feat(diarization): добавлено разделение транскрипта по говорящим
Зачем: - локальным транскриптам нужна структура реплик для конспектов и протоколов. Что: - добавлены пословные таймкоды для всех ASR-бэкендов и сведение с Sherpa-ONNX. - реализованы CLI-флаги, деградация без потери ASR и speaker Markdown. - добавлены проверяемый кеш моделей, тесты и документация. Проверка: - `pytest` — 283 passed, 1 skipped. - `pyright` — 0 errors. - Ruff и `git diff --check` — без ошибок. - выполнены три контрольных прогона на реальных записях.
This commit is contained in:
Dmitriy Dementiev
+166
-9
@@ -5,16 +5,18 @@ import warnings
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import pytest
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from local_transcriber.backends.onnx_asr import OnnxAsrBackend
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from local_transcriber.types import UNKNOWN_LANGUAGE, Segment, TranscribeResult
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from local_transcriber.types import UNKNOWN_LANGUAGE, Segment, TranscribeResult, Word
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class FakeVadSegment:
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"""Mimics onnx-asr SegmentResult."""
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def __init__(self, start, end, text):
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def __init__(self, start, end, text, tokens=None, timestamps=None):
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self.start = start
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self.end = end
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self.text = text
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self.tokens = [f" {text}"] if tokens is None else tokens
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self.timestamps = [0.0] if timestamps is None else timestamps
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class TestEnsureModelAvailable:
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@@ -59,6 +61,9 @@ class TestEnsureModelAvailable:
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def with_vad(self, vad):
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return self
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def with_timestamps(self):
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return self
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def fake_load_model(*, model, quantization):
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quantizations.append(quantization)
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return FakeAsrAdapter()
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@@ -82,21 +87,45 @@ class TestEnsureModelAvailable:
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class TestCreateModel:
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def test_wraps_vad_model_with_timestamps(self, monkeypatch):
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timestamped_model = object()
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class FakeVadAdapter:
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def with_timestamps(self):
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return timestamped_model
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class FakeAsrAdapter:
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def with_vad(self, vad):
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return FakeVadAdapter()
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monkeypatch.setattr("onnx_asr.load_model", lambda **kwargs: FakeAsrAdapter())
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monkeypatch.setattr("onnx_asr.load_vad", lambda model: object())
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model = OnnxAsrBackend().create_model("gigaam-v3-e2e-rnnt", "onnx", "int8")
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assert model is timestamped_model
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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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**kwargs):
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calls.append({
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"model": model, "path": path, "quantization": quantization,
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})
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def fake_load_model(model=None, path=None, quantization=None, **kwargs):
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calls.append(
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{
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"model": model,
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"path": path,
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"quantization": quantization,
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}
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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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def with_timestamps(self):
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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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@@ -123,6 +152,9 @@ class TestCreateModel:
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self._vad = vad
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return self
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def with_timestamps(self):
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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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@@ -143,6 +175,9 @@ class TestCreateModel:
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def with_vad(self, vad):
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return self
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def with_timestamps(self):
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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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@@ -167,6 +202,9 @@ class TestCreateModel:
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def with_vad(self, vad):
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return self
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def with_timestamps(self):
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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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@@ -187,6 +225,9 @@ class TestCreateModel:
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def with_vad(self, vad):
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return self
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def with_timestamps(self):
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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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@@ -207,6 +248,9 @@ class TestCreateModel:
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def with_vad(self, vad):
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return self
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def with_timestamps(self):
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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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@@ -298,6 +342,7 @@ class TestTranscribe:
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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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@@ -310,7 +355,9 @@ class TestTranscribe:
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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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FakeModel(),
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wav_file,
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language=None,
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)
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assert isinstance(result, TranscribeResult)
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@@ -319,6 +366,110 @@ class TestTranscribe:
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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_converts_vad_token_timestamps_to_global_words(
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self, monkeypatch, tmp_path
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):
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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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monkeypatch.setattr(
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"faster_whisper.decode_audio",
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lambda path, sampling_rate=16000: [0.0] * 16_000,
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)
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timestamped_segment = FakeVadSegment(
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10.0,
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12.0,
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"Привет, мир",
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tokens=[" ", "П", "р", "и", "в", "е", "т", ",", " ", "м", "и", "р"],
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timestamps=[0.0, 0.1, 0.1, 0.1, 0.2, 0.2, 0.3, 0.3, 0.5, 0.6, 0.6, 0.7],
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)
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class FakeModel:
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def recognize(self, waveform, sample_rate, language=None):
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yield timestamped_segment
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result = OnnxAsrBackend().transcribe(FakeModel(), wav_file, language="ru")
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assert result.words == [
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Word(start=10.0, end=10.5, text=" Привет,"),
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Word(start=10.5, end=12.0, text=" мир"),
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]
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assert "".join(word.text for word in result.words).strip() == "Привет, мир"
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def test_transcribe_rejects_nonempty_segment_without_token_timestamps(
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self, monkeypatch, tmp_path
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):
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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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monkeypatch.setattr(
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"faster_whisper.decode_audio",
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lambda path, sampling_rate=16000: [0.0] * 16_000,
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)
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segment = FakeVadSegment(0.0, 1.0, "Текст")
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segment.tokens = None
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segment.timestamps = None
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class FakeModel:
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def recognize(self, waveform, sample_rate, language=None):
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yield segment
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with pytest.raises(RuntimeError, match="пословные таймкоды"):
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OnnxAsrBackend().transcribe(FakeModel(), wav_file, language="ru")
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def test_transcribe_keeps_words_with_equal_emission_timestamps(
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self, monkeypatch, tmp_path
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):
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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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monkeypatch.setattr(
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"faster_whisper.decode_audio",
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lambda path, sampling_rate=16000: [0.0] * 16_000,
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)
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segment = FakeVadSegment(
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10.0,
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12.0,
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"Да нет потом",
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tokens=[" ", "Да", " ", "нет", " ", "потом"],
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timestamps=[0.0, 0.0, 0.0, 0.0, 0.5, 0.5],
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)
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class FakeModel:
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def recognize(self, waveform, sample_rate, language=None):
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yield segment
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result = OnnxAsrBackend().transcribe(FakeModel(), wav_file, language="ru")
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assert [word.text for word in result.words] == [" Да", " нет", " потом"]
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assert [(word.start, word.end) for word in result.words] == [
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(10.0, 10.5),
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(10.0, 10.5),
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(10.5, 12.0),
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]
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assert "".join(word.text for word in result.words).strip() == segment.text
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def test_transcribe_keeps_word_clamped_to_segment_end(self, monkeypatch, tmp_path):
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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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monkeypatch.setattr(
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"faster_whisper.decode_audio",
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lambda path, sampling_rate=16000: [0.0] * 16_000,
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)
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segment = FakeVadSegment(
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10.0,
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12.0,
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"Позднее",
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tokens=[" ", "Позднее"],
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timestamps=[2.0, 2.0],
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)
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class FakeModel:
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def recognize(self, waveform, sample_rate, language=None):
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yield segment
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result = OnnxAsrBackend().transcribe(FakeModel(), wav_file, language="ru")
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assert result.words == [Word(start=12.0, end=12.0, text=" Позднее")]
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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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@@ -326,6 +477,7 @@ class TestTranscribe:
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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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@@ -339,7 +491,9 @@ class TestTranscribe:
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backend = OnnxAsrBackend()
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backend.transcribe(
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FakeModel(), wav_file, language=None,
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FakeModel(),
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wav_file,
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language=None,
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on_segment=lambda s: segments_captured.append(s),
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)
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@@ -354,6 +508,7 @@ class TestTranscribe:
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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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@@ -377,6 +532,7 @@ class TestTranscribe:
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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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@@ -419,6 +575,7 @@ class TestTranscribe:
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class TestBackendRegistration:
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def test_get_backend_returns_onnx_backend(self):
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from local_transcriber.backends import get_backend
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backend = get_backend("onnx")
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assert isinstance(backend, OnnxAsrBackend)
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