config.json update

config.json

@@ -1,11 +1,12 @@
 {
-    "model_name": "loc-sens-attn-new",
+    "model_name": "loc-sens-attn-lws",
+    "audio_processor": "audio-lws",
     "num_mels": 80,
     "num_freq": 1025,
     "sample_rate": 20000,
     "frame_length_ms": 50,
     "frame_shift_ms": 12.5,
-    "preemphasis": 0.97,
+    "preemphasis": 0,
     "min_mel_freq": 125,
     "max_mel_freq": 7600,
     "min_level_db": -100,
@@ -16,8 +17,8 @@
     "epochs": 1000,
     "lr": 0.002,
     "warmup_steps": 4000,
-    "batch_size": 32,
+    "batch_size": 52,
-    "eval_batch_size":32,
+    "eval_batch_size":-1,
     "r": 5,
 
     "griffin_lim_iters": 60,
@@ -26,7 +27,7 @@
     "num_loader_workers": 8,
 
     "checkpoint": true,
-    "save_step": 750,
+    "save_step": 25000,
     "print_step": 10,
     "run_eval": false,
     "data_path": "/snakepit/shared/data/keithito/LJSpeech-1.1/",
@@ -35,4 +36,4 @@
     "dataset": "LJSpeechCached",
     "min_seq_len": 0,
     "output_path": "experiments/"
-  }
+}

utils/audio_lws.py

@@ -0,0 +1,108 @@
+import os
+import librosa
+import pickle
+import copy
+import numpy as np
+from scipy import signal
+import lws
+
+_mel_basis = None
+
+class AudioProcessor(object):
+
+    def __init__(self, sample_rate, num_mels, min_level_db, frame_shift_ms,
+                 frame_length_ms, ref_level_db, num_freq, power, preemphasis,
+                 min_mel_freq, max_mel_freq, griffin_lim_iters=None, ):
+        self.sample_rate = sample_rate
+        self.num_mels = num_mels
+        self.min_level_db = min_level_db
+        self.frame_shift_ms = frame_shift_ms
+        self.frame_length_ms = frame_length_ms
+        self.ref_level_db = ref_level_db
+        self.num_freq = num_freq
+        self.power = power
+        self.min_mel_freq = min_mel_freq
+        self.max_mel_freq = max_mel_freq
+        self.griffin_lim_iters = griffin_lim_iters
+        self.preemphasis =preemphasis
+        _, self.hop_length, self.win_length = self._stft_parameters()
+        self.num_freq = (self.win_length // 2) + 1
+        if preemphasis == 0:
+            print(" | > Preemphasis is deactive.")
+
+    def save_wav(self, wav, path):
+        wav *= 32767 / max(0.01, np.max(np.abs(wav)))
+        librosa.output.write_wav(path, wav.astype(np.float), self.sample_rate, norm=True)
+        
+    def _stft_parameters(self, ):
+        n_fft = (self.num_freq - 1) * 2
+        hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate) 
+        win_length = int(self.frame_length_ms / 1000.0 * self.sample_rate)
+        return n_fft, hop_length, win_length
+        
+    def _lws_processor(self):
+        try:
+            return lws.lws(self.win_length, self.hop_length ,mode="speech")
+        except:
+            raise RuntimeError(" !! WindowLength({}) is not multiple of HopLength({}).".format(self.win_length, self.hop_length))
+
+    def _amp_to_db(self, x):
+        min_level = np.exp(self.min_level_db / 20 * np.log(10))
+        return 20 * np.log10(np.maximum(min_level, x))
+
+    def _db_to_amp(self, x):
+        return np.power(10.0, x * 0.05)
+
+    def _normalize(self, S):
+        return np.clip((S - self.min_level_db) / -self.min_level_db, 0, 1)
+
+    def _denormalize(self, S):
+        return (np.clip(S, 0, 1) * -self.min_level_db) + self.min_level_db
+
+    def apply_preemphasis(self, x):
+        if self.preemphasis == 0:
+            raise RuntimeError(" !! Preemphasis is applied with factor 0.0. ")
+        return signal.lfilter([1, -self.preemphasis], [1], x)
+    
+    def apply_inv_preemphasis(self, x):
+        if self.preemphasis == 0:
+            raise RuntimeError(" !! Preemphasis is applied with factor 0.0. ")
+        return signal.lfilter([1], [1, -self.preemphasis], x)
+
+    def spectrogram(self, y):
+        if self.preemphasis:
+            D = self._lws_processor().stft(self.apply_preemphasis(y)).T
+        else:
+            D = self._lws_processor().stft(y).T
+        S = self._amp_to_db(np.abs(D)) - self.ref_level_db
+        return self._normalize(S)
+
+    def inv_spectrogram(self, spectrogram):
+        '''Converts spectrogram to waveform using librosa'''
+        S = self._denormalize(spectrogram)
+        S = self._db_to_amp(S + self.ref_level_db)  # Convert back to linear
+        processor = self._lws_processor()
+        D = processor.run_lws(S.astype(np.float64).T ** self.power)
+        y = processor.istft(D).astype(np.float32)
+        # Reconstruct phase
+        if self.preemphasis:
+            return self.apply_inv_preemphasis(y)
+        return y
+    
+    def _linear_to_mel(self, spectrogram):
+        global _mel_basis
+        if _mel_basis is None:
+            _mel_basis = self._build_mel_basis()
+        return np.dot(_mel_basis, spectrogram)
+
+    def _build_mel_basis(self, ):
+        return librosa.filters.mel(self.sample_rate, self.win_length, n_mels=self.num_mels)
+#                                    fmin=self.min_mel_freq, fmax=self.max_mel_freq)
+
+    def melspectrogram(self, y):
+        if self.preemphasis:
+            D = self._lws_processor().stft(self.apply_preemphasis(y)).T
+        else:
+            D = self._lws_processor().stft(y).T
+        S = self._amp_to_db(self._linear_to_mel(np.abs(D))) - self.ref_level_db
+        return self._normalize(S)