def melspectrogram_torch(wav, hparams=None):
"""mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)"""
mel = melspectrogram(wav, hparams)
mel_output = torch.from_numpy(mel).type(torch.FloatTensor)
return mel_output
def files_to_list(filename):
"""
Takes a text file of filenames and makes a list of filenames
"""
with open(filename, encoding='utf-8') as f:
files = f.readlines()
files = [f.rstrip() for f in files]
return files
def to_gpu(x):
x = x.contiguous()
if torch.cuda.is_available():
x = x.cuda(non_blocking=True)
return torch.autograd.Variable(x)
if __name__ == "__main__":
import aukit
inpath = r"E:\data\temp\01.wav"
wav = load_wav(inpath, sr=16000)
mel = melspectrogram(wav)
out = inv_melspectrogram(mel)
aukit.play_audio(wav)
aukit.play_audio(out)
