価格は論外だけど
+

The key difference is the word 転売:

+
    +
  • SenseVoice writes 転売 correctly (tenbai, "resale / scalping") — which is exactly what the sentence is about (talking about resale prices).
    • +
  • Whisper-small produces the homophone 天売 — same reading (tenbai), but not a real word: a classic Japanese homophone/wrong-kanji error.
    • +
  • SenseVoice also adds punctuation (、 。) automatically; the Whisper-small output has none.
    • +
+

To be fair about the setup: this compares same-tier Whisper-small (a common CPU-friendly choice). A larger Whisper-large would usually fix this kind of homophone, but it is far bigger and slower; SenseVoice gets the word right at a small-model footprint, and throws in punctuation and emotion on top.

+ +

Japanese ASR in three lines

+
pip install funasr
+
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)
+res = model.generate(input="japanese.wav", language="auto", use_itn=True)
+
+print(rich_transcription_postprocess(res[0]["text"]))
+# 供給量が減る と ある程度は仕方ないんじゃね、転売の価格は論外だけど。
+

Set language to "ja" explicitly, or use "auto" as above for automatic language ID — on this clip auto correctly resolves to ja with output identical to the explicit setting.

+ +

What else the raw output carries

+

SenseVoice's raw output begins with a set of tags:

+
<|ja|><|NEUTRAL|><|Speech|><|withitn|>供給量が減る と ある程度は仕方ないんじゃね、転売の価格は論外だけど。
+

Meaning: <|ja|> = language is Japanese, <|NEUTRAL|> = emotion (neutral), <|Speech|> = audio event (clean speech; it can also flag BGM/laughter/applause), <|withitn|> = inverse text normalization applied. So while transcribing Japanese you also get language, emotion and audio events for free. One call to rich_transcription_postprocess() strips the tags to plain text.

+ +

Why SenseVoice is worth a try for Japanese

+ + + + + + + + + +
SenseVoiceWhisper-small
Japanese homophone (転売)✅ correct❌ wrong (天売)
Automatic punctuation✅ built-in❌ none
Emotion / audio events✅ in one pass❌
Inverse text normalization✅ built-inpartial
Speednon-autoregressive, measured RTF≈0.04 (GPU)autoregressive, slower
Multilingualone model for 50+ languages (ja/ko/zh/yue/en…)multilingual
Licenseopen-source, commercial-friendlyopen-source
+

If your use case mixes Japanese + Chinese + English (cross-border support, subtitles, meetings), one SenseVoice model covers all of them and bundles emotion analysis. For its emotion/event detection, see SenseVoice emotion & audio event detection; to pick a model, see the FunASR model selection guide; and for Chinese accuracy, the FunASR vs Whisper benchmark.

+ +

The whole FunASR stack is open-source — industrial-grade ASR / VAD / punctuation / speaker / emotion & events / LLM-ASR, with Japanese and 50+ languages out of the box. If it helps, a GitHub Star really supports the project 👇

⭐ Star SenseVoice

Also star:FunASR · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/lightweight-speech-recognition-cpu.html b/web-pages/product-site/legacy/en/blog/lightweight-speech-recognition-cpu.html new file mode 100644 index 0000000000..51ff5e0709 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/lightweight-speech-recognition-cpu.html @@ -0,0 +1,116 @@ + + +Lightweight Speech Recognition: Chinese ASR in ~250MB on CPU (no GPU/Python) | FunASR Blog + + + + + + + +
+

Lightweight speech-to-text: Chinese ASR in ~250 MB on CPU (no GPU, no Python)

+

2026-06-22 · FunASR Team

+

Most speech recognition wants a GPU, a Python environment, and multi-GB models. If you just need to turn Chinese speech into text on a laptop, a small server, or an edge device, that's a lot of overhead.

+

FunASR's llama.cpp / GGUF runtime strips it down to the minimum: **one self-contained binary + one quantized model file**, pure CPU, zero Python. The q8 build of SenseVoice is just **254 MB** with virtually unchanged accuracy.

+ +

Tested: a 254 MB model, 0.16 s on CPU (real output)

+

Grab a prebuilt binary (Linux/macOS/Windows) and a q8 model, then run:

+
# binaries are on Releases; then fetch a model
+bash download-funasr-model.sh sensevoice ./gguf
+llama-funasr-sensevoice -m ./gguf/sensevoice-small-q8.gguf --vad ./gguf/fsmn-vad.gguf -a audio.wav
+# → 欢迎大家来体验达摩院推出的语音识别模型。   (CPU, 0.16 s)
+

The model is 243–254 MB, the VAD just 1.7 MB, and detokenization is built into the binary (no Python).

+ +

Small and accurate: vs whisper.cpp

+

Both on CPU (Chinese, 184-file micro-CER, lower is better):

+ + + + + + +
ModelSizeChinese CER ↓
FunASR SenseVoice q8254 MB7.99%
FunASR Paraformer q8237 MB9.78%
whisper.cpp small466 MB22.12%
whisper.cpp large-v3-turbo1.6 GB23.15%
+

FunASR's q8 model is smaller than whisper.cpp small yet ~3× more accurate on Chinese.

+ +

Even smaller: the quant matrix

+ + + + + +
ModelTypeSizeCER
SenseVoice encoderq8254 MB7.99%
Paraformer encoderq8237 MB9.78%
Fun-ASR-Nano LLM (+ encoder 470 MB)q4_K_M484 MB8.35%
+

Prebuilt binaries for Linux x64 / arm64, macOS arm64, Windows x64 — the arm64 build suits Raspberry Pi and edge boxes.

+ +

Where to get it

+ +

FunASR is fully open-source & commercial-friendly. A GitHub Star really helps 👇

⭐ Star FunASR

Also star: SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/punctuation-restoration-python.html b/web-pages/product-site/legacy/en/blog/punctuation-restoration-python.html new file mode 100644 index 0000000000..fd34f6b01d --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/punctuation-restoration-python.html @@ -0,0 +1,136 @@ + + + + + +Punctuation Restoration in Python — Add Punctuation to Text / ASR Output | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Punctuation Restoration in Python — Add Punctuation to Unpunctuated Text / ASR Output

+

2026-06-23 · FunASR Team

+ +

Many speech recognition (ASR) models output unpunctuated text — one long run of words that is hard to read. Punctuation restoration adds the commas, periods, and question marks back so the transcript is readable. FunASR's ct-punc is an open-source punctuation-restoration model that works for both Chinese and English, adding punctuation to any text in three lines of Python. Everything below is real measured output.

+ +

Punctuation restoration in three lines (real output)

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="ct-punc", disable_update=True)
+print(model.generate(input="the meeting is at 3 pm please bring your laptop and the report")[0]["text"])
+# The meeting is at 3 pm, please bring your laptop and the report.
+

The input is a continuous string with no punctuation; the output gets a comma, a period, and a capitalized first letter.

+ +

Real Chinese & English examples

+ + + + + +
Input (no punctuation)Output (ct-punc)
the meeting is at 3 pm please bring your laptop and the reportThe meeting is at 3 pm, please bring your laptop and the report.
我们都是木头人不许说话不许动我们都是木头人，不许说话，不许动。
今天天气怎么样我想出去走走你要一起吗今天天气怎么样，我想出去走走，你要一起吗？
+

So: English gets commas, periods, and a capitalized first letter; Chinese gets ，。？ — and it even picks a question mark for questions. One model handles both languages.

+ +

The most common use: punctuate ASR output

+

The biggest use of punctuation restoration is cleaning up speech-to-text output. FunASR's ASR models let you attach ct-punc in one line so the transcript comes out punctuated:

+
from funasr import AutoModel
+
+# ASR + VAD + punctuation in one call (Chinese Paraformer here)
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc")
+result = model.generate(input="audio.wav")
+print(result[0]["text"])   # output already has punctuation
+

You can also use ct-punc standalone on any text — not just speech. Old subtitles, OCR output, chat logs: anything unpunctuated can be punctuated.

+ +

Why ct-punc

+ + + + + + +
ct-punc (FunASR)
LanguagesChinese + English (one bilingual model)
Usage3 lines of Python on raw text; or attach to ASR in one line
Punctuation，。？ etc.; also capitalizes the first letter in English
Licenseopen-source, commercial-friendly
+

For full speech pipelines, see Chinese speech recognition, VAD / silence removal, and speaker diarization; to pick an ASR model, see the model selection guide (default recommendation: the flagship Fun-ASR-Nano).

+ +

The whole FunASR stack is open-source (MIT) — punctuation restoration, ASR, VAD, speaker, emotion, LLM-ASR (flagship Fun-ASR-Nano), ready to use. If it helps, a GitHub Star supports the project 👇

⭐ Star FunASR

Also star:SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/self-hosted-deepgram-assemblyai-alternative.html b/web-pages/product-site/legacy/en/blog/self-hosted-deepgram-assemblyai-alternative.html new file mode 100644 index 0000000000..643d0bbafa --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/self-hosted-deepgram-assemblyai-alternative.html @@ -0,0 +1,106 @@ + + +Self-Hosted Deepgram / AssemblyAI Alternative: Open-Source Speech-to-Text API | FunASR Blog + + + + + + +
+

Self-Hosted Alternative to Deepgram & AssemblyAI: Open-Source, Free, Data Stays Local

+

2026-06-23 · FunASR Team

+

Cloud speech-to-text APIs like Deepgram, AssemblyAI and Google/Azure Speech bill per minute and require uploading your audio to a third party. If you want to self-host, pay no per-minute fees, and keep audio on your own machines — especially for Chinese — FunASR is a direct open-source alternative with a built-in OpenAI-compatible transcription API: clients just change the base URL.

+ +

Comparison

+ + + + + + + + +
FunASR (self-hosted)Deepgram / AssemblyAI
PriceFree (MIT), no per-minute billingPay per minute
DeploymentSelf-hosted (local / private cloud / edge)Cloud API (managed)
Data privacyAudio never leaves your machineUploaded to vendor
Chinese / CantoneseIndustry-leadingEnglish-first
InterfaceOpenAI-compatible (drop-in)Proprietary SDKs
Offline / CPU✅ (incl. llama.cpp single binary)❌ cloud only
+

Cloud APIs win on zero-ops, elastic scaling, and mature English/value-added features; FunASR wins on cost, self-hosting, privacy, and Chinese. Choose by your needs.

+ +

Run an OpenAI-compatible local STT server

+
pip install funasr
+funasr-server --model sensevoice --device cuda      # or --device cpu
+# → POST http://localhost:8000/v1/audio/transcriptions
+

Then any OpenAI client just changes base_url — no business-code changes:

+
from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
+text = client.audio.transcriptions.create(model="sensevoice", file=open("audio.wav","rb")).text
+

Migrating from Deepgram/AssemblyAI or the OpenAI Whisper API: point requests at your own funasr-server — usage bills go to zero and audio stays local.

+ +

Which model?

+

Use SenseVoice for multilingual + emotion; Paraformer for Chinese + timestamps/hotwords; flagship Fun-ASR-Nano for zh/en/ja plus Chinese dialects/accents; and the separate Fun-ASR-MLT-Nano for 31 languages. See the model selection guide.

+

FunASR is open-source, commercial-friendly, and self-hostable. A Star really helps 👇

⭐ Star FunASR

Also: SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + diff --git a/web-pages/product-site/legacy/en/blog/self-hosted-google-aws-azure-speech-to-text.html b/web-pages/product-site/legacy/en/blog/self-hosted-google-aws-azure-speech-to-text.html new file mode 100644 index 0000000000..cded7593e0 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/self-hosted-google-aws-azure-speech-to-text.html @@ -0,0 +1,147 @@ + + + + + +Self-Hosted Speech-to-Text — Free Alternative to Google / AWS / Azure Cloud Speech APIs | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Self-Hosted Speech-to-Text — A Free, Open-Source Alternative to Google / AWS / Azure Cloud Speech APIs

+

2026-06-22 · FunASR Team

+ +

Cloud speech APIs like Google Cloud Speech-to-Text, AWS Transcribe, and Azure Speech are convenient — fully managed, auto-scaling, zero ops. But as volume grows or compliance matters, their downsides show: per-minute billing (expensive at scale), audio is uploaded to the vendor's cloud (privacy / compliance / data-residency), rate limits, internet dependency, and limited customization.

+

FunASR (open-sourced by Tongyi Lab) is a mature self-hosted alternative: open-source and free (MIT, commercial-friendly), runs on your own machine (fully offline-capable), no per-minute billing, and your audio never leaves your network. It's especially strong on Chinese and Asian languages, and ships an OpenAI-compatible API so migration is nearly free. Here's runnable code.

+ +

Transcribe locally in 4 lines (real output)

+
pip install funasr
+
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)  # uses CPU if no GPU
+res = model.generate(input="audio.wav", language="auto", use_itn=True)
+
+print(rich_transcription_postprocess(res[0]["text"]))
+# 欢迎大家来体验达摩院推出的语音识别模型。  (output for a Chinese sample)
+

The model downloads on first run, then everything is local inference — no API key, no per-minute bill, no audio uploaded anywhere.

+ +

Already on a cloud API? Migrate by changing the base_url

+

FunASR ships an OpenAI-compatible transcription server, so if your app already calls a cloud SDK you often only point base_url at your own service:

+
# start a local server (OpenAI-compatible /v1/audio/transcriptions)
+funasr-server --model sensevoice --device cuda
+
+# client: the OpenAI SDK, just change base_url to your server
+from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
+text = client.audio.transcriptions.create(model="sensevoice", file=open("audio.wav","rb")).text
+ +

FunASR vs cloud speech APIs

+ + + + + + + + + + + +
FunASR (self-hosted)Cloud STT (Google/AWS/Azure)
Pricingopen-source & free; fixed machine cost onlyper-minute, scales linearly with usage
Datastays on your servers (offline-capable)audio uploaded to the vendor cloud
Deploymentyou run it (pip / Docker)fully managed, zero ops
Languages50+, very strong Chinese/Asianmany (varies by vendor)
Speaker diarization✅ built-in (cam++)✅ (extra cost/config)
Emotion / audio events✅ (SenseVoice)mostly ❌
Customization / fine-tune✅ full model accesslimited
Offline / air-gapped✅❌
Licenseopen-source, commercial-friendlyproprietary
+

To be fair: if what you want is zero ops, elastic scale, and no machines to manage, a managed cloud API is still the easy choice. FunASR's trade-off is that you run one machine of your own in exchange for controllable cost, private data, offline capability, and full customization.

+ +

When self-hosting pays off

+

Cloud STT typically bills per audio minute (roughly $0.6–$1.5 per audio hour depending on vendor/tier). So 1,000 hours/month is about $600–$1,500/month and grows linearly, whereas a self-hosted FunASR instance is a fixed machine cost, largely independent of volume. Self-hosting usually wins when any of these hold:

+
    +
  • High volume: lots of audio per month, where per-minute bills dwarf one machine's cost;
    • +
  • Sensitive data: healthcare/finance/government, where audio can't leave your network;
    • +
  • Offline / air-gapped deployment environments;
    • +
  • Chinese / dialects / Asian languages as the primary need, where you want stronger local accuracy and customization.
    • +
+ +

See also: the self-hosted OpenAI Whisper API alternative, the Deepgram/AssemblyAI alternative, the FunASR vs Whisper benchmark, and the model selection guide.

+ +

The whole FunASR stack is open-source (MIT) — industrial-grade ASR / VAD / punctuation / speaker / emotion & events / LLM-ASR, self-hosted and commercial-friendly. If it helps, a GitHub Star really supports the project 👇

⭐ Star FunASR

Also star:SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/self-hosted-openai-whisper-api-alternative.html b/web-pages/product-site/legacy/en/blog/self-hosted-openai-whisper-api-alternative.html new file mode 100644 index 0000000000..e58806d392 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/self-hosted-openai-whisper-api-alternative.html @@ -0,0 +1,151 @@ + + +Self-Hosted OpenAI Whisper API Alternative: Drop-in /v1/audio/transcriptions with FunASR | FunASR + + + + + + + +
+

Self-Hosted OpenAI Whisper API Alternative: Drop-in /v1/audio/transcriptions with FunASR

+

FunASR Blog · 2026-06-18 · Tutorial

+

OpenAI's /v1/audio/transcriptions (the Whisper API) charges per minute, uploads your audio to the cloud, and is rate-limited. If you just need audio turned into text, you can run a server with an identical interface on your own machine — existing code switches over by changing a single base_url. FunASR ships exactly that as funasr-server. Every snippet below is tested.

+ +

Start an OpenAI-compatible server in one line

+
pip install -U funasr
+funasr-server --model sensevoice --device cuda     # listens on localhost:8000
+

You now have the same two endpoints OpenAI exposes: POST /v1/audio/transcriptions and GET /v1/models.

+ +

Call it with the official OpenAI SDK (just change base_url)

+

Your existing OpenAI code is almost unchanged — point base_url at the local server and put any non-empty api_key:

+
from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
+
+with open("audio.wav", "rb") as f:
+    r = client.audio.transcriptions.create(model="sensevoice", file=f)
+print(r.text)
+# -> 欢迎大家来体验达摩院推出的语音识别模型   (the Chinese sample above)
+

Listing models works the same way:

+
print([m.id for m in client.models.list().data])
+# -> ['fun-asr-nano', 'sensevoice', 'paraformer']
+ +

Or use curl / any HTTP client

+
curl http://localhost:8000/v1/audio/transcriptions \
+  -F "file=@audio.wav" \
+  -F "model=sensevoice"
+# -> {"text": "..."}
+

Because the API is OpenAI-compatible, the Node openai package, LangChain, and most existing SDKs connect to it unchanged.

+ +

OpenAI Whisper API vs self-hosted FunASR

+ + + + + + + +
OpenAI Whisper APIFunASR self-hosted
Cost$0.006 / minFree (your hardware)
PrivacyAudio leaves your machineStays local
Rate limitsYesNone
Chinese accuracyMediocreHigher (benchmark)
Speed—SenseVoice is non-autoregressive, far faster than Whisper
Interface/v1/audio/transcriptionsIdentical
+ +

Which model to pick

+
    +
  • sensevoice — default; non-autoregressive, very fast, 50+ languages, with emotion/events. Best everyday choice.
    • +
  • fun-asr-nano — flagship LLM decoder for zh/en/ja plus Chinese dialects/accents (needs vLLM).
    • +
  • For 31 languages, deploy the separate FunAudioLLM/Fun-ASR-MLT-Nano-2512 checkpoint.
    • +
  • paraformer — classic, production-grade Chinese.
    • +
+

Just set model= in the request — no server restart needed.

+ +

What changes when migrating from OpenAI

+
    +
  • base_url: point it at your funasr-server.
    • +
  • api_key: not validated locally — any non-empty string works.
    • +
  • Everything else: the client.audio.transcriptions.create(...) call is the same.
    • +
+ +

Use FunASR as the speech-to-text in Open-WebUI

+

Open-WebUI (the 150k-star self-hosted LLM interface) lets its speech-to-text (STT) point at any OpenAI-compatible endpoint. Since funasr-server is OpenAI-compatible, you just point it there — no plugin, no Open-WebUI code changes — and get more accurate Chinese transcription than the built-in Whisper, fully local and free.

+

1. Start FunASR (as above):

+
funasr-server --model sensevoice --device cuda   # listens on localhost:8000
+

2. In Open-WebUI: Admin Panel → Settings → Audio → Speech-to-Text, set Engine to OpenAI and fill in:

+
    +
  • API Base URL: http://localhost:8000/v1 (if Open-WebUI runs in Docker and FunASR on the host, use http://host.docker.internal:8000/v1)
    • +
  • API Key: any non-empty string (funasr-server does not validate it)
    • +
  • STT Model: sensevoice (or paraformer / fun-asr-nano)
    • +
+

With Docker Compose this is the same as a few environment variables:

+
AUDIO_STT_ENGINE=openai
+AUDIO_STT_OPENAI_API_BASE_URL=http://host.docker.internal:8000/v1
+AUDIO_STT_OPENAI_API_KEY=funasr
+AUDIO_STT_MODEL=sensevoice
+

Now Open-WebUI's microphone / voice input runs through FunASR — better Chinese accuracy, data never leaves your machine. The same config works for any tool that supports a custom OpenAI STT endpoint (LibreChat, AnythingLLM, etc.).

+

FunASR is Tongyi Lab's open-source, industrial-grade speech recognition toolkit.

Star FunASR on GitHub ★
+ +

Related posts

+ +
+
FunASR · Open-source speech recognition by Tongyi Lab · GitHub
+ diff --git a/web-pages/product-site/legacy/en/blog/sensevoice-deployment-guide.html b/web-pages/product-site/legacy/en/blog/sensevoice-deployment-guide.html new file mode 100644 index 0000000000..22433516f0 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/sensevoice-deployment-guide.html @@ -0,0 +1,157 @@ + + + + + +SenseVoice Deployment Guide: Multilingual ASR 15x Faster Than Whisper | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

SenseVoice Deployment Guide: Multilingual Speech Recognition 15× Faster Than Whisper

+

FunASR Blog · 2026-06-16 · Tutorial

+

SenseVoice is an open-source multilingual speech understanding model from the FunAudioLLM team. Beyond automatic speech recognition (ASR), a single forward pass also returns spoken-language identification, emotion recognition, and audio-event detection. Its non-autoregressive architecture makes it roughly 15× faster than Whisper-Large, with latency low enough for both real-time and high-throughput batch workloads.

+

Here is a copy-paste guide to get SenseVoice running in three steps.

+ +

1. Install

+
pip install -U funasr torch torchaudio
+ +

2. Transcribe in three lines

+

This downloads SenseVoiceSmall automatically and runs recognition:

+
from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", device="cuda:0")  # use device="cpu" for CPU
+res = model.generate(input="audio.wav", language="auto", use_itn=True)
+print(rich_transcription_postprocess(res[0]["text"]))
+

Model IDs: iic/SenseVoiceSmall on ModelScope, FunAudioLLM/SenseVoiceSmall on HuggingFace. use_itn=True adds punctuation and inverse text normalization.

+ +

3. Reading the output: language + emotion + audio event

+

The raw output carries rich-text tags, for example:

+
<|en|><|NEUTRAL|><|Speech|><|withitn|>Welcome to the speech recognition model.
+
    +
  • <|en|> — language (zh, en, yue, ja, ko)
    • +
  • <|NEUTRAL|> — emotion (HAPPY / SAD / ANGRY / NEUTRAL ...)
    • +
  • <|Speech|> — audio event (Speech / BGM / Applause / Laughter ...)
    • +
+

Call rich_transcription_postprocess() for clean text, or read res[0]["text"] directly to keep the tags for downstream analysis.

+ +

4. Long audio: add VAD segmentation

+
model = AutoModel(
+    model="iic/SenseVoiceSmall",
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device="cuda:0",
+)
+res = model.generate(input="long_audio.wav", language="auto", use_itn=True, batch_size_s=60)
+ +

5. GPU vs CPU

+ + + + + + + +
ScenarioRecommendation
High concurrency / batchGPU (device="cuda:0")
Lightweight / edge / offlineCPU (device="cpu") — non-autoregressive, fast on CPU too
Production serviceFunASR runtime (C++ / Docker, multi-concurrency)
+ +

6. At a glance

+ + + + + + + + + +
DimensionSenseVoice-Small
Speed vs Whisper-Large~15×
LanguagesChinese / Cantonese / English / Japanese / Korean ...
Extra capabilitiesLanguage ID · Emotion · Audio events
ArchitectureNon-autoregressive (low latency)
LicenseOpen source (commercial use allowed)
+ +
+

Get started with SenseVoice

+

If this guide helped, please star the project on GitHub ⭐ to support open source.

+SenseVoice GitHub ★ +
+ +

Read more: FunASR Quickstart · All models · Ecosystem

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/sensevoice-emotion-language-detection.html b/web-pages/product-site/legacy/en/blog/sensevoice-emotion-language-detection.html new file mode 100644 index 0000000000..a9e0f408b1 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/sensevoice-emotion-language-detection.html @@ -0,0 +1,158 @@ + + + + + +Speech Emotion Recognition in Python — Language ID & Audio Events in One Model (SenseVoice) | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Speech Emotion Recognition in Python — Language ID & Audio Events in One Model (SenseVoice)

+

2026-06-19 · FunASR Team

+ +

Most speech-recognition (ASR) models give you only text. But real applications often need more: what emotion is the speaker in? what language is this? is the background clean speech or music? With Whisper you have to stack several models — language detection + a separate emotion model + an audio-event classifier. Slow and hard to maintain.

+

SenseVoice (an open-source multilingual speech-understanding model from the FunAudioLLM team) returns all of it in one non-autoregressive forward pass: the transcript plus spoken-language ID, emotion, audio-event detection, and inverse text normalization (ITN). It is roughly 15× faster than Whisper-Large.

+ +

What one inference gives you

+ + + + + + + +
CapabilityDetail
ASR50+ languages, leading Chinese accuracy
Language IDauto-detects zh / en / ja / ko / yue …
EmotionHAPPY 😊 / SAD 😔 / ANGRY 😡 / NEUTRAL / FEARFUL / DISGUSTED / SURPRISED
Audio eventsSpeech / BGM 🎵 / Applause 👏 / Laughter / Cry
Inverse text norm (ITN)“nine o'clock”→“9:00”, “fifty”→“50”
+ +

Three lines of Python

+
pip install funasr
+
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)
+res = model.generate(input="audio.wav", cache={}, language="auto", use_itn=True)
+
+print(res[0]["text"])                                    # raw, with tags
+print(rich_transcription_postprocess(res[0]["text"]))    # cleaned text
+ +

Real output: 5 languages auto-detected

+

Run on SenseVoice's bundled multilingual samples (zh/en/ja/ko/yue). Each raw output starts with tags:

+
<|zh|><|NEUTRAL|><|Speech|><|withitn|>开放时间早上9点至下午5点。
+<|en|><|NEUTRAL|><|Speech|><|withitn|>The tribal chieftain called for the boy and presented him with 50 pieces of gold.
+<|ja|><|NEUTRAL|><|Speech|><|withitn|>うちの中学は弁当制で持っていけない場合は、50 円の学校販売のパンを買う。
+<|ko|><|NEUTRAL|><|Speech|><|withitn|>조금만 생각을 하면서 살면 훨씬 편할 거야.
+<|yue|><|NEUTRAL|><|Speech|><|withitn|>呢几个字都表达唔到，我想讲嘅意思。
+

Note the leading tags: <|zh|> language (it even separates Cantonese yue), <|NEUTRAL|> emotion, <|Speech|> audio event, <|withitn|> ITN applied (“9点至下午5点”, “50 pieces”).

+ +

Emotion & audio events on real-world audio

+

Running SenseVoice over 60 real-world web clips: 56 were correctly tagged BGM (background music present) — exactly the case where Whisper tends to force a transcription and hallucinate — while HAPPY / ANGRY / NEUTRAL emotions were detected. For example, one clip tagged ANGRY:

+
<|zh|><|ANGRY|><|Speech|><|withitn|>哎,不要看不起那些理想主义者,你脚下的每一步都是他们走出来的。
+

Full tag sets — emotion: HAPPY / SAD / ANGRY / NEUTRAL / FEARFUL / DISGUSTED / SURPRISED; events: Speech / BGM / Applause / Laughter / Cry.

+ +

Parsing the tags

+
import re
+
+raw = res[0]["text"]
+tags = re.findall(r"<\|([^|]+)\|>", raw)
+language = tags[0] if tags else None                       # 'zh'
+emotion  = next((t for t in tags if t in
+            {"HAPPY","SAD","ANGRY","NEUTRAL","FEARFUL","DISGUSTED","SURPRISED"}), None)
+event    = next((t for t in tags if t in
+            {"Speech","BGM","Applause","Laughter","Cry"}), None)
+text     = re.sub(r"<\|[^|]+\|>", "", raw)         # clean text
+print(language, emotion, event, text)
+ +

vs Whisper

+ + + + + + + +
SenseVoiceWhisper
Transcript + language✅ one call✅
Emotion recognition✅ built-in❌ needs extra model
Audio events (BGM/applause/laughter)✅ built-in❌
Inverse text normalization✅ built-inpartial
Speednon-autoregressive, ~15× fasterautoregressive baseline
+

When you need understanding of audio and not just text, one SenseVoice model replaces a language-detector + emotion-model + event-classifier stack.

+ +

The whole FunASR stack is open-source — industrial-grade ASR / VAD / punctuation / speaker / emotion & events / LLM-ASR. If it helps, a GitHub Star really supports the project 👇

⭐ Star FunASR

Also star:SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/speech-to-text-python-transcribe-audio.html b/web-pages/product-site/legacy/en/blog/speech-to-text-python-transcribe-audio.html new file mode 100644 index 0000000000..55bb18c249 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/speech-to-text-python-transcribe-audio.html @@ -0,0 +1,134 @@ + + +Speech to Text in Python: Transcribe Audio Locally and Free with FunASR | FunASR + + + + + + + +
+

Speech to Text in Python: Transcribe Audio Locally and Free with FunASR

+

FunASR Blog · 2026-06-18 · Tutorial

+

You don't need a cloud API or a heavy Whisper setup to transcribe audio in Python. FunASR turns audio into text in a few lines, locally and for free, with timestamps, speaker diarization, and batch processing built in. It's especially strong on Chinese and supports 50+ languages. Every snippet below is tested.

+ +

Install

+
pip install -U torch torchaudio
+pip install -U funasr
+ +

Simplest: transcribe a file in a few lines

+
from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", device="cuda")
+result = model.generate(input="audio.wav")
+print(rich_transcription_postprocess(result[0]["text"]))
+# -> 欢迎大家来体验达摩院推出的语音识别模型   (Chinese sample)
+

Defaults to SenseVoice (non-autoregressive, very fast). rich_transcription_postprocess strips SenseVoice tags like <|zh|> to give clean text — don't skip it.

+ +

Transcribe straight from a URL

+
result = model.generate(
+    input="https://example.com/audio.wav"   # local paths, numpy, bytes also work
+)
+ +

Get timestamps and speakers

+

Each entry in sentence_info carries start/end (ms), spk (speaker), and sentence:

+
model = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad", spk_model="cam++", device="cuda")
+result = model.generate(input="audio.wav")
+
+for seg in result[0]["sentence_info"]:
+    start = seg["start"] / 1000
+    text = rich_transcription_postprocess(seg["sentence"])
+    print("[%.1fs] Speaker %s: %s" % (start, seg["spk"], text))
+# -> [0.6s] Speaker 0: 欢迎大家来体验达摩院推出的语音识别模型
+ +

Batch-transcribe multiple files

+

Pass a list to input and get one result per file:

+
results = model.generate(input=["a.wav", "b.wav", "c.wav"])
+for r in results:
+    print(rich_transcription_postprocess(r["text"]))
+ +

Which model to pick

+ + + + + +
model=Best for
iic/SenseVoiceSmallDefault; very fast, 50+ languages, emotion/events
paraformer-zhClassic, production-grade Chinese
FunAudioLLM/Fun-ASR-Nano-2512Flagship LLM decoder for zh/en/ja + Chinese dialects/accents
FunAudioLLM/Fun-ASR-MLT-Nano-2512Separate multilingual checkpoint covering 31 languages
+

Switch models by changing only model= — the rest of the code stays the same.

+ +

Why FunASR

+
    +
  • Local, free, private — no internet, no API key, no per-minute fees.
    • +
  • Fast: SenseVoice is non-autoregressive, far faster than Whisper (benchmark) — real-time even on CPU.
    • +
  • Stronger on Chinese + 50+ languages; timestamps, speakers, hotwords, and batching out of the box.
    • +
+

FunASR is Tongyi Lab's open-source, industrial-grade speech recognition toolkit.

Star FunASR on GitHub ★
+ +

Related posts

+ +
+
FunASR · Open-source speech recognition by Tongyi Lab · GitHub
+ diff --git a/web-pages/product-site/legacy/en/blog/speech-to-text-timestamps-python.html b/web-pages/product-site/legacy/en/blog/speech-to-text-timestamps-python.html new file mode 100644 index 0000000000..b7b4bb633c --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/speech-to-text-timestamps-python.html @@ -0,0 +1,144 @@ + + + + + +Speech-to-Text with Word/Character-Level Timestamps in Python — Millisecond-Accurate | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Speech-to-Text with Word/Character-Level Timestamps in Python — Millisecond-Accurate

+

2026-06-23 · FunASR Team

+ +

Many use cases need more than "what was said" — they need to know when each word was spoken: karaoke-style highlighting, click-a-word-to-seek transcripts, subtitle alignment, video-editing search. Whisper's word timestamps are bolted on (DTW-based, variable accuracy). FunASR's Paraformer emits character-level timestamps natively: every character comes with [start_ms, end_ms] in a single call. Here is real measured output.

+ +

Transcribe with timestamps (real output)

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", disable_update=True)
+res = model.generate(input="audio.wav")
+
+text = res[0]["text"]            # 欢 迎 大 家 来 体 验 ...
+timestamp = res[0]["timestamp"]  # [[880, 1120], [1120, 1360], ...]  per-character start/end ms
+

timestamp is a list of [start_ms, end_ms] pairs, one per character in the text.

+ +

Pair characters with timestamps (real)

+
chars = text.split()          # Paraformer's Chinese output is space-separated characters
+for ch, (start, end) in zip(chars, timestamp):
+    print(f"{ch}  {start}-{end}ms")
+

Actual output:

+ + + + + + + + + +
CharSpan (ms)
欢880 – 1120
迎1120 – 1360
大1380 – 1540
家1540 – 1780
来1780 – 2020
体2020 – 2180
验2180 – 2420
+

All 19 characters of the sentence get a millisecond-accurate start/end.

+ +

What you can build

+
    +
  • Word highlighting / karaoke: highlight the character whose span contains the current playback time.
    • +
  • Click-to-seek transcripts: clicking a character seeks the audio to its start_ms.
    • +
  • Precise subtitle alignment: split and time captions from the timestamps. For full subtitles, see generating SRT/VTT subtitles.
    • +
  • Video-edit search: jump to the exact moment a keyword is spoken.
    • +
+ +

Why Paraformer for timestamps

+ + + + + + +
FunASR ParaformerWhisper
Word/char timestamps✅ native, single callbolt-on (--word_timestamps, DTW)
Accuracynon-autoregressive + CIF alignment, stableimplementation-dependent
Chinesecharacter-level, CER 10.18%~20%
Licenseopen-source, commercial-friendlyopen-source
+

For higher Chinese accuracy, default to the flagship Fun-ASR-Nano; for the full Chinese walkthrough see Chinese speech recognition; for long-audio segmentation see VAD.

+ +

The whole FunASR stack is open-source (MIT) — character timestamps, ASR, VAD, punctuation, speaker, LLM-ASR (flagship Fun-ASR-Nano), ready to use. If it helps, a GitHub Star supports the project 👇

⭐ Star FunASR

Also star:SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/voice-activity-detection-python.html b/web-pages/product-site/legacy/en/blog/voice-activity-detection-python.html new file mode 100644 index 0000000000..4ffee985da --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/voice-activity-detection-python.html @@ -0,0 +1,155 @@ + + + + + +Voice Activity Detection in Python — Detect Speech, Remove Silence, Split Audio | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Voice Activity Detection in Python — Detect Speech, Remove Silence, Split Audio by Pauses

+

2026-06-22 · FunASR Team

+ +

Voice Activity Detection (VAD) is a foundational step in audio processing: finding where someone is speaking versus silence/noise in a recording. It's the first stage of almost every speech pipeline — drop silence to save compute, split long recordings into utterances on pauses, and preprocess audio for any downstream ASR (including feeding Whisper clean speech to cut cost and stop it from "hallucinating" text over silent stretches).

+

FunASR's fsmn-vad is an industrial-grade, lightweight, very fast open-source VAD model. Three lines of Python, and it returns the start/end milliseconds of every speech region. Everything below is real measured output.

+ +

VAD in three lines

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="fsmn-vad", disable_update=True)
+segments = model.generate(input="audio.wav")[0]["value"]
+
+print(segments)
+# [[610, 5530]]   # milliseconds — speech from 0.61s to 5.53s; the leading 610ms of silence is dropped
+

The return value is a list of [start_ms, end_ms] intervals, one per continuous speech region. In this 5.55s clip, the 0.61s of leading silence was removed automatically.

+ +

Real example: a 13-second clip split into 2 segments

+

Concatenating two utterances with a 1.5s pause between them gives a 13.31s recording (simulating a real clip with a pause). Actual VAD output:

+ + + + +
SegmentSpanDuration
seg00.61s → 5.45s4.84s
seg17.28s → 13.29s6.01s
+

VAD cleanly isolates the two speech regions and marks both the 1.5s pause (5.45s–7.28s) and the leading silence as non-speech. Speech is 82% of the 13.31s clip — 18% of silence removed. This took just 0.12s on GPU (~110× real-time): VAD is extremely light and adds almost no overhead.

+ +

Three practical use cases

+

1. Remove silence / keep speech only

+
import soundfile as sf
+import numpy as np
+
+audio, sr = sf.read("audio.wav")
+segments = model.generate(input="audio.wav")[0]["value"]
+
+# concatenate all speech regions, drop silence
+speech = np.concatenate([audio[int(s/1000*sr):int(e/1000*sr)] for s, e in segments])
+sf.write("speech_only.wav", speech, sr)
+ +

2. Split a long recording into files on pauses (for batch ASR)

+
for i, (s, e) in enumerate(segments):
+    chunk = audio[int(s/1000*sr):int(e/1000*sr)]
+    sf.write(f"chunk_{i}.wav", chunk, sr)
+# chunk_0.wav, chunk_1.wav, ...  each file is one independent utterance
+ +

3. Preprocess for any ASR (including Whisper)

+

Running VAD before recognition has two concrete benefits: (1) you never send silence/noise into the model = less compute, lower cost; (2) Whisper tends to hallucinate repeated text over long silences, and trimming silence with VAD first measurably reduces those hallucinations. With FunASR's own ASR models (SenseVoice / Paraformer) you can wire VAD in with a single vad_model="fsmn-vad" argument for automatic segmentation.

+
from funasr import AutoModel
+# attach VAD directly to ASR; long audio is auto-segmented
+asr = AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad")
+result = asr.generate(input="long_audio.wav")
+ +

Why fsmn-vad

+ + + + + + + +
fsmn-vad (FunASR)
Usage3 lines of Python, returns millisecond speech spans
Speedmeasured 0.12s on 13s audio (~110× real-time)
Footprintlightweight FSMN architecture, real-time even on CPU
Ecosystemuse standalone, or attach to FunASR ASR in one line for auto-segmentation
Licenseopen-source, commercial-friendly
+

If you already use FunASR for recognition, VAD is the same toolkit with zero extra dependencies; if you use Whisper or another ASR, fsmn-vad works as a standalone preprocessor. For full pipelines, see transcribing long audio and speaker diarization.

+ +

The whole FunASR stack is open-source — fsmn-vad, industrial-grade ASR, punctuation, speaker, emotion & events, LLM-ASR, ready to use. If it helps, a GitHub Star really supports the project 👇

⭐ Star FunASR

Also star:SenseVoice · Fun-ASR · FunClip

+ +

Related posts

+ +
+ + + \ No newline at end of file diff --git a/web-pages/product-site/legacy/en/blog/which-funasr-model.html b/web-pages/product-site/legacy/en/blog/which-funasr-model.html new file mode 100644 index 0000000000..813af2cba7 --- /dev/null +++ b/web-pages/product-site/legacy/en/blog/which-funasr-model.html @@ -0,0 +1,157 @@ + + + + + +Which FunASR Model? Nano vs MLT-Nano vs SenseVoice vs Paraformer (2026 Guide) | FunASR Blog + + + + + + + + + + + + + + + + + + +
+

Which FunASR Model? Nano vs MLT-Nano vs SenseVoice vs Paraformer (2026 Guide)

+

Updated 2026-07-15 · FunASR Team

+ +

FunASR ships three main model families, plus a separate multilingual MLT-Nano checkpoint. In one line: with a GPU, use flagship Fun-ASR-Nano for Chinese, English, Japanese, and Chinese dialects/accents; use Fun-ASR-MLT-Nano when you need 31 languages; on CPU use SenseVoice, or Paraformer for Chinese timestamps/hotwords.

+ +

Pick at a glance

+ + + + + + +
Your situationPickWhy
GPU / zh, en, ja, or Chinese dialects and accents / want the best⭐ Fun-ASR-NanoLLM-ASR flagship, strongest on context and hard cases, Chinese CER 8.06%
Need broad multilingual coverageFun-ASR-MLT-NanoSeparate checkpoint covering 31 languages
CPU / edge / want fastestSenseVoicenon-autoregressive & very fast, real-time on CPU, multilingual + emotion, CER 7.81%
CPU + Chinese-only + timestamps/hotwordsParaformerlightest, character timestamps, hotwords, CER 10.18%
+

The Chinese CER figures compare Nano, SenseVoice, and Paraformer; all three beat Whisper on Chinese (~20%) by a wide margin. SenseVoice has a slightly lower CER, but Fun-ASR-Nano is the LLM-based flagship — more robust on real-world audio, context, and proper nouns. MLT-Nano is the choice for 31-language coverage; do not apply its language claim to Nano.

+ +

Fun-ASR-Nano — LLM-ASR flagship, the default (GPU)

+

SenseVoice encoder + a Qwen3-0.6B decoder, supporting Chinese, English, Japanese, 7 Chinese dialect groups, and 26 regional accents, with LLM-grade context understanding for hard cases and long-tail vocabulary.

+
pip install funasr
+
+from funasr import AutoModel
+
+model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", disable_update=True, device="cuda")
+res = model.generate(input="audio.wav")
+
+print(res[0]["text"])
+# 欢迎大家来体验达摩院推出的语音识别模型。  ("Welcome everyone to try the speech model from DAMO Academy")
+

Models: Hugging Face · ModelScope. For scale/throughput, use vLLM acceleration (~340× real-time measured). See the Fun-ASR-Nano guide.

+ +

Fun-ASR-MLT-Nano — separate 31-language checkpoint (GPU)

+

Choose MLT-Nano for recognition across 31 languages. It is a separate checkpoint from flagship Nano, with a different model ID and language scope.

+

Models: Hugging Face · ModelScope.

+ +

SenseVoice — the CPU/edge choice

+

Non-autoregressive, lightweight and very fast — real-time even on CPU — emitting language + emotion + audio events in one pass; q8 quantization is ~250MB. Pick it for CPU deployment or lowest latency.

+
from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+model = AutoModel(model="iic/SenseVoiceSmall", disable_update=True)  # CPU by default
+res = model.generate(input="audio.wav", language="auto", use_itn=True)
+
+print(rich_transcription_postprocess(res[0]["text"]))
+# 欢迎大家来体验达摩院推出的语音识别模型。
+ +

Paraformer — CPU + Chinese-only (timestamps/hotwords)

+

The lightest Chinese recognizer, with character-level timestamps and hotword customization — use it for subtitles or word-level alignment.

+
from funasr import AutoModel
+
+model = AutoModel(model="paraformer-zh", vad_model="fsmn-vad", punc_model="ct-punc")
+res = model.generate(input="audio.wav", batch_size_s=300)
+
+print(res[0]["text"])
+# 欢迎大家来体验达摩院推出的语音识别模型。
+# res[0]["timestamp"] -> [[880, 1120], [1120, 1360], ...] per-character start/end ms
+ +

Quick decision

+
    +
  • Need 31-language coverage? → Fun-ASR-MLT-Nano.
    • +
  • Have a GPU and mainly need zh/en/ja or Chinese dialects/accents? → Fun-ASR-Nano (the default flagship).
    • +
  • CPU only? Chinese-only with timestamps/hotwords → Paraformer; otherwise (incl. multilingual/emotion) → SenseVoice.
    • +
  • Need Cantonese? SenseVoice supports it natively — see Cantonese speech recognition.
    • +
+

More: Chinese speech recognition · FunASR vs Whisper benchmark · Fun-ASR-Nano guide.

+ +

The whole FunASR stack is open-source (MIT) — Fun-ASR-Nano / MLT-Nano + SenseVoice + Paraformer + VAD/punctuation/speaker. If it helps, a GitHub Star supports the project 👇

⭐ Star Fun-ASR

Also star:FunASR · SenseVoice · FunClip

+ +

Related posts

+ +
+ + + diff --git a/web-pages/product-site/legacy/en/donors.html b/web-pages/product-site/legacy/en/donors.html new file mode 100644 index 0000000000..20548ac524 --- /dev/null +++ b/web-pages/product-site/legacy/en/donors.html @@ -0,0 +1,57 @@ + + +FunASR Community Thanks + + + + + + + +
+
Community
+

FunASR Community Thanks

+

Thank you to everyone who supports the FunASR open-source community. Donations fund community infrastructure, including server purchase and maintenance, as well as the purchase, renewal, and maintenance of the www.funasr.com domain. This helps keep the website, documentation, and community services reliably available. The list is preserved from the historical home-page donor list.

+
+
刘连响300 RMB
+
程俊陶300 RMB
+
黄明236 RMB
+
高志付235 RMB
+
陈翔200 RMB
+
夏侯杰200 RMB
+
黄药师198 RMB
+
肖炜100 RMB
+
刘柱100 RMB
+
黄水杯85 RMB
+
子龙85 RMB
+
+

Thank you for helping the open-source FunASR community keep moving. For name or amount corrections, please contact maintainers through a GitHub issue.

+
+ + + diff --git a/web-pages/product-site/legacy/en/ecosystem.html b/web-pages/product-site/legacy/en/ecosystem.html new file mode 100644 index 0000000000..06fc8200ff --- /dev/null +++ b/web-pages/product-site/legacy/en/ecosystem.html @@ -0,0 +1,403 @@ + + + + + +FunASR Ecosystem — Projects & Integrations Using FunASR + + + + + + + + + + + + + + + + + + + +
+
+ +

FunASR Ecosystem

+

Open-source projects and integrations powered by FunASR, SenseVoice, and Paraformer.

+ +
+
50+
Integrations
+
50+
Languages
+
35K+
GitHub Stars
+
1M+
pip installs/month
+
+ +

Video & Media Tools

+
+
+
FunClip
+
6K+ stars
+
ASR-powered video clipping. Automatic subtitles, keyword and speaker-based clip extraction. Download v2.1.0
+
VideoOfficial
+
+
+
pyVideoTrans
+
17.6K stars
+
Video translation tool. Uses FunASR for Chinese speech recognition with subtitle generation.
+
VideoTranslation
+
+
+
Voice-Pro
+
10.4K stars
+
Gradio WebUI for TTS, voice cloning, and audio processing with ASR capabilities.
+
AudioTTS
+
+
+
Linly-Dubbing
+
3.2K stars
+
AI video dubbing toolkit. Automatic speech recognition, translation, and voice cloning for multilingual dubbing.
+
DubbingVideo
+
+
+ +

Voice Input & Desktop Apps

+
+
+
CapsWriter-Offline
+
5.5K stars
+
PC voice input tool with offline recognition. Hold CapsLock to speak, release to paste. Powered by FunASR Paraformer.
+
DesktopVoice Input
+
+
+
OpenLess
+
1.8K stars
+
Voice input for macOS & Windows. Hold a key, speak, release — text appears at cursor. Uses SenseVoice via Sherpa ONNX.
+
DesktopmacOS
+
+
+
ququ
+
2.2K stars
+
Open-source Wispr Flow alternative. Desktop voice workflow integrating FunASR local models with configurable LLMs.
+
DesktopVoice Input
+
+
+
MTools
+
1.2K stars
+
Multi-function desktop app with audio/video processing, image editing, and AI-enhanced speech transcription.
+
DesktopToolkit
+
+
+
VocoType
+
704 stars
+
Privacy-first local voice input tool. Converts speech to text via hotkey and auto-types into any app. Supports MCP integration.
+
Voice InputPrivacy
+
+
+
LiveTranslate
+
325 stars
+
Real-time audio translation. Captures system audio + mic, uses SenseVoice for ASR, then LLM streaming translation.
+
TranslationReal-time
+
+
+
VocoType-linux
+
139 stars
+
High-performance Linux offline Chinese voice input. Based on FunASR, 0.1s instant display, IBus/Fcitx5 support.
+
LinuxInput Method
+
+
+
Murmur
+
103 stars
+
Free offline voice-to-text for macOS. Push-to-talk, works in any app. Fully local processing with SenseVoice.
+
macOSVoice Input
+
+
+
AriaType
+
74 stars
+
Voice-driven writing, input, and cross-app work for your desktop. Speech-to-text with AI refinement.
+
DesktopWriting
+
+
+
VoiceSnap
+
73 stars
+
Open-source offline voice dictation — a free Typeless alternative. 100% local, SenseVoice + DirectML, ideal for air-gapped environments.
+
OfflineSecurity
+
+
+ +

Voice Assistants & Agents

+
+
+
Fay
+
12.8K stars
+
Digital human agent framework connecting 2.5D/3D avatars with LLMs. Uses FunASR for real-time speech recognition.
+
Digital HumanAgent
+
+
+
Duix Avatar
+
13.4K stars
+
Open-source AI digital human toolkit. Offline video generation and real-time interaction. Uses FunASR for speech recognition.
+
Digital HumanVideo
+
+
+
Wukong Robot
+
7.1K stars
+
Chinese voice assistant / smart speaker on Raspberry Pi. Supports ChatGPT, brain-computer interaction. FunASR as ASR engine.
+
IoTAssistant
+
+
+
Paseo
+
6.9K stars
+
Coding agent from your phone, desktop, and CLI. Uses Paraformer and SenseVoice for speech recognition via Sherpa ONNX.
+
CodingAgent
+
+
+
Linly-Talker
+
3.3K stars
+
Digital avatar conversational system. Combines ASR, LLM, and TTS for natural dialogue with virtual characters. Uses FunASR.
+
Digital AvatarDialogue
+
+
+
AudioNotes
+
2.1K stars
+
Extract audio/video content into structured markdown notes. Uses FunASR for accurate transcription.
+
NotesProductivity
+
+
+
Bailing
+
1.7K stars
+
GPT-4o-style voice chatbot. Full ASR + LLM + TTS pipeline for natural voice conversations. Powered by FunASR.
+
Voice ChatGPT-4o
+
+
+ +

AI Platforms & Frameworks

+
+
+
GPT-SoVITS
+
58K stars
+
1-minute voice data TTS. Uses FunASR for training data annotation — automatic speech-to-text labeling.
+
TTSTraining
+
+
+
LocalAI
+
33K stars
+
Self-hosted OpenAI alternative. FunASR integration as speech-to-text backend (PR in review).
+
LLMSelf-hosted
+
+
+
LiteLLM
+
54.3K stars
+
OpenAI-compatible AI Gateway. FunASR / SenseVoice can be routed through custom_openai as a self-hosted transcription endpoint.
+
AI GatewayOpenAI-compatible
+
+
+
Pipecat
+
12.5K stars
+
Voice and multimodal conversational AI framework. FunASR as community STT integration.
+
Conversational AI
+
+
+
Amphion
+
9.8K stars
+
Open-source audio, music, and speech generation toolkit from OpenMMLab. Uses FunASR for TTS evaluation and data processing.
+
Audio ToolkitOpenMMLab
+
+
+
Dify · Official FunASR Plugin
+
150K stars · 352 installs
+
The official FunASR plugin 0.1.1 is live on Dify Marketplace with SenseVoice, Fun-ASR-Nano, and Paraformer presets, supporting 25 MB uploads.
+
Official PluginSpeech-to-Text
+
+
+
Xinference
+
9.3K stars
+
Distributed inference framework. Built-in FunASR speech recognition backend with one-click ASR model deployment.
+
InferenceDistributed
+
+
+
AIGCPanel
+
5K stars
+
All-in-one AI digital human system with video synthesis, voice cloning. Integrates FunASR for speech recognition.
+
Digital HumanAIGC
+
+
+
MiniMind-O
+
1.6K stars
+
Lightweight multimodal model combining vision, audio, and language understanding. Uses FunASR for speech recognition module.
+
MultimodalLLM
+
+
+
ComfyUI-FunAudioLLM
+
95 stars
+
ComfyUI custom nodes for SenseVoice and CosyVoice. Visual workflow builder for speech recognition and synthesis.
+
ComfyUIWorkflow
+
+
+ +

SenseVoice Community Extensions

+
+
+
OmniSenseVoice
+
894 stars
+
Enhanced SenseVoice with high-accuracy word-level timestamps. Same speed as original model.
+
TimestampsSenseVoice
+
+
+
api4sensevoice
+
541 stars
+
API and WebSocket server for SenseVoice. Supports VAD detection, real-time streaming, and speaker verification.
+
APIWebSocket
+
+
+
streaming-sensevoice
+
451 stars
+
Pseudo-streaming SenseVoice with hotword boosting. Low-latency near-realtime speech recognition.
+
StreamingHotwords
+
+
+
SenseVoice-python
+
111 stars
+
Enterprise-grade SenseVoice inference with ONNX Runtime. No PyTorch dependency, production-ready deployment.
+
ONNXDeployment
+
+
+
SenseVoice-Api
+
109 stars
+
FastAPI wrapper for SenseVoice with ONNX inference. Smaller footprint, quantized models, GPU acceleration.
+
FastAPIQuantized
+
+
+
SenseVoice-OneApi
+
93 stars
+
SenseVoice API service compatible with OneAPI. Unified interface for managing multiple speech recognition models.
+
OneAPIAPI
+
+
+ +

Cross-Platform Inference

+
+
+
Sherpa-ONNX
+
5K+ stars
+
Cross-platform speech processing with ONNX. Runs SenseVoice and Paraformer on iOS, Android, Raspberry Pi, and browsers.
+
MobileEdge
+
+
+
RapidASR
+
608 stars
+
Cross-platform ASR inference library based on ONNX Runtime and FunASR. Ready to use, supports Chinese-English mixed recognition.
+
ONNXCross-Platform
+
+
+
SenseVoice.cpp
+
550 stars
+
C/C++ implementation of SenseVoice model. Pure C++ inference with no Python dependency.
+
C++Embedded
+
+
+
Transformers
+
142K stars
+
Hugging Face Transformers library. Fun-ASR-Nano integration (PR in review) — use FunASR models with the familiar HF API.
+
ML Framework
+
+
+
Vox-Box
+
211 stars
+
OpenAI-compatible speech server supporting FunASR, Whisper, Bark, and CosyVoice backends.
+
API ServerOpenAI-compat
+
+
+
FunSpeech
+
136 stars
+
Out-of-the-box local speech service. Microservice architecture, compatible with Alibaba Cloud Speech API and OpenAI TTS API.
+
API ServerSelf-hosted
+
+
+
FunASR-GGML
+
113 stars
+
C++ inference engine based on GGML. CPU/CUDA support, real-time mic streaming, single GGUF file deployment.
+
GGMLC++
+
+
+
ManySpeech
+
79 stars
+
Multi-model ASR inference solution supporting Paraformer, SenseVoice, Whisper, and more. ONNX-based, multi-scenario ready.
+
Multi-modelONNX
+
+
+ +

Get Started

+ +

The fastest way to try FunASR:

+ +
pip install funasr
+
+# Python API
+from funasr import AutoModel
+model = AutoModel(model="iic/SenseVoiceSmall")
+result = model.generate(input="audio.wav")
+
+# Or start an OpenAI-compatible server
+pip install vllm fastapi uvicorn python-multipart
+funasr-server --device cuda
+ +
+

Build something with FunASR? We'd love to list it here.

+GitHub +Open an Issue +
+ +
+
+ + + + diff --git a/web-pages/product-site/legacy/en/index.html b/web-pages/product-site/legacy/en/index.html new file mode 100644 index 0000000000..b176644f4a --- /dev/null +++ b/web-pages/product-site/legacy/en/index.html @@ -0,0 +1,375 @@ + + + + + +FunASR - Industrial Speech Recognition | All-in-One Speech Understanding + + + + + + + + + + + + + + + + + + + +
+
+
+

Industrial-Grade
Speech Recognition

+
+🚀 Flagship Fun-ASR-Nano · zh/en/ja + Chinese dialects/accents +🌍 31 languages Fun-ASR-MLT-Nano · separate checkpoint +
+

Speech recognition, voice activity detection, punctuation restoration, speaker diarization, emotion detection, and audio event recognition — one unified Python API handles it all. 50+ languages, self-hosted, production-ready.

+
+How to Use +Watch Demo +
+
+
50+Languages
+
170xRealtime Speed
+
1 APIUnified Interface
+
+
+
+# Start speech recognition service
+$ pip install torch torchaudio +pip install funasr vllm fastapi uvicorn python-multipart
+$ funasr-server --device cuda
+
+# Call with OpenAI SDK
+from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8899/v1", api_key="x")
+result = client.audio.transcriptions.create(
+    model="fun-asr-nano",
+    file=open("meeting.wav", "rb")
+)
+print(result.text) +
+
+
+ + +
+
+

Core Capabilities

+

A complete speech understanding pipeline from raw audio to structured output — all in one call

+
+
+
🎙️

Speech Recognition

End-to-end ASR supporting 50+ languages, including 7 Chinese dialects and 26 regional accents, with automatic language detection

+
📍

Voice Activity Detection

Millisecond-precision VAD with adaptive silence thresholds, accurately segmenting speech from silence

+
✍️

Punctuation Restoration

Automatically adds punctuation and applies inverse text normalization, producing readable formatted text

+
👥

Speaker Diarization

Identifies who said what, labeling each sentence with a speaker ID — ideal for meetings and interviews

+
😊

Emotion Detection

Recognizes emotional states — happy, sad, angry, neutral — for customer service QA and sentiment analysis

+
🔔

Audio Event Recognition

Detects background music, applause, laughter, crying, and other acoustic events for full scene understanding

+
+
+ + +
+
+

How to Use

+

Three steps: Install → Choose your scenario → Call

+
+ + +
+
+$ pip install torch torchaudio +pip install funasr vllm fastapi uvicorn python-multipart +
+

Python 3.8+ · GPU 8GB+ · Linux / macOS

+
+ + +
+ + + +
+ +
+
+

File Transcription — Upload audio, get complete results

+

Ideal for meeting recordings, video subtitles, and batch processing. Automatically includes VAD segmentation, punctuation, timestamps, and speaker labels.

+
+# Start offline transcription service (works after pip install)
+$ funasr-server --device cuda --port 8899
+
+# Call (curl)
+$ curl -X POST http://localhost:8899/v1/audio/transcriptions \
+    -F "file=@meeting.wav" -F "model=fun-asr-nano" -F "response_format=verbose_json" +
+
+Output +[00:01.7 → 00:05.5] Speaker 0: Let's discuss the three topics today.
+[00:05.8 → 00:08.2] Speaker 1: Sounds good. First one is the Q3 plan.
+[00:08.5 → 00:12.1] Speaker 0: Go ahead, we have 30 minutes. +
+
+
+ +
+
+

Real-time Recognition — Speak and see results instantly

+

For live captions, broadcast transcription, and voice assistants. WebSocket-based protocol with confirmed text that never changes and new text that updates continuously.

+
+# Streaming requires source code (not in pip yet)
+$ git clone https://github.com/modelscope/FunASR.git && cd FunASR
+$ python examples/industrial_data_pretraining/fun_asr_nano/serve_realtime_ws.py --port 10095 --language 中文
+
+# Open the built-in browser client
+$ open client_mic.html
+
+# Or connect via Python
+$ python client_python.py --server ws://localhost:10095 --mic +
+
+Real-time output (updates progressively) +[live] Let's discuss the...
+[confirmed] Let's discuss the three topics today.
+[live] Sounds good first...
+[confirmed] Sounds good. First one is the Q3 plan. +
+
+
+ +
+
+

API Integration — OpenAI-compatible, zero-code changes for AI frameworks

+

Standard /v1/audio/transcriptions endpoint. LangChain, AutoGen, Dify, and Coze can connect directly without any code modifications.

+
+# Start OpenAI-compatible API
+$ funasr-server --device cuda
+
+# Python (identical to OpenAI Whisper API)
+from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8899/v1", api_key="x")
+
+result = client.audio.transcriptions.create(
+    model="fun-asr-nano",
+    file=open("audio.wav", "rb"),
+    response_format="verbose_json"
+)
+print(result.text) +
+
+JSON Response +{"text": "Let's discuss the three topics today.", "segments": [{"start": 1.7, "end": 5.5, "text": "..."}], "duration": 12.1} +
+
+
+ + + +
+ + +
+
+

Performance

+

184 files / 11,541 seconds / Fun-ASR-Nano

+
+ + + + + + + + +
ModelEngineRTFxCERNotes
Fun-ASR-NanoPyTorch218.06%Baseline
Fun-ASR-NanovLLM batch3408.20%16x speedup
Fun-ASR-NanoOffline service1028.14%Incl. VAD segment timing
GLM-ASR-NanovLLM batch26512.93%Community model
+

Accuracy matches PyTorch exactly (CER delta < 0.2%), with 16–340x speedup. Full report →

+
+ + +
+
+

Product Demo

+

Watch FunASR real-time speech recognition in action

+
+
+ +
+
+ + + + + + + + diff --git a/web-pages/product-site/legacy/en/llama-cpp.html b/web-pages/product-site/legacy/en/llama-cpp.html new file mode 100644 index 0000000000..11f35d5e3e --- /dev/null +++ b/web-pages/product-site/legacy/en/llama-cpp.html @@ -0,0 +1,132 @@ + + + + + +Run FunASR on CPU: the llama.cpp / GGUF Runtime (a whisper.cpp Alternative) | FunASR + + + + + + + + + + + + + + + +
+

Run FunASR on CPU — the llama.cpp / GGUF Runtime

+

Single binary · No Python · No GPU · Quantized weights · whisper.cpp-style on-device ASR, strong on Chinese

+ +

FunASR on llama.cpp is to FunASR what whisper.cpp is to Whisper: it runs SenseVoice / Paraformer / Fun-ASR-Nano on the ggml stack, so the models work where there is no GPU and no Python (laptops, edge boxes, embedded C/C++ apps), complementing the PyTorch / vLLM paths for GPU serving. FSMN-VAD is built into the binaries.

+ +

Download prebuilt binaries (download & run)

+

Linux (x64/arm64), macOS (arm64), Windows (x64) — static, self-contained, zero dependencies. Available in all three repos' Releases:

+
+⬇ modelscope/FunASR
all three models +⬇ Current runtime v0.1.8
Linux/Windows Vulkan + Windows CUDA + 9 platform assets +Python package v1.3.26
--hub ms fix + runtime download table +⬇ QwenAudio/SenseVoice
multilingual + emotion/events +⬇ QwenAudio/Fun-ASR
Fun-ASR-Nano (LLM-ASR) +
+

Recommended download entry today: runtime-llamacpp-v0.1.9. Common accelerated builds include funasr-llamacpp-linux-x64-vulkan.tar.gz, funasr-llamacpp-windows-x64-vulkan.zip, and funasr-llamacpp-windows-x64-cuda.zip. Python users can install python -m pip install -U "funasr==1.3.26"; the v1.3.26 Latest Release also lists the same runtime download links and SHA-256 checksums.

+ +

Three lines to run

+
# 1) Unpack the binaries, fetch a model (downloads GGUF + VAD)
+bash download-funasr-model.sh sensevoice ./gguf
+
+# 2) Get text directly (in-binary detok, no Python)
+llama-funasr-sensevoice -m ./gguf/SenseVoiceSmall-f16.gguf --vad ./gguf/fsmn-vad.gguf -a audio.wav
+

Other models: download-funasr-model.sh paraformer with llama-funasr-paraformer; download-funasr-model.sh nano with llama-funasr-cli (Fun-ASR-Nano for zh/en/ja + Chinese dialects/accents). The 31-language scope belongs to the separate MLT-Nano checkpoint.

+ +

Accuracy: far ahead of whisper.cpp on Chinese

+

Same 184-clip Chinese test set, character error rate (CER, micro-avg, lower is better):

+ + + + + + + + +
ModelCER ↓Notes
FunASR SenseVoice8.01%multilingual + emotion/events
FunASR Paraformer9.85%non-autoregressive, industrial Chinese
FunASR Fun-ASR-Nano8.30%LLM-ASR, zh/en/ja + Chinese dialects/accents
whisper.cpp small22.12%
whisper.cpp large-v3-turbo23.15%
whisper.cpp base31.33%
+

FunASR's Chinese CER is roughly a third of whisper.cpp's. Full methodology in each repo's runtime/llama.cpp/BENCHMARKS.md.

+ +

Model sizes & accuracy (quantized)

+

Quantized builds are smaller and faster with virtually unchanged accuracy (184-file micro-CER):

+ + + + + + + + + +
ModelTypeSizeCER ↓Speed
SenseVoice encoderf16470 MB8.01%23×
SenseVoice encoderq8254 MB7.99%27×
Paraformer encoderf16435 MB9.85%22×
Paraformer encoderq8237 MB9.78%27×
Fun-ASR-Nano LLM*q8_0805 MB8.30%6.0×
Fun-ASR-Nano LLM*q5_K_M551 MB8.25%5.7×
Fun-ASR-Nano LLM*q4_K_M484 MB8.35%6.1×
+

* Fun-ASR-Nano also needs the encoder (f16, 470 MB). The q8 encoder halves size while CER even drops slightly; the three Nano LLM tiers are nearly identical — pick q4_K_M (smallest) or q5_K_M (best).

+ +

What's included

+
    +
  • 6 binaries: llama-funasr-{cli,encoder,embd,sensevoice,paraformer,vad}, static, no .so dependencies.
    • +
  • Built-in FSMN-VAD (--vad), in-binary detokenization (prints text), kaldi-compatible fbank front end.
    • +
  • GGUF models on Hugging Face: FunAudioLLM / funasr (f16 / f32 / q8 with embedded vocab; q8 is ~half the size of f16 with the same accuracy).
    • +
  • Source & docs: runtime/llama.cpp/ (README / DESIGN / BENCHMARKS).
    • +
+ +

If it helps, a GitHub Star really supports the project 👇 Fully open-source, commercial-friendly.

+

⭐ Star FunASR

+

Also star: SenseVoice · Fun-ASR · FunClip

+

Further reading: FunASR on llama.cpp (a whisper.cpp alternative) — deep dive

+
+ + + diff --git a/web-pages/product-site/legacy/en/models.html b/web-pages/product-site/legacy/en/models.html new file mode 100644 index 0000000000..dfd20ecd84 --- /dev/null +++ b/web-pages/product-site/legacy/en/models.html @@ -0,0 +1,235 @@ + + + + + +FunASR Models — SenseVoice, Paraformer, Fun-ASR-Nano, cam++ + + + + + + + + + + + + + + + + + +
+
+ +

FunASR Models

+

Choose the right model for your use case — from ultra-fast multilingual recognition to the highest Chinese accuracy.

+ +

Quick Comparison

+ + + + + + + + + + + + +
ModelSpeedLanguagesParamsBest For
Fun-ASR-Nano ⭐vLLM 340xzh/en/ja + Chinese dialects/accents800MFlagship · LLM-ASR · hardest cases
Fun-ASR-MLT-NanovLLM31800MSeparate multilingual checkpoint
SenseVoice Small170x realtime50+234MFast multilingual, emotion detection
Paraformer-zh120x realtimezh, yue220MBest Chinese accuracy
cam++realtimeany7.2MSpeaker diarization & verification
+ +

ASR Models

+ +
+
Fun-ASR-Nano ⭐ Flagship / default
+
800M params · LLM-based (SenseVoice encoder + Qwen3-0.6B) · GitHub · HuggingFace
+
Next-generation LLM-based ASR model. Combines SenseVoice's audio encoder with Qwen3-0.6B language model for superior context understanding. Supports vLLM acceleration for high-throughput batch inference and real-time streaming. The released model.pt checkpoint does not provide reliable checkpoint-native character timestamps (issue #106).
+
+vLLM accelerated +zh/en/ja + Chinese dialects/accents +Streaming +LLM-quality +
+

When to use

+

Best for high-throughput batch processing, real-time subtitles, and scenarios where LLM-quality context understanding improves output (e.g., proper nouns and code-switching). For reliable character-level timestamps, use Paraformer.

+
# With vLLM acceleration
+from funasr import AutoModel
+model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", device="cuda", backend="vllm")
+result = model.generate(input="audio.wav")
+print(result[0]["text"])
+
+ +
+
Fun-ASR-MLT-Nano 31 languages
+
800M params · Separate multilingual checkpoint · HuggingFace · ModelScope
+
A separate checkpoint for broad multilingual recognition across 31 languages. Its model ID and language scope differ from flagship Fun-ASR-Nano.
+
+vLLM accelerated +31 languages +Separate checkpoint +
+

When to use

+

Choose MLT-Nano for recognition across 31 languages; choose flagship Nano for zh/en/ja and Chinese dialects/accents.

+
from funasr import AutoModel
+model = AutoModel(model="FunAudioLLM/Fun-ASR-MLT-Nano-2512", device="cuda")
+result = model.generate(input="audio.wav")
+
+ +
+
SenseVoice Small
+
234M params · Non-autoregressive · GitHub · HuggingFace
+
Ultra-fast speech recognition with built-in emotion and audio event detection. Supports 50+ languages including Chinese, English, Japanese, Korean, French, German, and more. Non-autoregressive architecture delivers 170x realtime speed on GPU.
+
+170x realtime +50+ languages +Emotion detection +Audio events +CPU-viable +
+

When to use

+

Best for: multilingual applications, real-time streaming, batch processing large audio collections, applications needing emotion or audio event detection.

+
from funasr import AutoModel
+model = AutoModel(model="iic/SenseVoiceSmall")
+result = model.generate(input="audio.wav")
+print(result[0]["text"])
+
+ +
+
Paraformer-zh Large
+
220M params · Non-autoregressive · HuggingFace
+
Highest-accuracy Chinese speech recognition model. Non-autoregressive with CTC-guided attention, trained on 60,000+ hours of Mandarin speech. Includes built-in punctuation restoration and timestamp prediction.
+
+120x realtime +Chinese + Cantonese +Best accuracy +Timestamps +Punctuation +
+

When to use

+

Best for: Chinese-only applications where accuracy is the top priority — meeting transcription, subtitle generation, voice input, training data annotation.

+
from funasr import AutoModel
+model = AutoModel(
+    model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+    vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+    punc_model="iic/punc_ct-transformer_cn-en-common-vocab471067-large",
+)
+result = model.generate(input="audio.wav")
+print(result[0]["text"])
+
+ +

Supporting Models

+ +
+
cam++ (Speaker Diarization)
+
7.2M params · HuggingFace
+
Lightweight speaker embedding model for speaker diarization (who spoke when) and speaker verification (is this the same person). Only 7.2M parameters — runs on CPU in realtime.
+
+7.2M params +Diarization +Verification +CPU realtime +
+
+ +
+
FSMN-VAD
+
Built-in · Voice Activity Detection
+
Feedforward Sequential Memory Network for Voice Activity Detection. Accurately detects speech segments in audio, handling silence, noise, and music. Used as a preprocessing step for all ASR models.
+
+VAD +Lightweight +
+
+ +
+
CT-Transformer (Punctuation)
+
Built-in · Punctuation Restoration
+
Automatically adds punctuation to ASR output — commas, periods, question marks, etc. Supports Chinese and English. Dramatically improves readability of transcription output.
+
+Punctuation +zh + en +
+
+ +

OpenAI-Compatible API

+ +

All models are available through funasr-server, which exposes an OpenAI-compatible /v1/audio/transcriptions endpoint:

+ +
# Start the server
+pip install funasr vllm fastapi uvicorn python-multipart
+funasr-server --device cuda --port 8000
+
+# Use with any OpenAI-compatible client
+curl http://localhost:8000/v1/audio/transcriptions \
+  -F file=@audio.wav \
+  -F model=SenseVoiceSmall
+ +
+Drop-in replacement: Any application using OpenAI's Whisper API can switch to FunASR by changing the base URL. No code changes needed — same API format, same response structure. +
+ +

Deployment Options

+ + + + + + + + + + + +
MethodCommandBest For
pippip install funasr && funasr-serverDevelopment, quick testing
Dockerdocker run -d --gpus all -p 8000:8000 ...Production deployment
Python APIfrom funasr import AutoModelEmbedding in applications
ONNXVia Sherpa-ONNXMobile, edge, browser
+ +
+
+ + + + diff --git a/web-pages/product-site/legacy/en/quickstart.html b/web-pages/product-site/legacy/en/quickstart.html new file mode 100644 index 0000000000..6d8de4fd5f --- /dev/null +++ b/web-pages/product-site/legacy/en/quickstart.html @@ -0,0 +1,380 @@ + + + + + +FunASR Quickstart Guide | Speech Recognition in Python — Get Started in 3 Minutes + + + + + + + + + + + + + + + + + + + + + +
+

Quickstart Guide

+

Master the three core workflows of FunASR in under 3 minutes: file transcription, real-time streaming, and API server deployment. Every code snippet is ready to copy and run.

+
+Python 3.8+ +GPU 8GB+ (optional, CPU works too) +Linux / macOS / Windows +
+
+ + +
+
+

0
Installation

+

One command to install everything. GPU users should add vllm for a 16x speed boost.

+
+# Basic install (works on both CPU and GPU)
+$ pip install funasr
+
+# Full install (API server + vLLM acceleration, recommended for GPU)
+$ pip install funasr vllm fastapi uvicorn python-multipart +
+
+Tip +If you just need a one-off transcription, pip install funasr is enough. For deploying an API server or maximum throughput, go with the full install. +
+
+
+ + +
+
+

1
File Transcription

+

Pass in an audio file, get back the full transcript. Supports 50+ languages with built-in emotion and audio event detection.

+ +
+from funasr import AutoModel
+
+# Fun-ASR-Nano: flagship LLM-ASR for zh/en/ja + Chinese dialects/accents (GPU)
+model = AutoModel(model="FunAudioLLM/Fun-ASR-Nano-2512", device="cuda")
+result = model.generate(input="meeting.wav")
+print(result[0]["text"])
+
+# For 31 languages use the separate checkpoint: FunAudioLLM/Fun-ASR-MLT-Nano-2512
+# On CPU, use SenseVoice: AutoModel(model="iic/SenseVoiceSmall", vad_model="fsmn-vad") +
+ +
+Output +Let's discuss the three topics today. Sounds good. First one is the Q3 plan. Go ahead, we have 30 minutes. +
+ +
+# Batch-process multiple files at once
+results = model.generate(input=["file1.wav", "file2.mp3", "file3.flac"])
+for r in results:
+    print(r["text"]) +
+ +
+Supported Formats +WAV, MP3, FLAC, AAC, OGG, MP4 video -- virtually every common audio and video format works out of the box, no manual conversion needed. +
+
+
+ + +
+
+

2
Real-time Streaming ASR

+

Feed audio from a microphone or stream and get results as you speak. Perfect for live captions, broadcast transcription, and voice assistants.

+ +
+Which model +The flagship Fun-ASR-Nano (LLM-ASR) is an offline model and the default for file transcription and the OpenAI-compatible API (sections 1 & 3). For true low-latency, speak-as-you-go streaming, FunASR ships a dedicated streaming model, paraformer-zh-streaming — shown below. One industrial-grade FunASR stack; pick the model per use case. +
+ +
+from funasr import AutoModel
+
+# Paraformer streaming model
+model = AutoModel(model="paraformer-zh-streaming", vad_model="fsmn-vad")
+
+# Streaming inference: chunk_size = [5, 10, 5] means 600ms lookahead
+chunk_size = [5, 10, 5]
+cache = {}
+
+# Simulate streaming input (replace with mic capture in production)
+import soundfile as sf
+speech, sr = sf.read("meeting.wav")
+chunk_stride = chunk_size[1] * 960 # samples per chunk
+
+for i in range(0, len(speech), chunk_stride):
+    chunk = speech[i:i+chunk_stride]
+    is_final = (i + chunk_stride >= len(speech))
+    result = model.generate(
+        input=chunk, cache=cache,
+        is_final=is_final, chunk_size=chunk_size
+    )
+    print(result[0]["text"], end="", flush=True) +
+ +
+Live output (updates progressively) +Let's discuss the...three topics today. Sounds good. First one is...the Q3 plan. +
+ +
+WebSocket Real-time Service +Need browser-based live transcription? FunASR ships with a complete WebSocket server solution. Check the GitHub repository under examples/ for the WebSocket server and browser client code. +
+
+
+ + +
+
+

3
OpenAI-compatible API Server

+

Deploy an OpenAI-compatible speech recognition API with a single command. Drop-in replacement for any app already using the Whisper API.

+ +
+# Install and start the server
+$ pip install funasr vllm fastapi uvicorn python-multipart
+$ funasr-server --device cuda
+
+# Server starts on port 8899 by default +
+ +
+# Option 1: curl
+$ curl -X POST http://localhost:8899/v1/audio/transcriptions \
+    -F "file=@audio.wav" \
+    -F "model=iic/SenseVoiceSmall" +
+ +
+# Option 2: OpenAI Python SDK (identical to Whisper API)
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8899/v1", api_key="x")
+result = client.audio.transcriptions.create(
+    model="fun-asr-nano",
+    file=open("audio.wav", "rb"),
+    response_format="verbose_json"
+)
+print(result.text) +
+ +
+JSON Response +{"text": "Let's discuss the three topics today.", "segments": [{"start": 1.7, "end": 5.5, "text": "Let's discuss the three topics today."}], "duration": 12.1} +
+
+
+ + +
+
+

+
MCP Server: AI Assistant Integration

+

With MCP enabled, AI assistants like Claude, Cursor, and Windsurf can call FunASR directly for speech recognition.

+ +
+# Start the server with MCP support
+$ funasr-server --device cuda --enable-mcp
+
+# AI assistants can now invoke speech recognition via MCP protocol
+# Works with Claude Desktop, Cursor, Windsurf, and more +
+ +
+What is MCP? +MCP (Model Context Protocol) is a standard protocol for AI assistants to invoke external tools. Once enabled, your AI coding assistant can directly "listen to" audio files, unlocking voice-driven development workflows. +
+
+
+ + +
+
+

Next Steps

+

Explore more of what FunASR can do

+
+
+ +
⭐
+

GitHub Repository

Source code, examples, and issue tracker

+ + +
🧠
+

Model Gallery

Browse all available models and their capabilities

+ + +
🔗
+

Ecosystem

FunClip, CosyVoice, and companion tools

+ + +
⚡
+

vs Whisper

Performance, accuracy, and feature comparison

+ +
+
+ + + + + + + diff --git a/web-pages/product-site/legacy/en/vs-whisper.html b/web-pages/product-site/legacy/en/vs-whisper.html new file mode 100644 index 0000000000..7414374687 --- /dev/null +++ b/web-pages/product-site/legacy/en/vs-whisper.html @@ -0,0 +1,172 @@ + + + + + +FunASR vs Whisper — Open Source Speech Recognition Comparison 2026 + + + + + + + + + + + + + + + + + + + +
+ +

FunASR vs Whisper: Which Open Source ASR Should You Use?

+

Both FunASR and OpenAI Whisper are open-source speech recognition tools. Here's a detailed comparison to help you choose the right one for your use case.

+ +

Speed Comparison

+

Tested on 184 long-form audio files (192 minutes total). Higher RTF = faster.

+ + + + + + + + + + +
ModelGPU SpeedCPU Speedvs Whisper-large-v3
FunASR Fun-ASR-Nano (vLLM)340x realtime—26x faster
FunASR SenseVoice-Small170x realtime17x realtime13x faster
FunASR Paraformer-Large120x realtime15x realtime9x faster
Whisper-large-v3-turbo46x realtime❌ Too slow3.4x faster
Whisper-large-v313x realtime❌baseline
+ +

Key takeaway: FunASR models run on CPU faster than Whisper runs on GPU.

+ +

Feature Comparison

+ + + + + + + + + + + + + + + + + + + +
FeatureFunASRWhisper
Languages50+ (SenseVoice) / 31 (MLT-Nano) / zh-en-ja + Chinese dialects/accents (Nano)57
Speaker Diarization✅ Built-in (cam++)❌ Needs pyannote
Emotion Detection✅ Happy/Sad/Angry/Neutral❌
Audio Event Detection✅ Music, applause, laughter❌
Streaming / Real-time✅ WebSocket + vLLM❌
Hotwords / Boosting✅ Custom vocabulary❌
Chinese Dialects7 dialects + 26 accentsLimited
OpenAI-compatible API✅ funasr-serverSeparate wrapper needed
VAD (Voice Activity)✅ Built-in❌ External
Punctuation✅ Built-inPartial
CPU Inference✅ 17x realtime❌ Impractical
Fine-tuning✅ DeepSpeed scriptsCommunity scripts
LicenseMITMIT
CostFree (self-hosted)Free (self-hosted)
+ +

When to Choose FunASR

+
    +
  • You need speaker diarization without extra tools
    • +
  • You need real-time streaming transcription
    • +
  • You process Chinese, Japanese, or Asian languages
    • +
  • You need CPU-viable deployment (edge, cost-sensitive)
    • +
  • You want an OpenAI-compatible API for AI agents
    • +
  • You need emotion detection or audio event classification
    • +
  • You have high-throughput batch workloads
    • +
+ +

When to Choose Whisper

+
    +
  • You need the absolute widest language coverage (57 languages)
    • +
  • You're already integrated with the OpenAI ecosystem
    • +
  • Your workload is small enough that speed doesn't matter
    • +
+ +

Quick Start

+
pip install funasr
+
+from funasr import AutoModel
+
+# One-line transcription with speaker diarization
+model = AutoModel(
+    model="iic/SenseVoiceSmall",
+    vad_model="fsmn-vad",
+    spk_model="cam++",
+    device="cuda"  # or "cpu"
+)
+result = model.generate(input="meeting.wav")
+
+ +
+

Ready to try FunASR?

+

The FunASR open-source community keeps growing. Try it and join the project.

+View on GitHub ★ +
+ +

Migration Guide

+

Already using Whisper? We have a detailed migration guide that covers feature mapping, evaluation methodology, and deployment options.

+ +

Related Projects

+ + + + + + + + + +
ProjectBest ForLink
FunASRFull-featured toolkit (all models)GitHub
Fun-ASR-NanoFlagship LLM-ASR for zh/en/ja + Chinese dialects/accentsGitHub
Fun-ASR-MLT-NanoSeparate multilingual checkpoint for 31 languagesGitHub
SenseVoiceUltra-fast ASR + emotion + eventsGitHub
FunClipAI video clipping with ASRGitHub
+ +
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