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James PineandClaude Opus 4.7
4ffbc03d15
Review feedback on the preprocessor: 1. ``trim_top_db=30`` was labelled "conservative" in the docstring but is actually *more* aggressive than librosa's default of 60. Normal speech dynamic range sits around 30 dB, so 30 dB would eat quiet trailing syllables and soft consonants. Raise the default to 40 dB — below normal speech dynamic range but still catching obvious edge silence — and fix the docstring. 2. Unconditional 100 ms edge padding ran even when ``librosa.effects.trim`` removed nothing. For a well-recorded 29.9 s upload that path would push the waveform past the 30 s ceiling and trigger a spurious "too long" rejection. Only pad when trimming actually shortened the audio, and cap the pad so the output never exceeds the input length. Adds a regression test for the net-neutral length behaviour. Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
113 lines
3.2 KiB
Python
113 lines
3.2 KiB
Python
"""
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Unit tests for reference-audio preprocessing.
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Covers :func:`backend.utils.audio.preprocess_reference_audio` and
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:func:`backend.utils.audio.validate_and_load_reference_audio`.
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"""
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import sys
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from pathlib import Path
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import numpy as np
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import pytest
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import soundfile as sf
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from utils.audio import ( # noqa: E402
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preprocess_reference_audio,
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validate_and_load_reference_audio,
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)
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SR = 24000
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def _tone(duration_s: float, amp: float = 0.3, freq: float = 220.0) -> np.ndarray:
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n = int(duration_s * SR)
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t = np.arange(n, dtype=np.float32) / SR
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return (amp * np.sin(2 * np.pi * freq * t)).astype(np.float32)
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def test_peak_cap_scales_hot_input():
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audio = _tone(3.0, amp=0.99)
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out = preprocess_reference_audio(audio, SR)
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assert np.abs(out).max() <= 0.951
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def test_peak_cap_leaves_moderate_input_untouched():
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audio = _tone(3.0, amp=0.5)
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out = preprocess_reference_audio(audio, SR)
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assert np.isclose(np.abs(out).max(), 0.5, atol=1e-3)
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def test_dc_offset_removed():
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audio = _tone(3.0, amp=0.3) + 0.1
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out = preprocess_reference_audio(audio, SR)
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assert abs(float(np.mean(out))) < 1e-3
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def test_silence_is_trimmed_with_padding_kept():
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silence = np.zeros(int(SR * 1.0), dtype=np.float32)
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speech = _tone(3.0, amp=0.3)
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audio = np.concatenate([silence, speech, silence])
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out = preprocess_reference_audio(audio, SR)
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# Most of the 2s of leading/trailing silence should be gone, but the
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# 3s of speech plus ~200ms of padding should remain.
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assert len(audio) - len(out) >= SR, "expected >=1s of silence trimmed"
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assert len(out) >= int(3.0 * SR), "speech body should be preserved"
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def test_clean_audio_is_not_padded_past_original_length():
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# Well-recorded audio with no edge silence shouldn't get longer after
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# preprocessing — otherwise a 29.9 s upload could be pushed past the
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# 30 s max_duration ceiling downstream.
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audio = _tone(3.0, amp=0.3)
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out = preprocess_reference_audio(audio, SR)
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assert len(out) <= len(audio)
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def test_empty_input_returns_empty():
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out = preprocess_reference_audio(np.zeros(0, dtype=np.float32), SR)
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assert out.size == 0
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def test_validate_accepts_previously_rejected_hot_file(tmp_path):
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audio = _tone(3.0, amp=0.995)
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path = tmp_path / "hot.wav"
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sf.write(str(path), audio, SR)
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ok, err, out_audio, out_sr = validate_and_load_reference_audio(str(path))
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assert ok, f"expected pass, got error: {err}"
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assert out_audio is not None
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assert out_sr == SR
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assert np.abs(out_audio).max() <= 0.951
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def test_validate_still_rejects_silent_input(tmp_path):
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audio = np.zeros(int(SR * 3.0), dtype=np.float32)
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path = tmp_path / "silent.wav"
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sf.write(str(path), audio, SR)
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ok, err, _, _ = validate_and_load_reference_audio(str(path))
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assert not ok
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assert err is not None
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assert "too short" in err.lower() or "quiet" in err.lower()
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def test_validate_rejects_too_short(tmp_path):
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audio = _tone(0.5, amp=0.3)
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path = tmp_path / "short.wav"
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sf.write(str(path), audio, SR)
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ok, err, _, _ = validate_and_load_reference_audio(str(path))
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assert not ok
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assert "too short" in (err or "").lower()
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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