Extend key entities metric to audio-native models

[weiz9]

What

Extends the existing transcription_accuracy_key_entities metric to support audio-native pipelines (S2S / AUDIO_LLM). It stays a single text-to-text judge — no new metric class.

Previously the metric was CASCADE-only: it compared what the user was instructed to say (intended_user_turns) against what the agent's STT transcribed (transcribed_user_turns). Audio-native models expose no reliable STT transcript, so there was nothing to compare against.

How

The metric now branches on pipeline type:

• CASCADE (unchanged): compares intended_user_turns vs transcribed_user_turns (STT output) — transcription accuracy.
• Audio-native (new): compares the user's intended entities against the agent's tool-call arguments (from conversation_trace), which are the evidence of what the model actually understood. If the user says "my id is EM123" and the agent calls a tool with "EMP124", the entity is flagged as misheard.

Both paths reuse the same per-entity ratio scoring (correct / total) and per-entity-type sub-metrics. Audio-native uses a new audio_native_prompt judge template; the cascade user_prompt is untouched.

Changes

• transcription_accuracy_key_entities.py: removed the CASCADE-only restriction (now all pipeline types), added _build_prompt_and_turns() to select source + prompt via context.is_audio_native, plus _get_audio_native_turns() / _format_intended_user_turns(). Bumped metric version v0.3 → v0.4.
• configs/prompts/judge.yaml: added audio_native_prompt under the same metric key.
• Tests: added TestPipelineSupport and TestAudioNativeCompute (misheard → 0, correct → 1, verifies the audio-native prompt is used with tool-call evidence).
• Bumped metrics_version 2.2.0 → 2.3.0 and regenerated metric_signatures.json.

Verification

• All 423 metrics unit tests pass, including the drift/signature test.
• ruff check + format clean.

Notes

• Signature coverage gap: the drift signature only hashes judge.{name}.user_prompt, so future text edits to the new audio_native_prompt won't auto-trip the drift test — they'd need a manual version bump. Happy to extend signatures.py to hash all prompt keys if preferred.
• Pre-existing mypy errors in this file (MetricScore pydantic defaults under strict) are unchanged; the new code adds none.