def main(args):
checkpoint = torch.load(args.checkpoint_path)
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint['hp_str'])
model = ModifiedGenerator(hp.audio.n_mel_channels, hp.model.n_residual_layers,
ratios=hp.model.generator_ratio, mult = hp.model.mult,
out_band = hp.model.out_channels).cuda()
model.load_state_dict(checkpoint['model_g'])
model.eval(inference=True)
with torch.no_grad():
mel = torch.from_numpy(np.load(args.input))
if len(mel.shape) == 2:
mel = mel.unsqueeze(0)
mel = mel.cuda()
audio = model.inference(mel)
audio = audio.squeeze(0) # collapse all dimension except time axis
if args.d:
denoiser = Denoiser(model).cuda()
audio = denoiser(audio, 0.01)
audio = audio.squeeze()
audio = audio[:-(hp.audio.hop_length*10)]
audio = MAX_WAV_VALUE * audio
audio = audio.clamp(min=-MAX_WAV_VALUE, max=MAX_WAV_VALUE-1)
audio = audio.short()
audio = audio.cpu().detach().numpy()
out_path = args.input.replace('.npy', '_reconstructed_epoch%04d.wav' % checkpoint['epoch'])
write(out_path, hp.audio.sampling_rate, audio)
def main(cmd_args):
"""Run training."""
parser = get_parser()
args, _ = parser.parse_known_args(cmd_args)
args = parser.parse_args(cmd_args)
if os.path.exists(args.checkpoint_path):
checkpoint = torch.load(args.checkpoint_path)
else:
print("Checkpoint not exixts")
return None
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint["hp_str"])
validloader = loader.get_tts_dataset(hp.data.data_dir, 1, hp, True)
print("Checkpoint : ", args.checkpoint_path)
idim = len(valid_symbols)
odim = hp.audio.num_mels
model = FeedForwardTransformer(idim, odim, hp)
# os.makedirs(args.out, exist_ok=True)
checkpoint = torch.load(args.checkpoint_path)
model.load_state_dict(checkpoint["model"])
evaluate(hp, validloader, model)
def main(args):
checkpoint = torch.load(args.checkpoint_path)
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint['hp_str'])
model = Generator(hp.audio.n_mel_channels).cuda()
model.load_state_dict(checkpoint['model_g'])
model.eval(inference=False)
with torch.no_grad():
for melpath in tqdm.tqdm(
glob.glob(os.path.join(args.input_folder, '*.mel'))):
mel = torch.load(melpath)
if len(mel.shape) == 2:
mel = mel.unsqueeze(0)
mel = mel.cuda()
audio = model.inference(mel)
audio = audio.cpu().detach().numpy()
out_path = melpath.replace(
'.mel', '_reconstructed_epoch%04d.wav' % checkpoint['epoch'])
write(out_path, hp.audio.sampling_rate, audio)
def main(args):
checkpoint = torch.load(args.checkpoint_path)
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint['hp_str'])
model = Generator(hp.audio.n_mel_channels).cuda()
model.load_state_dict(checkpoint['model_g'])
model.eval()
with torch.no_grad():
for melpath in tqdm.tqdm(glob.glob(os.path.join(args.input_folder, '*.mel'))):
mel = torch.load(melpath)
if len(mel.shape) == 2:
mel = mel.unsqueeze(0)
mel = mel.cuda()
# pad input mel with zeros to cut artifact
# see https://github.com/seungwonpark/melgan/issues/8
zero = torch.full((1, hp.audio.n_mel_channels, 10), -11.5129).cuda()
mel = torch.cat((mel, zero), axis=2)
audio = model(mel)
audio = audio.squeeze() # collapse all dimension except time axis
audio = audio[:-(hp.audio.hop_length*10)]
audio = MAX_WAV_VALUE * audio
audio = audio.clamp(min=-MAX_WAV_VALUE, max=MAX_WAV_VALUE)
audio = audio.short()
audio = audio.cpu().detach().numpy()
out_path = melpath.replace('.mel', '_reconstructed_epoch%04d.wav' % checkpoint['epoch'])
write(out_path, hp.audio.sampling_rate, audio)
def main(args):
checkpoint = torch.load(args.checkpoint_path)
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint['hp_str'])
model = Generator(hp.audio.n_mel_channels).cuda()
model.load_state_dict(checkpoint['model_g'])
model.eval()
with torch.no_grad():
mel = torch.from_numpy(np.load(args.input))
if len(mel.shape) == 2:
mel = mel.unsqueeze(0)
mel = mel.cuda()
audio = model(mel)
# For multi-band inference
print(audio.shape)
audio = audio.squeeze(0) # collapse all dimension except time axis
if args.d:
denoiser = Denoiser(model).cuda()
audio = denoiser(audio, 0.1)
audio = audio.squeeze()
audio = audio[:-(hp.audio.hop_length * 10)]
audio = MAX_WAV_VALUE * audio
audio = audio.clamp(min=-MAX_WAV_VALUE, max=MAX_WAV_VALUE - 1)
audio = audio.short()
audio = audio.cpu().detach().numpy()
out_path = args.input.replace(
'.npy', '_hifi_GAN_epoch%04d.wav' % checkpoint['epoch'])
write(out_path, hp.audio.sampling_rate, audio)
def load_tiers(self):
for idx, chkpt_path in enumerate(self.infer_hp.checkpoints):
checkpoint = torch.load(chkpt_path)
hp = load_hparam_str(checkpoint['hp_str'])
if self.hp != hp:
print('Warning: hp different in file %s' % chkpt_path)
self.tiers[idx+1].load_state_dict(checkpoint['model'])
def init(config, checkpoint_path, device="cuda"):
checkpoint = torch.load(checkpoint_path)
if config is not None:
hp = HParam(config)
else:
hp = load_hparam_str(checkpoint['hp_str'])
model = Generator(hp.audio.n_mel_channels,
hp.model.n_residual_layers,
ratios=hp.model.generator_ratio,
mult=hp.model.mult,
out_band=hp.model.out_channels).to(device)
model.load_state_dict(checkpoint['model_g'])
model.eval(inference=True)
return hp, model
def main(args):
checkpoint = torch.load(args.checkpoint_path)
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint['hp_str'])
model = ModifiedGenerator(hp.audio.n_mel_channels, hp.model.n_residual_layers,
ratios=hp.model.generator_ratio, mult = hp.model.mult,
out_band = hp.model.out_channels).cuda()
model.load_state_dict(checkpoint['model_g'])
model.eval(inference=True)
with torch.no_grad():
mel = torch.from_numpy(np.load(args.input))
if len(mel.shape) == 2:
mel = mel.unsqueeze(0)
mel = mel.cuda()
zero = torch.full((1, 80, 10), -11.5129).to(mel.device)
mel = torch.cat((mel, zero), dim=2)
vocgan_trace = torch.jit.trace(model, mel)
vocgan_trace.save("{}/vocgan_ex_female_en_{}_{}.pt".format(args.out, checkpoint['githash'], checkpoint['epoch']))
def main(args):
checkpoint = torch.load(args.checkpoint_path)
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint['hp_str'])
model = Generator(hp.audio.n_mel_channels).cuda()
model.load_state_dict(checkpoint['model_g'])
model.eval()
#model.remove_weight_norm()
with torch.no_grad():
mel = torch.from_numpy(np.load(args.input))
if len(mel.shape) == 2:
mel = mel.unsqueeze(0)
mel = mel.cuda()
#zero = torch.full((1, 80, 10), -11.5129).to(mel.device)
#mel = torch.cat((mel, zero), dim=2)
hifigan_trace = torch.jit.trace(model, mel)
#print(state_dict_g.keys())
hifigan_trace.save("{}/hifigan_{}.pt".format(args.out, args.name))
def main(args):
"""Run deocding."""
para_mel = []
parser = get_parser()
args = parser.parse_args(args)
logging.info("python path = " + os.environ.get("PYTHONPATH", "(None)"))
print("Text : ", args.text)
print("Checkpoint : ", args.checkpoint_path)
if os.path.exists(args.checkpoint_path):
checkpoint = torch.load(args.checkpoint_path)
else:
logging.info("Checkpoint not exixts")
return None
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint["hp_str"])
idim = len(valid_symbols)
odim = hp.audio.num_mels
model = FeedForwardTransformer(
idim, odim, hp) # torch.jit.load("./etc/fastspeech_scrip_new.pt")
os.makedirs(args.out, exist_ok=True)
if args.old_model:
logging.info("\nSynthesis Session...\n")
model.load_state_dict(checkpoint, strict=False)
else:
checkpoint = torch.load(args.checkpoint_path)
model.load_state_dict(checkpoint["model"])
text = process_paragraph(args.text)
for i in range(0, len(text)):
txt = preprocess(text[i])
audio = synth(txt, model, hp)
m = audio.T
para_mel.append(m)
m = torch.cat(para_mel, dim=1)
np.save("mel.npy", m.cpu().numpy())
plot_mel(m)
if hp.train.melgan_vocoder:
m = m.unsqueeze(0)
print("Mel shape: ", m.shape)
vocoder = torch.hub.load("seungwonpark/melgan", "melgan")
vocoder.eval()
if torch.cuda.is_available():
vocoder = vocoder.cuda()
mel = m.cuda()
with torch.no_grad():
wav = vocoder.inference(
mel) # mel ---> batch, num_mels, frames [1, 80, 234]
wav = wav.cpu().float().numpy()
else:
stft = STFT(filter_length=1024, hop_length=256, win_length=1024)
print(m.size())
m = m.unsqueeze(0)
wav = griffin_lim(m, stft, 30)
wav = wav.cpu().numpy()
save_path = "{}/test_tts.wav".format(args.out)
write(save_path, hp.audio.sample_rate, wav.astype("int16"))
type=str,
default=None,
help=
"yaml file for config. will use hp_str from checkpoint if not given.")
parser.add_argument('-p',
'--chkpt',
default=None,
type=str,
help='path to latest checkpoint (default: None)')
parser.add_argument('-d', action='store_true', help="denoising ")
parser.add_argument('--cpu', action='store_true')
args = parser.parse_args()
torch.manual_seed(2020)
checkpoint = torch.load(args.chkpt)
if args.config is not None:
hp = HParam(args.config)
else:
hp = load_hparam_str(checkpoint['hp_str'])
filename = "out"
if args.cpu:
filename = filename + "_" + "cpu"
device = torch.device('cpu')
else:
filename = filename + "_" + "cuda"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Mel file input : ", args.mel)
main(hp, checkpoint, args.infile, args.out, filename, args.sigma,
args.duration, args.half, args.mel, device)
