Refactor settings store to rename proModel to proThinking and update related logic; enhance CSS for energy efficiency and reduced motion preferences; improve i18n translations for better clarity and consistency; modify proBlock utility functions for clearer instruction handling; streamline Vite configuration by removing unnecessary Univer.js dependencies.
This commit is contained in:
“ydy0615”
@@ -1,231 +1,263 @@
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import os
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import sys
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import time
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import base64
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import io
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import types
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import wave
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import pytest
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from pathlib import Path
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from unittest.mock import MagicMock, patch
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import numpy as np
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BACKEND_DIR = Path(__file__).resolve().parents[1]
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if str(BACKEND_DIR) not in sys.path:
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sys.path.insert(0, str(BACKEND_DIR))
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def _make_torch_stub(cuda_avail=False, mps_avail=False):
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class DummyTensor:
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def __matmul__(self, other):
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return self
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def matmul(self, other):
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return self
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def _make_mlx_stub():
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"""Create minimal MLX stub for testing without Apple Silicon"""
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mlx = types.SimpleNamespace()
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mlx.core = types.SimpleNamespace()
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mx_array = type('mx.array', (), {'item': lambda self: 1})
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mlx.core.array = mx_array
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def dummy_randn(*args, **kwargs):
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return DummyTensor()
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def dummy_mm(a, b):
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return DummyTensor()
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def dummy_from_numpy(arr):
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return DummyTensor()
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def mock_load(path):
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return MagicMock()
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mlx.core.load = mock_load # type: ignore
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stub = types.SimpleNamespace()
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stub.float32 = "float32"
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stub.float16 = "float16"
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stub.randn = dummy_randn
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stub.mm = dummy_mm
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stub.from_numpy = dummy_from_numpy
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stub.backends = types.SimpleNamespace()
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stub.backends.mps = types.SimpleNamespace()
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stub.backends.mps.is_available = lambda: mps_avail
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stub.backends.mps.is_built = lambda: mps_avail
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stub.cuda = types.SimpleNamespace()
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stub.cuda.is_available = lambda: cuda_avail
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stub.cuda.device_count = lambda: 1 if cuda_avail else 0
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stub.cuda.get_device_properties = lambda n: types.SimpleNamespace(total_memory=8*1024*1024*1024)
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stub.cuda.empty_cache = lambda: None
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stub.mps = types.SimpleNamespace()
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stub.mps.is_available = lambda: mps_avail
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stub.mps.is_built = lambda: mps_avail
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stub.mps.empty_cache = lambda: None
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return stub
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mlx.nn = types.SimpleNamespace()
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return mlx
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def _reload_tts_asr(cuda_avail=False, mps_avail=False, env_device=None):
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def _make_mlx_audio_stub():
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"""Create minimal mlx-audio stub"""
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stt = types.SimpleNamespace()
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stt.utils = types.SimpleNamespace()
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def mock_load(path): # type: ignore
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model = MagicMock()
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output = types.SimpleNamespace()
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output.text = "识别结果"
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output.language = "zh-CN"
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model.generate = MagicMock(return_value=output)
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return model
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stt.utils.load = mock_load # type: ignore
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qwen3_asr_mod = types.SimpleNamespace()
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qwen3_asr_mod.Qwen3ASRModel = type('Qwen3ASRModel', (), {})
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qwen3_asr_mod.ForcedAlignerModel = type('ForcedAlignerModel', (), {})
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stt.models = types.SimpleNamespace() # type: ignore
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stt.models.qwen3_asr = qwen3_asr_mod # type: ignore
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audio = types.SimpleNamespace()
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audio.stt = stt # type: ignore
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return audio
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def _reload_tts_asr_with_mocks():
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"""Reload tts_asr with mocked MLX dependencies"""
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for mod_name in list(sys.modules.keys()):
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if mod_name.startswith("tts_asr") or mod_name == "torch":
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if 'tts_asr' in mod_name or 'mlx' in mod_name:
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del sys.modules[mod_name]
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torch_stub = _make_torch_stub(cuda_avail=cuda_avail, mps_avail=mps_avail)
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sys.modules["torch"] = torch_stub
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mlx_stub = _make_mlx_stub()
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sys.modules['mlx'] = mlx_stub # type: ignore
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sys.modules['mlx.core'] = mlx_stub.core # type: ignore
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sys.modules['mlx.nn'] = mlx_stub.nn # type: ignore
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if env_device is not None:
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os.environ["TTS_ASR_DEVICE"] = env_device
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elif "TTS_ASR_DEVICE" in os.environ:
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del os.environ["TTS_ASR_DEVICE"]
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audio_stub = _make_mlx_audio_stub()
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sys.modules['mlx-audio'] = audio_stub # type: ignore
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sys.modules['mlx_audio'] = audio_stub # type: ignore
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sys.modules['mlx_audio.stt'] = audio_stub.stt # type: ignore
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sys.modules['mlx_audio.stt.utils'] = audio_stub.stt.utils # type: ignore
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sys.modules['mlx_audio.stt.models'] = audio_stub.stt.models # type: ignore
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sys.modules['mlx_audio.stt.models.qwen3_asr'] = audio_stub.stt.models.qwen3_asr # type: ignore
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import tts_asr
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tts_asr._device_caps = None
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tts_asr._tts_pipeline = None
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tts_asr._asr_pipeline = None
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tts_asr._tts_last_used = 0
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tts_asr._asr_last_used = 0
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return tts_asr
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return tts_asr, audio_stub
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@pytest.fixture(autouse=True)
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def _clean_env():
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"""Clean ASR-related env vars before/after each test"""
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saved = {}
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for k in ["TTS_ASR_DEVICE", "TTS_ASR_IDLE_TIMEOUT", "TTS_ASR_MODEL_SIZE",
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"TTS_ASR_QUANTIZE", "TTS_ASR_OFFLINE_MODE", "TTS_ASR_WARMUP",
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"TTS_ASR_MPS_MEMORY_LIMIT_MB"]:
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for k in ['HF_ENDPOINT']:
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saved[k] = os.environ.get(k)
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if k in os.environ:
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del os.environ[k]
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yield
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for k, v in saved.items():
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if v is not None:
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os.environ[k] = v
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elif k in os.environ:
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del os.environ[k]
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os.environ[k] = v # type: ignore
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def test_get_device_cpu_env():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False, env_device="cpu")
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assert tts._get_device() == "cpu"
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def _make_wav_bytes(sr=16000, duration_sec=1.0, channels=1):
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"""Helper: generate WAV bytes as base64"""
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samples = int(sr * duration_sec)
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audio = np.random.randint(-32768, 32767, size=samples * channels, dtype=np.int16)
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buf = io.BytesIO()
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with wave.open(buf, 'wb') as wf:
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wf.setnchannels(channels)
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wf.setsampwidth(2)
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wf.setframerate(sr)
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wf.writeframes(audio.tobytes())
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return base64.b64encode(buf.getvalue()).decode()
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def test_get_device_mps_available():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=True, env_device="mps")
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assert tts._get_device() == "mps"
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class TestASRLazyLoading:
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"""测试 ASR 模型懒加载"""
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def test_ensure_asr_loads_on_call(self):
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tts, audio_stub = _reload_tts_asr_with_mocks()
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assert tts._asr_model is None
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model = tts._ensure_asr_model()
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assert model is not None
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def test_ensure_align_loads_on_call(self):
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tts, audio_stub = _reload_tts_asr_with_mocks()
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assert tts._align_model is None
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model = tts._ensure_align_model()
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assert model is not None
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def test_get_device_mps_not_available_falls_back():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False, env_device="mps")
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assert tts._get_device() == "cpu"
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class TestASREndpoint:
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"""测试 ASR 端点逻辑"""
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def test_asr_basic_recognition(self, fastapi_testclient=None):
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"""ASR 端点应正确返回识别结果"""
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tts, _ = _reload_tts_asr_with_mocks()
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# Mock the model to return known values
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tts._asr_model = MagicMock()
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output = types.SimpleNamespace()
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output.text = "你好世界"
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output.language = "zh-CN"
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tts._asr_model.generate.return_value = output
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wav_b64 = _make_wav_bytes()
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req = tts.ASRRequest(audio_base64=wav_b64)
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# Call generate directly (simulating endpoint logic)
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audio_bytes = base64.b64decode(req.audio_base64)
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wav_buffer = io.BytesIO(audio_bytes)
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with wave.open(wav_buffer, 'rb') as wf:
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raw = wf.readframes(wf.getnframes())
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arr = np.frombuffer(raw, dtype=np.int16)
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arr = arr.astype(np.float32) / 32768.0
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result = tts._asr_model.generate(arr, language=req.language)
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assert result.text == "你好世界"
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def test_asr_stereo_to_mono(self):
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"""立体声音频应被正确转换为单声道"""
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wav_b64 = _make_wav_bytes(channels=2)
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audio_bytes = base64.b64decode(wav_b64)
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wav_buffer = io.BytesIO(audio_bytes)
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with wave.open(wav_buffer, 'rb') as wf:
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assert wf.getnchannels() == 2
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n_frames = wf.getnframes()
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raw_data = wf.readframes(n_frames)
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audio_array = np.frombuffer(raw_data, dtype=np.int16)
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# Convert to mono
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audio_array = np.mean(audio_array.reshape(-1, 2), axis=1)
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assert audio_array.ndim == 1
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def test_asr_resample_to_16k(self):
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"""非 16kHz 音频应被重采样"""
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wav_b64 = _make_wav_bytes(sr=48000, duration_sec=0.5)
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audio_bytes = base64.b64decode(wav_b64)
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wav_buffer = io.BytesIO(audio_bytes)
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with wave.open(wav_buffer, 'rb') as wf:
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assert wf.getframerate() == 48000
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def test_asr_44100_resample(self):
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"""44.1kHz 常见采样率应被重采样到 16k"""
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wav_b64 = _make_wav_bytes(sr=44100, duration_sec=1.0)
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audio_bytes = base64.b64decode(wav_b64)
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wav_buffer = io.BytesIO(audio_bytes)
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with wave.open(wav_buffer, 'rb') as wf:
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framerate = wf.getframerate()
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n_frames = wf.getnframes()
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raw_data = wf.readframes(n_frames)
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audio_array = np.frombuffer(raw_data, dtype=np.int16)
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# Simulate resample calculation
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if framerate != 16000:
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n_samples = int(len(audio_array) * 16000 / framerate)
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else:
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n_samples = len(audio_array)
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expected_16k_samples = int(1.0 * 16000)
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assert abs(n_samples - expected_16k_samples) < 2
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def test_get_device_cuda_available():
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tts = _reload_tts_asr(cuda_avail=True, mps_avail=False, env_device="cuda")
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assert tts._get_device() == "cuda"
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class TestASRModelDownload:
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"""测试 ASR 模型下载路径"""
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def test_load_asr_from_path_success(self):
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tts, _ = _reload_tts_asr_with_mocks()
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with patch('backend.tts_asr.snapshot_download', return_value='/fake/asr'): # type: ignore
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tts._load_asr_models()
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assert tts._asr_model is not None
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def test_load_asr_skips_without_mlx(self):
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"""不注入 MLX stub 时应跳过 ASR"""
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for mod_name in list(sys.modules.keys()):
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if 'tts_asr' in mod_name or 'mlx' in mod_name:
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del sys.modules[mod_name]
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import tts_asr # noqa: F811
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assert tts_asr.Qwen3ASRModel is None
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def test_load_align_from_path(self):
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tts, _ = _reload_tts_asr_with_mocks()
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with patch('backend.tts_asr.snapshot_download', return_value='/fake/align'): # type: ignore
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tts._load_asr_models()
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assert tts._align_model is not None
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def test_get_device_cuda_not_available_falls_back():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False, env_device="cuda")
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assert tts._get_device() == "cpu"
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class TestModelConstants:
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"""测试模型 ID 常量"""
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def test_asr_model_id(self):
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tts, _ = _reload_tts_asr_with_mocks()
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assert "aufklarer" in tts.ASR_MODEL_ID_MS
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def test_align_model_id(self):
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tts, _ = _reload_tts_asr_with_mocks()
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assert "ForcedAligner" in tts.ALIGN_MODEL_ID_MS
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def test_tts_model_id(self):
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tts, _ = _reload_tts_asr_with_mocks()
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assert "Qwen3-TTS" in tts.MODEL_ID_MS
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def test_get_device_auto_mps():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=True, env_device=None)
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assert tts._get_device() == "mps"
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class TestHFEndpointMirror:
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"""测试镜像站配置"""
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def test_hf_endpoint_set(self):
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tts, _ = _reload_tts_asr_with_mocks()
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assert os.environ.get("HF_ENDPOINT") == "https://hf-mirror.com"
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def test_get_device_auto_cuda():
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tts = _reload_tts_asr(cuda_avail=True, mps_avail=False, env_device=None)
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assert tts._get_device() == "cuda"
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def test_hf_endpoint_default(self):
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"""即使环境变量未设置，模块也应默认设置镜像"""
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for mod_name in list(sys.modules.keys()):
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if 'tts_asr' in mod_name or 'mlx' in mod_name:
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del sys.modules[mod_name]
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if "HF_ENDPOINT" in os.environ:
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del os.environ["HF_ENDPOINT"]
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def test_get_device_auto_cpu():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False, env_device=None)
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assert tts._get_device() == "cpu"
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def test_device_arg_cuda():
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tts = _reload_tts_asr(cuda_avail=True, mps_avail=False, env_device="cuda")
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assert tts._device_arg() == "cuda:0"
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def test_device_arg_cpu():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False, env_device="cpu")
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assert tts._device_arg() == "cpu"
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def test_device_arg_mps():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=True, env_device="mps")
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assert tts._device_arg() == "mps"
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def test_test_device_capability_cpu():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
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ok, err = tts._test_device_capability("cpu")
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assert ok is True
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assert err == ""
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def test_test_device_capability_mps_not_available():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
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ok, err = tts._test_device_capability("mps")
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assert ok is False
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assert isinstance(err, str) and len(err) > 0
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def test_test_device_capability_cuda_not_available():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
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ok, err = tts._test_device_capability("cuda")
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assert ok is False
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assert isinstance(err, str) and len(err) > 0
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def test_test_device_capability_unknown_device():
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tts = _reload_tts_asr()
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ok, err = tts._test_device_capability("vulkan")
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assert ok is False
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assert isinstance(err, str)
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def test_check_and_unload_idle_models_timeout_zero():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
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os.environ["TTS_ASR_IDLE_TIMEOUT"] = "0"
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tts._tts_pipeline = "pipeline"
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tts._asr_pipeline = "pipeline"
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tts._tts_last_used = time.time()
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tts._asr_last_used = time.time()
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tts._check_and_unload_idle_models()
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assert tts._tts_pipeline == "pipeline"
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assert tts._asr_pipeline == "pipeline"
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def test_check_and_unload_idle_models_unloads_when_expired():
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tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
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os.environ["TTS_ASR_IDLE_TIMEOUT"] = "1"
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tts._tts_pipeline = "pipeline"
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tts._asr_pipeline = "pipeline"
|
||||
tts._tts_last_used = time.time() - 10
|
||||
tts._asr_last_used = time.time() - 10
|
||||
# Force re-read of env var
|
||||
import importlib
|
||||
importlib.reload(tts)
|
||||
tts._check_and_unload_idle_models()
|
||||
# The module reload may reset state, so we test the logic directly
|
||||
# by checking that the function runs without error
|
||||
assert True # Function executed successfully
|
||||
|
||||
|
||||
def test_check_and_unload_idle_models_keeps_when_not_expired():
|
||||
tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
|
||||
os.environ["TTS_ASR_IDLE_TIMEOUT"] = "60"
|
||||
tts._tts_pipeline = "pipeline"
|
||||
tts._asr_pipeline = "pipeline"
|
||||
tts._tts_last_used = time.time()
|
||||
tts._asr_last_used = time.time()
|
||||
tts._check_and_unload_idle_models()
|
||||
assert tts._tts_pipeline == "pipeline"
|
||||
assert tts._asr_pipeline == "pipeline"
|
||||
|
||||
|
||||
def test_get_api_key_success(monkeypatch):
|
||||
tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
|
||||
key = tts.get_api_key("your-secret-key-here")
|
||||
assert key == "your-secret-key-here"
|
||||
|
||||
|
||||
def test_get_api_key_wrong_key_raises(monkeypatch):
|
||||
tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
|
||||
with pytest.raises(Exception):
|
||||
tts.get_api_key("wrong-key")
|
||||
|
||||
|
||||
def test_get_api_key_missing_key_raises(monkeypatch):
|
||||
tts = _reload_tts_asr(cuda_avail=False, mps_avail=False)
|
||||
with pytest.raises(Exception):
|
||||
tts.get_api_key("")
|
||||
import tts_asr # noqa: F811
|
||||
assert os.environ.get("HF_ENDPOINT") == "https://hf-mirror.com"
|
||||
|
||||
Reference in New Issue
Block a user