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9e04dc8c25
- Зачем: - Основной метод бэкенда — транскрипция аудиофайла в сегменты с временными метками. - Что: - метод transcribe декодирует аудио через faster_whisper.decode_audio, затем вызывает model.recognize() с VAD-сегментацией. - каждый VAD-сегмент преобразуется в проектную структуру Segment. - поддержка колбэков on_segment, on_status. - написаны 4 теста: сбор сегментов, вызов on_segment, передача языка, обработка пустого аудио. - Проверка: - uv run pytest tests/test_onnx_asr.py -v (14 passed)
237 lines
8.0 KiB
Python
237 lines
8.0 KiB
Python
"""Tests for onnx-asr backend."""
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import pytest
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from pathlib import Path
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from local_transcriber.backends.onnx_asr import OnnxAsrBackend, MODEL_ALIASES
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from local_transcriber.types import Segment, TranscribeResult
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class FakeVadSegment:
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"""Mimics onnx-asr SegmentResult."""
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def __init__(self, start_ts, end_ts, text):
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self.start_ts = start_ts
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self.end_ts = end_ts
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self.text = text
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class TestEnsureModelAvailable:
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def test_returns_model_id_for_gigaam(self):
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backend = OnnxAsrBackend()
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result = backend.ensure_model_available("gigaam-v3", "int8")
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assert result == "gigaam-v3-ctc"
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def test_returns_model_id_for_parakeet(self):
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backend = OnnxAsrBackend()
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result = backend.ensure_model_available("parakeet-v3", "fp16")
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assert result == "nemo-parakeet-tdt-0.6b-v3"
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def test_stores_compute_type(self):
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backend = OnnxAsrBackend()
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backend.ensure_model_available("gigaam-v3", "float32")
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assert backend._resolved_model_id == "gigaam-v3-ctc"
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assert backend.actual_compute_type == "float32"
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class TestCreateModel:
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def test_calls_load_model_with_correct_args(self, monkeypatch):
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"""Verify create_model passes correct args to onnx_asr.load_model."""
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calls = []
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def fake_load_model(model=None, path=None, quantization=None,
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cpu_preprocessing=None, **kwargs):
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calls.append({
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"model": model, "path": path, "quantization": quantization,
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"cpu_preprocessing": cpu_preprocessing,
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})
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return FakeAsrAdapter()
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class FakeAsrAdapter:
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def with_vad(self, vad):
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return self
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monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
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backend = OnnxAsrBackend()
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backend.actual_compute_type = "int8"
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model = backend.create_model("gigaam-v3-ctc", "onnx", "int8")
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assert len(calls) == 1
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assert calls[0]["quantization"] == "int8"
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assert calls[0]["cpu_preprocessing"] is True
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assert model is not None
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def test_loads_silero_vad(self, monkeypatch):
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"""Verify Silero VAD is loaded and attached to model."""
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vad_calls = []
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def fake_load_vad(model, **kwargs):
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vad_calls.append(model)
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return "fake_vad"
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def fake_load_model(**kwargs):
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return FakeAsrAdapter()
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class FakeAsrAdapter:
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def with_vad(self, vad):
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self._vad = vad
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return self
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monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
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monkeypatch.setattr("onnx_asr.load_vad", fake_load_vad)
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backend = OnnxAsrBackend()
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model = backend.create_model("gigaam-v3-ctc", "onnx", "int8")
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assert vad_calls == ["silero"]
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def test_fp16_compute_type(self, monkeypatch):
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"""Verify fp16 compute_type is passed through."""
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calls = []
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def fake_load_model(model=None, quantization=None, **kwargs):
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calls.append(quantization)
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return FakeAsrAdapter()
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class FakeAsrAdapter:
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def with_vad(self, vad):
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return self
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monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
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monkeypatch.setattr("onnx_asr.load_vad", lambda model, **kw: None)
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backend = OnnxAsrBackend()
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backend.create_model("parakeet-v3", "onnx", "fp16")
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assert calls == ["fp16"]
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class TestTranscribe:
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def test_transcribe_collects_segments(self, monkeypatch, tmp_path):
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"""Verify transcribe maps VAD segments to project Segments."""
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wav_file = tmp_path / "test.wav"
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wav_file.write_bytes(b"fake audio")
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audio_samples = [0.0] * 16000 # 1 second of silence
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def fake_decode_audio(path, sampling_rate=16000):
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import numpy as np
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return np.array(audio_samples, dtype=np.float32)
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class FakeModel:
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def recognize(self, waveform, sample_rate, language=None):
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yield FakeVadSegment(0.0, 1.0, "hello")
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yield FakeVadSegment(1.0, 2.5, "world")
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monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
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backend = OnnxAsrBackend()
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backend.actual_compute_type = "int8"
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result = backend.transcribe(
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FakeModel(), wav_file, language=None,
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)
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assert isinstance(result, TranscribeResult)
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assert len(result.segments) == 2
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assert result.segments[0] == Segment(start=0.0, end=1.0, text="hello")
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assert result.segments[1] == Segment(start=1.0, end=2.5, text="world")
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assert result.duration == 1.0 # 16000 samples / 16000 Hz
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def test_transcribe_calls_on_segment(self, monkeypatch, tmp_path):
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"""Verify on_segment callback is invoked per segment."""
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wav_file = tmp_path / "test.wav"
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wav_file.write_bytes(b"fake audio")
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def fake_decode_audio(path, sampling_rate=16000):
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import numpy as np
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return np.array([0.0] * 16000, dtype=np.float32)
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segments_captured = []
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class FakeModel:
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def recognize(self, waveform, sample_rate, language=None):
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yield FakeVadSegment(0.0, 2.0, "one")
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yield FakeVadSegment(2.0, 4.0, "two")
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monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
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backend = OnnxAsrBackend()
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result = backend.transcribe(
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FakeModel(), wav_file, language=None,
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on_segment=lambda s: segments_captured.append(s),
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)
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assert len(segments_captured) == 2
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assert segments_captured[0].text == "one"
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assert segments_captured[1].text == "two"
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def test_transcribe_passes_language(self, monkeypatch, tmp_path):
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"""Verify language is passed to recognize()."""
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wav_file = tmp_path / "test.wav"
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wav_file.write_bytes(b"fake audio")
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def fake_decode_audio(path, sampling_rate=16000):
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import numpy as np
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return np.array([0.0] * 16000, dtype=np.float32)
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lang_received = []
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class FakeModel:
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def recognize(self, waveform, sample_rate, language=None):
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lang_received.append(language)
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yield FakeVadSegment(0.0, 1.0, "text")
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monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
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backend = OnnxAsrBackend()
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backend.transcribe(FakeModel(), wav_file, language="ru")
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assert lang_received == ["ru"]
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def test_transcribe_empty_audio(self, monkeypatch, tmp_path):
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"""Verify zero segments for silent audio."""
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wav_file = tmp_path / "test.wav"
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wav_file.write_bytes(b"fake audio")
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def fake_decode_audio(path, sampling_rate=16000):
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import numpy as np
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return np.array([0.0] * 16000, dtype=np.float32)
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class FakeModel:
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def recognize(self, waveform, sample_rate, language=None):
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# No segments yielded
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if False:
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yield
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monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
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backend = OnnxAsrBackend()
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result = backend.transcribe(FakeModel(), wav_file, language=None)
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assert len(result.segments) == 0
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assert result.language == "unknown"
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assert result.duration == 1.0
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class TestModelAliases:
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def test_gigaam_v3_resolves(self):
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backend = OnnxAsrBackend()
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result = backend._resolve_model("gigaam-v3")
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assert result == "gigaam-v3-ctc"
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def test_parakeet_v3_resolves(self):
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backend = OnnxAsrBackend()
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result = backend._resolve_model("parakeet-v3")
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assert result == "nemo-parakeet-tdt-0.6b-v3"
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def test_raw_name_passes_through(self):
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backend = OnnxAsrBackend()
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result = backend._resolve_model("nemo-canary-1b-v2")
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assert result == "nemo-canary-1b-v2"
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def test_unknown_alias_raises(self):
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backend = OnnxAsrBackend()
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with pytest.raises(ValueError, match="Неподдерживаемая модель"):
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backend._resolve_model("nonexistent-model")
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