Medix-AI/coqui-tts
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coqui-tts
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Make audio folder and save audio with scipy

This commit is contained in:
Eren 2018-09-21 17:27:02 +02:00
parent f2ef1ca36a
commit 172e0262de
2 changed files with 4 additions and 2 deletions
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2
train.py
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@ -472,7 +472,7 @@ if __name__ == '__main__':
OUT_PATH = create_experiment_folder(OUT_PATH, c.model_name, args.debug) OUT_PATH = create_experiment_folder(OUT_PATH, c.model_name, args.debug)
CHECKPOINT_PATH = os.path.join(OUT_PATH, 'checkpoints') CHECKPOINT_PATH = os.path.join(OUT_PATH, 'checkpoints')
AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios') AUDIO_PATH = os.path.join(OUT_PATH, 'test_audios')
os.mkdir(AUDIO_PATH) os.makedirs(AUDIO_PATH, exist_ok=True)
shutil.copyfile(args.config_path, os.path.join(OUT_PATH, 'config.json')) shutil.copyfile(args.config_path, os.path.join(OUT_PATH, 'config.json'))
# setup tensorboard # setup tensorboard

4
utils/audio.py
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@ -3,6 +3,7 @@ import librosa
import pickle import pickle
import copy import copy
import numpy as np import numpy as np
import scipy
from scipy import signal from scipy import signal
_mel_basis = None _mel_basis = None
@ -38,7 +39,8 @@ class AudioProcessor(object):
def save_wav(self, wav, path): def save_wav(self, wav, path):
wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav)))) wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
librosa.output.write_wav(path, wav_norm.astype(np.int16), self.sample_rate) # librosa.output.write_wav(path, wav_norm.astype(np.int16), self.sample_rate)
scipy.io.wavfile.write(path, self.sample_rate, wav.astype(np.int16))
def _linear_to_mel(self, spectrogram): def _linear_to_mel(self, spectrogram):
global _mel_basis global _mel_basis