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import julius |
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import torch |
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import pytest |
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from audiocraft.data.audio_utils import ( |
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_clip_wav, |
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convert_audio_channels, |
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convert_audio, |
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normalize_audio |
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) |
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from ..common_utils import get_batch_white_noise |
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class TestConvertAudioChannels: |
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def test_convert_audio_channels_downmix(self): |
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b, c, t = 2, 3, 100 |
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audio = get_batch_white_noise(b, c, t) |
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mixed = convert_audio_channels(audio, channels=2) |
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assert list(mixed.shape) == [b, 2, t] |
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def test_convert_audio_channels_nochange(self): |
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b, c, t = 2, 3, 100 |
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audio = get_batch_white_noise(b, c, t) |
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mixed = convert_audio_channels(audio, channels=c) |
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assert list(mixed.shape) == list(audio.shape) |
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def test_convert_audio_channels_upmix(self): |
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b, c, t = 2, 1, 100 |
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audio = get_batch_white_noise(b, c, t) |
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mixed = convert_audio_channels(audio, channels=3) |
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assert list(mixed.shape) == [b, 3, t] |
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def test_convert_audio_channels_upmix_error(self): |
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b, c, t = 2, 2, 100 |
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audio = get_batch_white_noise(b, c, t) |
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with pytest.raises(ValueError): |
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convert_audio_channels(audio, channels=3) |
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class TestConvertAudio: |
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def test_convert_audio_channels_downmix(self): |
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b, c, dur = 2, 3, 4. |
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sr = 128 |
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audio = get_batch_white_noise(b, c, int(sr * dur)) |
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out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=2) |
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assert list(out.shape) == [audio.shape[0], 2, audio.shape[-1]] |
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def test_convert_audio_channels_upmix(self): |
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b, c, dur = 2, 1, 4. |
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sr = 128 |
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audio = get_batch_white_noise(b, c, int(sr * dur)) |
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out = convert_audio(audio, from_rate=sr, to_rate=sr, to_channels=3) |
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assert list(out.shape) == [audio.shape[0], 3, audio.shape[-1]] |
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def test_convert_audio_upsample(self): |
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b, c, dur = 2, 1, 4. |
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sr = 2 |
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new_sr = 3 |
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audio = get_batch_white_noise(b, c, int(sr * dur)) |
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out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c) |
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out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr) |
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assert torch.allclose(out, out_j) |
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def test_convert_audio_resample(self): |
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b, c, dur = 2, 1, 4. |
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sr = 3 |
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new_sr = 2 |
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audio = get_batch_white_noise(b, c, int(sr * dur)) |
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out = convert_audio(audio, from_rate=sr, to_rate=new_sr, to_channels=c) |
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out_j = julius.resample.resample_frac(audio, old_sr=sr, new_sr=new_sr) |
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assert torch.allclose(out, out_j) |
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class TestNormalizeAudio: |
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def test_clip_wav(self): |
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b, c, dur = 2, 1, 4. |
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sr = 3 |
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audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur)) |
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_clip_wav(audio) |
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assert audio.abs().max() <= 1 |
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def test_normalize_audio_clip(self): |
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b, c, dur = 2, 1, 4. |
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sr = 3 |
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audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur)) |
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norm_audio = normalize_audio(audio, strategy='clip') |
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assert norm_audio.abs().max() <= 1 |
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def test_normalize_audio_rms(self): |
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b, c, dur = 2, 1, 4. |
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sr = 3 |
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audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur)) |
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norm_audio = normalize_audio(audio, strategy='rms') |
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assert norm_audio.abs().max() <= 1 |
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def test_normalize_audio_peak(self): |
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b, c, dur = 2, 1, 4. |
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sr = 3 |
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audio = 10.0 * get_batch_white_noise(b, c, int(sr * dur)) |
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norm_audio = normalize_audio(audio, strategy='peak') |
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assert norm_audio.abs().max() <= 1 |
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