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https://github.com/YuzuZensai/spleeter.git
synced 2026-01-31 14:58:23 +00:00
dont pad the signal, only the stft matrix before inversion
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@@ -122,20 +122,21 @@ class Separator(object):
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assert not (inverse and length is None)
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assert not (inverse and length is None)
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data = np.asfortranarray(data)
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data = np.asfortranarray(data)
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N = self._params["frame_length"]
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N = self._params["frame_length"]
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pad_edges = int(N/2)
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H = self._params["frame_step"]
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H = self._params["frame_step"]
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F = int(N/2) + 1
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win = hann(N, sym=False)
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win = hann(N, sym=False)
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fstft = istft if inverse else stft
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fstft = istft if inverse else stft
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win_len_arg = {"win_length": None, "length": length +
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win_len_arg = {"win_length": None,
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2*pad_edges} if inverse else {"n_fft": N}
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"length": None} if inverse else {"n_fft": N}
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n_channels = data.shape[-1]
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n_channels = data.shape[-1]
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out = []
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out = []
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for c in range(n_channels):
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for c in range(n_channels):
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d = data[:, :, c].T if inverse else np.concatenate(
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d = np.concatenate((np.zeros((F, 1)), data[:, :, c].T, np.zeros(
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(np.zeros(pad_edges,), data[:, c], np.zeros(pad_edges,)))
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(F, 1))), axis=1) if inverse else data[:, c]
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s = fstft(d, hop_length=H, window=win, center=False, **win_len_arg)
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s = fstft(d, hop_length=H, window=win, center=False, **win_len_arg)
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if inverse:
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if inverse:
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s = s[pad_edges:-pad_edges]
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s = s[H:]
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s = s[:length]
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s = np.expand_dims(s.T, 2-inverse)
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s = np.expand_dims(s.T, 2-inverse)
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out.append(s)
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out.append(s)
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if len(out) == 1:
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if len(out) == 1:
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@@ -51,7 +51,7 @@ def test_separator_backends(test_file):
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stft_matrix = separator_lib._stft(waveform)
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stft_matrix = separator_lib._stft(waveform)
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reconstructed = separator_lib._stft(
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reconstructed = separator_lib._stft(
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stft_matrix, inverse=True, length=waveform.shape[0])
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stft_matrix, inverse=True, length=waveform.shape[0])
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assert np.allclose(reconstructed, waveform, atol=1e-2)
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assert np.allclose(reconstructed, waveform, atol=3e-2)
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# # now also test that tensorflow and librosa STFT provide same results
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# # now also test that tensorflow and librosa STFT provide same results
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from spleeter.audio.spectrogram import compute_spectrogram_tf
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from spleeter.audio.spectrogram import compute_spectrogram_tf
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@@ -62,11 +62,10 @@ def test_separator_backends(test_file):
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with tf.Session() as sess:
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with tf.Session() as sess:
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spectrogram_tf_eval = spectrogram_tf.eval()
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spectrogram_tf_eval = spectrogram_tf.eval()
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# check that stfts are equivalent up to the padding in the librosa case
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# check that stfts are equivalent
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assert stft_matrix.shape[0] == spectrogram_tf_eval.shape[0] + 2
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assert stft_matrix.shape == spectrogram_tf_eval.shape
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assert stft_matrix.shape[1:] == spectrogram_tf_eval.shape[1:]
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assert np.allclose(
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assert np.allclose(
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np.abs(stft_matrix[1:-1]), spectrogram_tf_eval, atol=1e-2)
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np.abs(stft_matrix), spectrogram_tf_eval, atol=1e-2)
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# compare both separation, it should be close
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# compare both separation, it should be close
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out_tf = separator_tf._separate_tensorflow(waveform, test_file)
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out_tf = separator_tf._separate_tensorflow(waveform, test_file)
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@@ -78,7 +77,8 @@ def test_separator_backends(test_file):
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print(np.sum(np.abs(out_lib[instrument])))
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print(np.sum(np.abs(out_lib[instrument])))
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assert np.sum(np.abs(out_tf[instrument])) > 1000
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assert np.sum(np.abs(out_tf[instrument])) > 1000
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assert np.sum(np.abs(out_lib[instrument])) > 1000
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assert np.sum(np.abs(out_lib[instrument])) > 1000
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assert np.allclose(out_tf[instrument], out_lib[instrument], atol=0.01)
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print(np.max(out_tf[instrument]- out_lib[instrument]))
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assert np.allclose(out_tf[instrument], out_lib[instrument], atol=0.025)
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@pytest.mark.parametrize('test_file, configuration, backend', TEST_CONFIGURATIONS)
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@pytest.mark.parametrize('test_file, configuration, backend', TEST_CONFIGURATIONS)
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