Update audio.py

audio.py

@@ -1,136 +1,136 @@
-import librosa
-import librosa.filters
-import numpy as np
-# import tensorflow as tf
-from scipy import signal
-from scipy.io import wavfile
-from hparams import hparams as hp
-
-def load_wav(path, sr):
-    return librosa.core.load(path, sr=sr)[0]
-
-def save_wav(wav, path, sr):
-    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
-    #proposed by @dsmiller
-    wavfile.write(path, sr, wav.astype(np.int16))
-
-def save_wavenet_wav(wav, path, sr):
-    librosa.output.write_wav(path, wav, sr=sr)
-
-def preemphasis(wav, k, preemphasize=True):
-    if preemphasize:
-        return signal.lfilter([1, -k], [1], wav)
-    return wav
-
-def inv_preemphasis(wav, k, inv_preemphasize=True):
-    if inv_preemphasize:
-        return signal.lfilter([1], [1, -k], wav)
-    return wav
-
-def get_hop_size():
-    hop_size = hp.hop_size
-    if hop_size is None:
-        assert hp.frame_shift_ms is not None
-        hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
-    return hop_size
-
-def linearspectrogram(wav):
-    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
-    S = _amp_to_db(np.abs(D)) - hp.ref_level_db
-    
-    if hp.signal_normalization:
-        return _normalize(S)
-    return S
-
-def melspectrogram(wav):
-    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
-    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db
-    
-    if hp.signal_normalization:
-        return _normalize(S)
-    return S
-
-def _lws_processor():
-    import lws
-    return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")
-
-def _stft(y):
-    if hp.use_lws:
-        return _lws_processor(hp).stft(y).T
-    else:
-        return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)
-
-##########################################################
-#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
-def num_frames(length, fsize, fshift):
-    """Compute number of time frames of spectrogram
-    """
-    pad = (fsize - fshift)
-    if length % fshift == 0:
-        M = (length + pad * 2 - fsize) // fshift + 1
-    else:
-        M = (length + pad * 2 - fsize) // fshift + 2
-    return M
-
-
-def pad_lr(x, fsize, fshift):
-    """Compute left and right padding
-    """
-    M = num_frames(len(x), fsize, fshift)
-    pad = (fsize - fshift)
-    T = len(x) + 2 * pad
-    r = (M - 1) * fshift + fsize - T
-    return pad, pad + r
-##########################################################
-#Librosa correct padding
-def librosa_pad_lr(x, fsize, fshift):
-    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
-
-# Conversions
-_mel_basis = None
-
-def _linear_to_mel(spectogram):
-    global _mel_basis
-    if _mel_basis is None:
-        _mel_basis = _build_mel_basis()
-    return np.dot(_mel_basis, spectogram)
-
-def _build_mel_basis():
-    assert hp.fmax <= hp.sample_rate // 2
-    return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels,
-                               fmin=hp.fmin, fmax=hp.fmax)
-
-def _amp_to_db(x):
-    min_level = np.exp(hp.min_level_db / 20 * np.log(10))
-    return 20 * np.log10(np.maximum(min_level, x))
-
-def _db_to_amp(x):
-    return np.power(10.0, (x) * 0.05)
-
-def _normalize(S):
-    if hp.allow_clipping_in_normalization:
-        if hp.symmetric_mels:
-            return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
-                           -hp.max_abs_value, hp.max_abs_value)
-        else:
-            return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
-    
-    assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
-    if hp.symmetric_mels:
-        return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
-    else:
-        return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))
-
-def _denormalize(D):
-    if hp.allow_clipping_in_normalization:
-        if hp.symmetric_mels:
-            return (((np.clip(D, -hp.max_abs_value,
-                              hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
-                    + hp.min_level_db)
-        else:
-            return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
-    
-    if hp.symmetric_mels:
-        return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
-    else:
-        return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
+import librosa
+import librosa.filters
+import numpy as np
+# import tensorflow as tf
+from scipy import signal
+from scipy.io import wavfile
+from hparams import hparams as hp
+
+def load_wav(path, sr):
+    return librosa.core.load(path, sr=sr)[0]
+
+def save_wav(wav, path, sr):
+    wav *= 32767 / max(0.01, np.max(np.abs(wav)))
+    #proposed by @dsmiller
+    wavfile.write(path, sr, wav.astype(np.int16))
+
+def save_wavenet_wav(wav, path, sr):
+    librosa.output.write_wav(path, wav, sr=sr)
+
+def preemphasis(wav, k, preemphasize=True):
+    if preemphasize:
+        return signal.lfilter([1, -k], [1], wav)
+    return wav
+
+def inv_preemphasis(wav, k, inv_preemphasize=True):
+    if inv_preemphasize:
+        return signal.lfilter([1], [1, -k], wav)
+    return wav
+
+def get_hop_size():
+    hop_size = hp.hop_size
+    if hop_size is None:
+        assert hp.frame_shift_ms is not None
+        hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
+    return hop_size
+
+def linearspectrogram(wav):
+    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
+    S = _amp_to_db(np.abs(D)) - hp.ref_level_db
+    
+    if hp.signal_normalization:
+        return _normalize(S)
+    return S
+
+def melspectrogram(wav):
+    D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
+    S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db
+    
+    if hp.signal_normalization:
+        return _normalize(S)
+    return S
+
+def _lws_processor():
+    import lws
+    return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")
+
+def _stft(y):
+    if hp.use_lws:
+        return _lws_processor(hp).stft(y).T
+    else:
+        return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)
+
+##########################################################
+#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
+def num_frames(length, fsize, fshift):
+    """Compute number of time frames of spectrogram
+    """
+    pad = (fsize - fshift)
+    if length % fshift == 0:
+        M = (length + pad * 2 - fsize) // fshift + 1
+    else:
+        M = (length + pad * 2 - fsize) // fshift + 2
+    return M
+
+
+def pad_lr(x, fsize, fshift):
+    """Compute left and right padding
+    """
+    M = num_frames(len(x), fsize, fshift)
+    pad = (fsize - fshift)
+    T = len(x) + 2 * pad
+    r = (M - 1) * fshift + fsize - T
+    return pad, pad + r
+##########################################################
+#Librosa correct padding
+def librosa_pad_lr(x, fsize, fshift):
+    return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
+
+# Conversions
+_mel_basis = None
+
+def _linear_to_mel(spectogram):
+    global _mel_basis
+    if _mel_basis is None:
+        _mel_basis = _build_mel_basis()
+    return np.dot(_mel_basis, spectogram)
+
+def _build_mel_basis():
+    assert hp.fmax <= hp.sample_rate // 2
+    return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels,
+                               fmin=hp.fmin, fmax=hp.fmax)
+
+def _amp_to_db(x):
+    min_level = np.exp(hp.min_level_db / 20 * np.log(10))
+    return 20 * np.log10(np.maximum(min_level, x))
+
+def _db_to_amp(x):
+    return np.power(10.0, (x) * 0.05)
+
+def _normalize(S):
+    if hp.allow_clipping_in_normalization:
+        if hp.symmetric_mels:
+            return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
+                           -hp.max_abs_value, hp.max_abs_value)
+        else:
+            return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
+    
+    assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
+    if hp.symmetric_mels:
+        return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
+    else:
+        return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))
+
+def _denormalize(D):
+    if hp.allow_clipping_in_normalization:
+        if hp.symmetric_mels:
+            return (((np.clip(D, -hp.max_abs_value,
+                              hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
+                    + hp.min_level_db)
+        else:
+            return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
+    
+    if hp.symmetric_mels:
+        return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
+    else:
+        return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)