def __getitem__(self, idx):
name = self.metas.iloc[idx, 0]
path = "{}/wavs/{}.wav".format(self.root_path, name)
# Text normalization
text = self.metas.iloc[idx, 1]
text_norm = self.metas.iloc[idx, 2]
text_encoded = np.array(text_to_sequence(text_norm, self.text_cleaner))
text_pos = np.array([idx + 1 for idx, _ in enumerate(text_encoded)])
data = {
"name": name,
"text": text,
"text_norm": text_norm,
"text_encoded": text_encoded,
"text_pos": text_pos,
"text_len": text_encoded.shape[-1],
"sr": self.sr
}
if not self.exclude_mels:
wav, sr = librosa.load(path, sr=self.sr) # wav is [-1.0, 1.0]
if sr != self.sr:
raise ValueError("{} SR doesn't match target {} SR".format(
sr, self.sr))
# Audio processing
wav, _ = librosa.effects.trim(wav,
frame_length=self.win_len,
hop_length=self.hop_len)
if self.mels_path:
mel = np.load(os.path.join(self.mels_path, name + ".mel.npy"))
else:
mel = librosa.feature.melspectrogram(wav,
sr=sr,
n_fft=self.n_fft,
win_length=self.win_len,
hop_length=self.hop_len,
n_mels=self.n_mels,
fmin=self.mel_fmin,
fmax=self.mel_fmax,
power=1.0)
mel = audio.dynamic_range_compression(mel)
data_mel = {
"wav": wav,
"mel": mel,
"mel_len": mel.shape[-1],
}
data.update(data_mel)
if self.aligns_path:
aligns = np.load(
os.path.join(self.aligns_path, name + ".align.npy"))
data['align'] = aligns
return data
def __getitem__(self, idx):
text = self.texts[idx]
# Text normalization
text_encoded = np.array(text_to_sequence(text, self.text_cleaner))
text_pos = np.array([idx + 1 for idx, _ in enumerate(text_encoded)])
data = {
"text": text,
"text_norm": text,
"text_encoded": text_encoded,
"text_pos": text_pos,
}
return data
def synthesize(text, voice, sigma=0.6, denoiser_strength=0.1, is_fp16=False):
hparams = create_hparams()
hparams.sampling_rate = 22050
if voice == "papaito":
voice_model = "nvidia_tacotron2_papaito_300"
elif voice == "constantino":
voice_model = "tacotron2_Constantino_600"
elif voice == "orador":
voice_model = "checkpoint_tacotron2_29000_es"
checkpoint_path = "/home/debian/workspace/models/" + voice_model
model = load_model(hparams)
model.load_state_dict(torch.load(checkpoint_path)['state_dict'])
_ = model.cuda().eval().half()
waveglow_path = '/home/debian/workspace/models/waveglow_256channels_ljs_v2.pt'
waveglow = torch.load(waveglow_path, map_location='cuda')['model']
_ = waveglow.cuda().eval().half()
denoiser = Denoiser(waveglow)
#text="¡Cágate lorito!"
#with open(filelist_path, encoding='utf-8', mode='r') as f:
# text = f.read()
sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]
sequence = torch.autograd.Variable(
torch.from_numpy(sequence)).cuda().long()
mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
#mel = torch.unsqueeze(mel, 0)
mel = mel_outputs.half() if is_fp16 else mel_outputs
audio = np.array([])
with torch.no_grad():
audio = waveglow.infer(mel, sigma=sigma)
if denoiser_strength > 0:
audio = denoiser(audio, denoiser_strength)
audio = audio * hparams.max_wav_value
audio = audio.squeeze()
audio = audio.cpu().numpy()
audio = audio.astype('int16')
return audio, hparams.sampling_rate
#checkpoint_path = "output/checkpoint_29000"
checkpoint_path = args.checkpoint_path
model = load_model(hparams)
model.load_state_dict(torch.load(checkpoint_path)['state_dict'])
_ = model.cuda().eval().half()
#waveglow_path = '/media/debian/SSD_USB/models/waveglow_256channels_ljs_v2.pt'
waveglow = torch.load(args.waveglow_path)['model']
_ = waveglow.cuda().eval().half()
denoiser = Denoiser(waveglow)
#text="¡Cágate lorito!"
with open(args.filelist_path, encoding='utf-8', mode='r') as f:
text = f.read()
sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]
sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
#mel = torch.unsqueeze(mel, 0)
mel = mel_outputs.half() if args.is_fp16 else mel_outputs
with torch.no_grad():
audio = waveglow.infer(mel, sigma=args.sigma)
if args.denoiser_strength > 0:
audio = denoiser(audio, args.denoiser_strength)
audio = audio * MAX_WAV_VALUE
audio = audio.squeeze()
audio = audio.cpu().numpy()
audio = audio.astype('int16')
file_name = "audio"
audio_path = os.path.join(args.output_dir,