reformat for clean code

audio.py

@@ -1,71 +1,85 @@
 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
+    # 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)
+        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!)
+# 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)
+    """Compute number of time frames of spectrogram"""
+    pad = fsize - fshift
     if length % fshift == 0:
         M = (length + pad * 2 - fsize) // fshift + 1
     else:
@@ -74,63 +88,92 @@ def num_frames(length, fsize, fshift):
 
 
 def pad_lr(x, fsize, fshift):
-    """Compute left and right padding
-    """
+    """Compute left and right padding"""
     M = num_frames(len(x), fsize, fshift)
-    pad = (fsize - fshift)
+    pad = fsize - fshift
     T = len(x) + 2 * pad
     r = (M - 1) * fshift + fsize - T
     return pad, pad + r
+
+
 ##########################################################
-#Librosa correct padding
+# 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)
+    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)
+            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)
-
+            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
+        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)
+            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)
-
+            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)
+        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)
+        return (D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db