Dmitriy Dementiev
9ae013ad0f
- Зачем: - подсказки в ошибке и предупреждении называли только OpenVINO и CUDA, которых нет на macOS и ARM, поэтому пользователю там предлагались заведомо нерабочие команды. - Что: - в оба сообщения добавлен вариант --device cpu, работающий на любой платформе. - для large-v3-turbo подсказки по cpu и cuda подставляют medium, так как faster-whisper turbo не поддерживает. - Проверка: - uv run pytest -q: 243 passed, 1 skipped.
483 lines
17 KiB
Python
483 lines
17 KiB
Python
"""Tests for onnx-asr backend."""
|
||
|
||
import warnings
|
||
|
||
import pytest
|
||
|
||
from local_transcriber.backends.onnx_asr import OnnxAsrBackend
|
||
from local_transcriber.types import UNKNOWN_LANGUAGE, Segment, TranscribeResult
|
||
|
||
|
||
class FakeVadSegment:
|
||
"""Mimics onnx-asr SegmentResult."""
|
||
|
||
def __init__(self, start, end, text):
|
||
self.start = start
|
||
self.end = end
|
||
self.text = text
|
||
|
||
|
||
class TestEnsureModelAvailable:
|
||
def test_returns_model_id_for_gigaam(self):
|
||
backend = OnnxAsrBackend()
|
||
result = backend.ensure_model_available("gigaam-v3", "int8")
|
||
assert result == "gigaam-v3-ctc"
|
||
|
||
def test_returns_model_id_for_parakeet(self):
|
||
backend = OnnxAsrBackend()
|
||
result = backend.ensure_model_available("parakeet-v3", "int8")
|
||
assert result == "nemo-parakeet-tdt-0.6b-v3"
|
||
|
||
def test_stores_compute_type(self):
|
||
backend = OnnxAsrBackend()
|
||
backend.ensure_model_available("gigaam-v3", "float32")
|
||
assert backend._resolved_model_id == "gigaam-v3-ctc"
|
||
assert backend.actual_compute_type == "float32"
|
||
|
||
@pytest.mark.parametrize(
|
||
"model_name",
|
||
[
|
||
"gigaam-multilingual-ctc",
|
||
"gigaam-multilingual-large-ctc",
|
||
"gigaam-v3-e2e-ctc",
|
||
"gigaam-v3-e2e-rnnt",
|
||
],
|
||
)
|
||
def test_explicit_unavailable_compute_type_is_rejected(self, model_name):
|
||
backend = OnnxAsrBackend(compute_type_explicit=True)
|
||
|
||
with pytest.raises(ValueError, match="недоступна с compute_type='fp16'"):
|
||
backend.ensure_model_available(model_name, "fp16")
|
||
|
||
def test_implicit_unavailable_compute_type_falls_back_and_reports(
|
||
self, monkeypatch
|
||
):
|
||
quantizations = []
|
||
statuses = []
|
||
|
||
class FakeAsrAdapter:
|
||
def with_vad(self, vad):
|
||
return self
|
||
|
||
def fake_load_model(*, model, quantization):
|
||
quantizations.append(quantization)
|
||
return FakeAsrAdapter()
|
||
|
||
monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
|
||
monkeypatch.setattr("onnx_asr.load_vad", lambda model: None)
|
||
|
||
backend = OnnxAsrBackend(compute_type_explicit=False)
|
||
model_id = backend.ensure_model_available(
|
||
"gigaam-v3-e2e-ctc",
|
||
"fp16",
|
||
on_status=statuses.append,
|
||
)
|
||
backend.create_model(model_id, "onnx", "fp16")
|
||
|
||
assert backend.actual_compute_type == "int8"
|
||
assert quantizations == ["int8"]
|
||
assert statuses == [
|
||
"Модель gigaam-v3-e2e-ctc недоступна с compute_type=fp16; использую int8."
|
||
]
|
||
|
||
|
||
class TestCreateModel:
|
||
def test_calls_load_model_with_correct_args(self, monkeypatch):
|
||
"""Verify create_model passes correct args to onnx_asr.load_model."""
|
||
calls = []
|
||
|
||
def fake_load_model(model=None, path=None, quantization=None,
|
||
**kwargs):
|
||
calls.append({
|
||
"model": model, "path": path, "quantization": quantization,
|
||
})
|
||
return FakeAsrAdapter()
|
||
|
||
class FakeAsrAdapter:
|
||
def with_vad(self, vad):
|
||
return self
|
||
|
||
monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.actual_compute_type = "int8"
|
||
model = backend.create_model("gigaam-v3-ctc", "onnx", "int8")
|
||
|
||
assert len(calls) == 1
|
||
assert calls[0]["quantization"] == "int8"
|
||
assert model is not None
|
||
|
||
def test_loads_silero_vad(self, monkeypatch):
|
||
"""Verify Silero VAD is loaded and attached to model."""
|
||
vad_calls = []
|
||
|
||
def fake_load_vad(model, **kwargs):
|
||
vad_calls.append(model)
|
||
return "fake_vad"
|
||
|
||
def fake_load_model(**kwargs):
|
||
return FakeAsrAdapter()
|
||
|
||
class FakeAsrAdapter:
|
||
def with_vad(self, vad):
|
||
self._vad = vad
|
||
return self
|
||
|
||
monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
|
||
monkeypatch.setattr("onnx_asr.load_vad", fake_load_vad)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.create_model("gigaam-v3-ctc", "onnx", "int8")
|
||
|
||
assert vad_calls == ["silero"]
|
||
|
||
def test_fp16_compute_type(self, monkeypatch):
|
||
"""Verify fp16 compute_type is passed through."""
|
||
calls = []
|
||
|
||
def fake_load_model(model=None, quantization=None, **kwargs):
|
||
calls.append(quantization)
|
||
return FakeAsrAdapter()
|
||
|
||
class FakeAsrAdapter:
|
||
def with_vad(self, vad):
|
||
return self
|
||
|
||
monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
|
||
monkeypatch.setattr("onnx_asr.load_vad", lambda model, **kw: None)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.create_model("parakeet-v3", "onnx", "fp16")
|
||
|
||
assert calls == ["fp16"]
|
||
|
||
def test_float32_maps_to_none(self, monkeypatch):
|
||
"""compute_type='float32' маппится в quantization=None (unquantized).
|
||
|
||
onnx-asr использует quantization как суффикс файла; для float32 нужен None,
|
||
строка "float32" приведёт к попытке загрузить несуществующий файл.
|
||
"""
|
||
calls = []
|
||
|
||
def fake_load_model(model=None, quantization="MISSING", **kwargs):
|
||
calls.append(quantization)
|
||
return FakeAsrAdapter()
|
||
|
||
class FakeAsrAdapter:
|
||
def with_vad(self, vad):
|
||
return self
|
||
|
||
monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
|
||
monkeypatch.setattr("onnx_asr.load_vad", lambda model, **kw: None)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.create_model("gigaam-v3-ctc", "onnx", "float32")
|
||
|
||
assert calls == [None]
|
||
|
||
def test_fp32_maps_to_none(self, monkeypatch):
|
||
"""compute_type='fp32' тоже маппится в quantization=None."""
|
||
calls = []
|
||
|
||
def fake_load_model(model=None, quantization="MISSING", **kwargs):
|
||
calls.append(quantization)
|
||
return FakeAsrAdapter()
|
||
|
||
class FakeAsrAdapter:
|
||
def with_vad(self, vad):
|
||
return self
|
||
|
||
monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
|
||
monkeypatch.setattr("onnx_asr.load_vad", lambda model, **kw: None)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.create_model("gigaam-v3-ctc", "onnx", "fp32")
|
||
|
||
assert calls == [None]
|
||
|
||
def test_float16_alias_maps_to_fp16(self, monkeypatch):
|
||
"""compute_type='float16' (CUDA-naming) маппится в onnx-asr 'fp16'."""
|
||
calls = []
|
||
|
||
def fake_load_model(model=None, quantization=None, **kwargs):
|
||
calls.append(quantization)
|
||
return FakeAsrAdapter()
|
||
|
||
class FakeAsrAdapter:
|
||
def with_vad(self, vad):
|
||
return self
|
||
|
||
monkeypatch.setattr("onnx_asr.load_model", fake_load_model)
|
||
monkeypatch.setattr("onnx_asr.load_vad", lambda model, **kw: None)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.create_model("gigaam-v3-ctc", "onnx", "float16")
|
||
|
||
assert calls == ["fp16"]
|
||
|
||
def test_unknown_compute_type_raises(self, monkeypatch):
|
||
"""Неподдерживаемый compute_type → ValueError, не silent fallback."""
|
||
monkeypatch.setattr("onnx_asr.load_model", lambda **kw: None)
|
||
monkeypatch.setattr("onnx_asr.load_vad", lambda model, **kw: None)
|
||
|
||
backend = OnnxAsrBackend()
|
||
with pytest.raises(ValueError, match="Неподдерживаемый compute_type"):
|
||
backend.create_model("gigaam-v3-ctc", "onnx", "int8_float32")
|
||
|
||
|
||
class TestTranscribe:
|
||
@pytest.mark.parametrize(
|
||
("model_name", "language", "expects_warning"),
|
||
[
|
||
("gigaam-v3-e2e-rnnt", "en", True),
|
||
("gigaam-v3-e2e-rnnt", "ru", False),
|
||
("gigaam-multilingual-ctc", "en", False),
|
||
],
|
||
)
|
||
def test_warns_when_language_is_not_supported(
|
||
self, monkeypatch, tmp_path, model_name, language, expects_warning
|
||
):
|
||
wav_file = tmp_path / "test.wav"
|
||
wav_file.write_bytes(b"fake audio")
|
||
|
||
monkeypatch.setattr(
|
||
"faster_whisper.decode_audio",
|
||
lambda path, sampling_rate=16000: [0.0] * 16000,
|
||
)
|
||
|
||
class FakeModel:
|
||
def recognize(self, waveform, sample_rate, language=None):
|
||
return iter(())
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.ensure_model_available(model_name, "int8")
|
||
|
||
if expects_warning:
|
||
with pytest.warns(
|
||
UserWarning,
|
||
match=(
|
||
r"Язык 'en'.*--device openvino-cpu --model medium.*"
|
||
r"--device cpu --model medium.*"
|
||
r"--device cuda --model medium"
|
||
),
|
||
):
|
||
backend.transcribe(FakeModel(), wav_file, language=language)
|
||
else:
|
||
with warnings.catch_warnings(record=True) as caught:
|
||
backend.transcribe(FakeModel(), wav_file, language=language)
|
||
assert caught == []
|
||
|
||
def test_auto_language_uses_single_supported_model_language(
|
||
self, monkeypatch, tmp_path
|
||
):
|
||
wav_file = tmp_path / "test.wav"
|
||
wav_file.write_bytes(b"fake audio")
|
||
monkeypatch.setattr(
|
||
"faster_whisper.decode_audio",
|
||
lambda path, sampling_rate=16000: [0.0] * 16000,
|
||
)
|
||
|
||
class FakeModel:
|
||
def recognize(self, waveform, sample_rate, language=None):
|
||
return iter(())
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.ensure_model_available("gigaam-v3-e2e-rnnt", "int8")
|
||
|
||
result = backend.transcribe(FakeModel(), wav_file, language=None)
|
||
|
||
assert result.language == "ru"
|
||
assert result.language_probability == 0.0
|
||
|
||
def test_transcribe_collects_segments(self, monkeypatch, tmp_path):
|
||
"""Verify transcribe maps VAD segments to project Segments."""
|
||
wav_file = tmp_path / "test.wav"
|
||
wav_file.write_bytes(b"fake audio")
|
||
|
||
audio_samples = [0.0] * 16000 # 1 second of silence
|
||
|
||
def fake_decode_audio(path, sampling_rate=16000):
|
||
import numpy as np
|
||
return np.array(audio_samples, dtype=np.float32)
|
||
|
||
class FakeModel:
|
||
def recognize(self, waveform, sample_rate, language=None):
|
||
yield FakeVadSegment(0.0, 1.0, "hello")
|
||
yield FakeVadSegment(1.0, 2.5, "world")
|
||
|
||
monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.actual_compute_type = "int8"
|
||
result = backend.transcribe(
|
||
FakeModel(), wav_file, language=None,
|
||
)
|
||
|
||
assert isinstance(result, TranscribeResult)
|
||
assert len(result.segments) == 2
|
||
assert result.segments[0] == Segment(start=0.0, end=1.0, text="hello")
|
||
assert result.segments[1] == Segment(start=1.0, end=2.5, text="world")
|
||
assert result.duration == 1.0 # 16000 samples / 16000 Hz
|
||
|
||
def test_transcribe_calls_on_segment(self, monkeypatch, tmp_path):
|
||
"""Verify on_segment callback is invoked per segment."""
|
||
wav_file = tmp_path / "test.wav"
|
||
wav_file.write_bytes(b"fake audio")
|
||
|
||
def fake_decode_audio(path, sampling_rate=16000):
|
||
import numpy as np
|
||
return np.array([0.0] * 16000, dtype=np.float32)
|
||
|
||
segments_captured = []
|
||
|
||
class FakeModel:
|
||
def recognize(self, waveform, sample_rate, language=None):
|
||
yield FakeVadSegment(0.0, 2.0, "one")
|
||
yield FakeVadSegment(2.0, 4.0, "two")
|
||
|
||
monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.transcribe(
|
||
FakeModel(), wav_file, language=None,
|
||
on_segment=lambda s: segments_captured.append(s),
|
||
)
|
||
|
||
assert len(segments_captured) == 2
|
||
assert segments_captured[0].text == "one"
|
||
assert segments_captured[1].text == "two"
|
||
|
||
def test_transcribe_passes_language(self, monkeypatch, tmp_path):
|
||
"""Verify language is passed to recognize()."""
|
||
wav_file = tmp_path / "test.wav"
|
||
wav_file.write_bytes(b"fake audio")
|
||
|
||
def fake_decode_audio(path, sampling_rate=16000):
|
||
import numpy as np
|
||
return np.array([0.0] * 16000, dtype=np.float32)
|
||
|
||
lang_received = []
|
||
|
||
class FakeModel:
|
||
def recognize(self, waveform, sample_rate, language=None):
|
||
lang_received.append(language)
|
||
yield FakeVadSegment(0.0, 1.0, "text")
|
||
|
||
monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
|
||
|
||
backend = OnnxAsrBackend()
|
||
backend.transcribe(FakeModel(), wav_file, language="ru")
|
||
|
||
assert lang_received == ["ru"]
|
||
|
||
def test_transcribe_empty_audio(self, monkeypatch, tmp_path):
|
||
"""Verify zero segments for silent audio."""
|
||
wav_file = tmp_path / "test.wav"
|
||
wav_file.write_bytes(b"fake audio")
|
||
|
||
def fake_decode_audio(path, sampling_rate=16000):
|
||
import numpy as np
|
||
return np.array([0.0] * 16000, dtype=np.float32)
|
||
|
||
class FakeModel:
|
||
def recognize(self, waveform, sample_rate, language=None):
|
||
# No segments yielded
|
||
if False:
|
||
yield
|
||
|
||
monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
|
||
|
||
backend = OnnxAsrBackend()
|
||
result = backend.transcribe(FakeModel(), wav_file, language=None)
|
||
|
||
assert len(result.segments) == 0
|
||
assert result.language == UNKNOWN_LANGUAGE
|
||
assert result.duration == 1.0
|
||
|
||
def test_transcribe_skips_zero_length_vad_segments(self, monkeypatch, tmp_path):
|
||
wav_file = tmp_path / "test.wav"
|
||
wav_file.write_bytes(b"fake audio")
|
||
|
||
def fake_decode_audio(path, sampling_rate=16000):
|
||
import numpy as np
|
||
|
||
return np.array([0.0] * 16000, dtype=np.float32)
|
||
|
||
class FakeModel:
|
||
def recognize(self, waveform, sample_rate, language=None):
|
||
yield FakeVadSegment(0.5, 0.5, "нулевой")
|
||
yield FakeVadSegment(0.75, 0.5, "обратный")
|
||
yield FakeVadSegment(0.5, 1.0, "валидный")
|
||
|
||
monkeypatch.setattr("faster_whisper.decode_audio", fake_decode_audio)
|
||
|
||
result = OnnxAsrBackend().transcribe(FakeModel(), wav_file, language=None)
|
||
|
||
assert result.segments == [Segment(start=0.5, end=1.0, text="валидный")]
|
||
|
||
|
||
class TestBackendRegistration:
|
||
def test_get_backend_returns_onnx_backend(self):
|
||
from local_transcriber.backends import get_backend
|
||
backend = get_backend("onnx")
|
||
assert isinstance(backend, OnnxAsrBackend)
|
||
|
||
def test_get_backend_preserves_implicit_compute_type(self):
|
||
from local_transcriber.backends import get_backend
|
||
|
||
backend = get_backend("onnx", compute_type_explicit=False)
|
||
backend.ensure_model_available("gigaam-v3-e2e-rnnt", "fp16")
|
||
|
||
assert backend.actual_compute_type == "int8"
|
||
|
||
|
||
class TestModelAliases:
|
||
def test_gigaam_v3_resolves(self):
|
||
backend = OnnxAsrBackend()
|
||
result = backend._resolve_model("gigaam-v3")
|
||
assert result == "gigaam-v3-ctc"
|
||
|
||
def test_parakeet_v3_resolves(self):
|
||
backend = OnnxAsrBackend()
|
||
result = backend._resolve_model("parakeet-v3")
|
||
assert result == "nemo-parakeet-tdt-0.6b-v3"
|
||
|
||
def test_raw_name_passes_through(self):
|
||
backend = OnnxAsrBackend()
|
||
result = backend._resolve_model("nemo-canary-1b-v2")
|
||
assert result == "nemo-canary-1b-v2"
|
||
|
||
def test_unknown_alias_raises(self):
|
||
backend = OnnxAsrBackend()
|
||
with pytest.raises(ValueError, match="Неподдерживаемая модель"):
|
||
backend._resolve_model("nonexistent-model")
|
||
|
||
def test_whisper_alias_error_suggests_explicit_backend(self):
|
||
backend = OnnxAsrBackend()
|
||
|
||
with pytest.raises(
|
||
ValueError,
|
||
match=r"Whisper.*--device openvino-cpu.*--device cuda",
|
||
):
|
||
backend._resolve_model("medium")
|
||
|
||
def test_whisper_error_offers_platform_independent_backend(self):
|
||
"""На macOS и ARM нет ни OpenVINO, ни CUDA — нужен путь через cpu."""
|
||
backend = OnnxAsrBackend()
|
||
|
||
with pytest.raises(ValueError, match=r"--device cpu --model medium"):
|
||
backend._resolve_model("medium")
|
||
|
||
def test_turbo_whisper_error_suggests_models_supported_by_backends(self):
|
||
backend = OnnxAsrBackend()
|
||
|
||
with pytest.raises(ValueError) as exc_info:
|
||
backend._resolve_model("large-v3-turbo")
|
||
|
||
message = str(exc_info.value)
|
||
assert "--device openvino-cpu --model large-v3-turbo" in message
|
||
assert "--device cuda --model medium" in message
|
||
assert "--device cpu --model medium" in message
|
||
assert "--device cuda --model large-v3-turbo" not in message
|
||
assert "--device cpu --model large-v3-turbo" not in message
|