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James PineandClaude Opus 4.7
a49cc6afbb
Uploaded/recorded voice samples were rejected outright whenever the peak
exceeded 0.99 ("Audio is clipping (reduce input gain)"). That wasn't
actionable: a recording in the app has no pre-gain control, and an
already-captured file can't be re-taken by the user. The Settings
"Normalize audio" toggle only affects generated TTS output, so users who
enabled it expecting it to help with sample uploads were still blocked.
Replace the hard reject with a small, always-on preprocess step that
runs right after load:
- DC-offset removal
- Conservative edge-silence trim (top_db=30) with 100 ms padding kept
- Peak cap at 0.95 if the input peak exceeds that
Duration and RMS checks now run on the preprocessed waveform, so
samples that were previously rejected for being "hot" are accepted and
stored with safe headroom. True in-waveform clipping artifacts still
can't be repaired — peak scaling only prevents downstream re-clipping
during multi-sample combination and TTS inference.
Adds a unit-test file (previously none existed for audio.py).
Fixes #456.
Co-Authored-By: Claude Opus 4.7 (1M context) <[email protected]>
104 lines
2.9 KiB
Python
104 lines
2.9 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_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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