feat: add Qwen3-ASR engine with timestamp support and unit tests#16
feat: add Qwen3-ASR engine with timestamp support and unit tests#16
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app/engines/stt/qwen3asr/engine.py
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| # Process with Qwen3-ASR | ||
| processing_start = time.time() | ||
| results = self._model.transcribe( |
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this model not supported stream ?
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Pull request overview
Adds a new STT provider to VoiceCore by integrating a Qwen3-ASR engine (batch + streaming) with optional word-level timestamp extraction, plus configuration and a dedicated unit test suite.
Changes:
- Introduces
Qwen3ASREngineandQwen3ASRConfigunderapp/engines/stt/qwen3asr/. - Adds a
qwen3-asruv dependency group and a sampleengines.yamlentry. - Adds unit tests covering config validation, lifecycle, batch transcription, streaming, and metrics.
Reviewed changes
Copilot reviewed 6 out of 8 changed files in this pull request and generated 11 comments.
Show a summary per file
| File | Description |
|---|---|
app/engines/stt/qwen3asr/engine.py |
New Qwen3-ASR STT engine implementation (batch + streaming, metrics, optional timestamps). |
app/engines/stt/qwen3asr/config.py |
New Pydantic config model for Qwen3-ASR/vLLM and streaming parameters. |
app/engines/stt/qwen3asr/__init__.py |
Exports the new engine and config. |
engines.yaml |
Adds a sample Qwen3-ASR engine configuration entry. |
pyproject.toml |
Adds a qwen3-asr dependency group for qwen-asr[vllm]. |
tests/unit/engines/stt/qwen3asr/test_qwen3asr_engine.py |
New unit tests for Qwen3-ASR engine behavior and timestamps. |
tests/unit/engines/stt/qwen3asr/__init__.py |
Test package marker for the new unit test directory. |
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| ), | ||
| time_to_first_token_ms=time_to_first_token_ms, | ||
| total_stream_duration_ms=total_duration_ms, | ||
| total_chunks=int(len(audio_array) / chunk_size_samples) + 1, |
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total_chunks is computed as int(len(audio_array) / chunk_size_samples) + 1, which is off by one when len(audio_array) is an exact multiple of chunk_size_samples. Track the actual number of yielded STTChunks (or use a ceiling division) to avoid incorrect metrics.
| total_chunks=int(len(audio_array) / chunk_size_samples) + 1, | |
| total_chunks=(len(audio_array) + chunk_size_samples - 1) // chunk_size_samples, |
| yield STTResponse( | ||
| text=final_text, | ||
| language=detected_language, | ||
| segments=None, # Streaming doesn't return full segments list usually | ||
| performance_metrics=metrics, | ||
| ) |
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The final streaming STTResponse hard-codes segments=None, but the PR description and the new unit tests expect word-level timestamps in streaming mode. Either populate segments when forced_aligner/timestamps are enabled, or update the tests/docs to reflect that streaming does not return segments.
| from unittest.mock import MagicMock, patch | ||
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| import numpy as np | ||
| import pytest |
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These unit tests patch qwen_asr.Qwen3ASRModel, which will raise ModuleNotFoundError if the optional qwen-asr dependency group isn’t installed (e.g., after uv sync --dev). Add a module-level pytest.importorskip("qwen_asr", ...) (or similar guarding) so the overall test suite can run without optional deps.
| import pytest | |
| import pytest | |
| pytest.importorskip("qwen_asr", reason="qwen-asr package is not installed") |
| async def test_transcribe_stream_returns_segments_with_timestamps( | ||
| self, qwen3asr_config, mock_qwen3asr_model | ||
| ): | ||
| """Should extract and return segments in streaming mode""" | ||
| from app.engines.stt.qwen3asr.engine import Qwen3ASREngine | ||
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| engine = Qwen3ASREngine(qwen3asr_config) | ||
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| mock_result = MagicMock() | ||
| mock_result.text = "Hello world" | ||
| mock_result.language = "English" | ||
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| # Mock qwen-asr ForcedAlignItem mocks | ||
| item1 = MagicMock() | ||
| item1.text = "Hello" | ||
| item1.start_time = 0.0 | ||
| item1.end_time = 0.5 | ||
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| item2 = MagicMock() | ||
| item2.text = "world" | ||
| item2.start_time = 0.5 | ||
| item2.end_time = 1.0 | ||
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| mock_result.time_stamps = [item1, item2] | ||
| mock_qwen3asr_model.transcribe.return_value = [mock_result] | ||
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| with patch.object(engine, "_audio_processor") as mock_processor: | ||
| mock_processor.to_numpy.return_value = (np.array([0.1, 0.2]), 16000) | ||
| mock_processor.get_duration_ms.return_value = 1000.0 | ||
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| chunks = [] | ||
| async for item in engine.transcribe_stream(np.array([0.1, 0.2])): | ||
| chunks.append(item) | ||
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| final_response = chunks[-1] | ||
| assert final_response.segments is not None | ||
| assert len(final_response.segments) == 2 | ||
| assert final_response.segments[0].text == "Hello" | ||
| assert final_response.segments[0].start == 0.0 | ||
| assert final_response.segments[0].end == 0.5 | ||
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Qwen3ASREngine.transcribe_stream() never calls self._model.transcribe() and the implementation currently yields a final STTResponse with segments=None. This test sets up mock_qwen3asr_model.transcribe.return_value and then asserts final_response.segments contains timestamps, which will not be true with the current streaming implementation. Update the test to reflect actual streaming behavior, or extend the engine to extract/return segments in streaming mode.
| ) | ||
| except ImportError as e: | ||
| raise EngineNotReadyError( | ||
| "qwen-asr package not installed. Run: pip install qwen-asr[vllm]" |
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The ImportError message suggests pip install qwen-asr[vllm], but this repo’s install flow uses uv dependency groups (see engines.yaml / pyproject.toml). Consider updating the message to also mention uv sync --group qwen3-asr so users follow the project’s standard setup path.
| "qwen-asr package not installed. Run: pip install qwen-asr[vllm]" | |
| "qwen-asr package not installed. Install via: uv sync --group qwen3-asr (recommended) or pip install qwen-asr[vllm]" |
| async def transcribe( | ||
| self, audio_data: AudioInput, language: str | None = None, **kwargs | ||
| ) -> STTResponse: |
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**kwargs is accepted but never used. If callers pass engine-specific params via engine_params, they’ll be silently ignored. Consider forwarding supported kwargs into self._model.transcribe(...) (and/or streaming calls) or remove **kwargs to avoid a misleading API.
| # Capture first token time | ||
| if first_token_time is None: | ||
| first_token_time = time.time() | ||
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In streaming mode, first_token_time is set on the first processed chunk regardless of whether any text was produced. This makes time_to_first_token_ms inaccurate when initial chunks yield empty text; align with the Whisper engine behavior by setting this timestamp only when state.text first becomes non-empty (or changes from empty).
| chunk_latency_ms = (time.time() - processing_start) * 1000 | ||
| yield STTChunk( | ||
| text=current_text, | ||
| timestamp=float(pos) / sample_rate, | ||
| confidence=None, | ||
| chunk_latency_ms=chunk_latency_ms, | ||
| ) |
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chunk_latency_ms is computed from processing_start, so it grows cumulatively over the stream. The STTChunk.chunk_latency_ms field is described as per-chunk latency; measure just the time spent processing the current chunk (similar to the Whisper streaming implementation).
| qwen3-asr: | ||
| enabled: true | ||
| engine_class: "app.engines.stt.qwen3asr.engine.Qwen3ASREngine" |
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This config enables qwen3-asr by default. Since qwen-asr is in an optional dependency group, startup will log an initialization error (and the engine won’t register) unless users install the extra group. Consider setting enabled: false by default and leaving this as a sample config users can opt into.
| gt=0, | ||
| description="Maximum batch size for inference. -1 for unlimited.", | ||
| ) |
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max_inference_batch_size is documented as supporting -1 for unlimited, but the field is validated with gt=0, which rejects -1. Either allow -1 in validation (e.g., ge=-1 with a custom validator) or adjust the description/tests to match the enforced constraint.
| gt=0, | |
| description="Maximum batch size for inference. -1 for unlimited.", | |
| ) | |
| ge=-1, | |
| description="Maximum batch size for inference. -1 for unlimited.", | |
| ) | |
| @field_validator("max_inference_batch_size") | |
| @classmethod | |
| def validate_max_inference_batch_size(cls, v: int) -> int: | |
| """ | |
| Ensure max_inference_batch_size is either -1 (for unlimited) or a positive integer. | |
| """ | |
| if v == -1 or v > 0: | |
| return v | |
| raise ValueError( | |
| "max_inference_batch_size must be -1 (for unlimited) or a positive integer" | |
| ) |
Description
Integrates the Qwen3-ASR engine into VoiceCore, supporting high-accuracy multilingual speech recognition (52 languages). This engine supports both batch and streaming transcription modes and includes word-level timestamp support via the Qwen3-ForcedAligner model.
Key changes:
app/engines/stt/qwen3asr/.AttributeErrorwhen extracting timestamps fromForcedAlignItem(theqwen-asrlibrary updated these from dictionaries to dataclasses).engines.yamlwith a sample configuration for Qwen3-ASR.Type of Change
Checklist
make format)make lint)make test)Related Issues
Closes #
Testing & Verification
Automated Tests
tests/unit/engines/stt/qwen3asr/test_qwen3asr_engine.py(42 tests passed)make test).Manual Verification
Verified on a GPU-enabled environment:
AttributeErroris resolved).Segmentobjects.API Endpoints Tested
POST /api/v1/stt/transcribe)POST /api/v1/stt/transcribe/stream)Engine-Specific Tests
Security Impact