def load_forward_taco(
checkpoint_path: str) -> Tuple[ForwardTacotron, Dict[str, Any]]:
print(f'Loading tts checkpoint {checkpoint_path}')
checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu'))
config = checkpoint['config']
tts_model = ForwardTacotron.from_config(config)
tts_model.load_state_dict(checkpoint['model'])
print(f'Loaded forward taco with step {tts_model.get_step()}')
return tts_model, config
def __init__(self, tts_path: str, voc_path: str, device='cuda'):
self.device = torch.device(device)
tts_checkpoint = torch.load(tts_path, map_location=self.device)
tts_config = tts_checkpoint['config']
tts_model = ForwardTacotron.from_config(tts_config)
tts_model.load_state_dict(tts_checkpoint['model'])
self.tts_model = tts_model
self.wavernn = WaveRNN.from_checkpoint(voc_path)
self.melgan = torch.hub.load('seungwonpark/melgan', 'melgan')
self.melgan.to(device).eval()
self.cleaner = Cleaner.from_config(tts_config)
self.tokenizer = Tokenizer()
self.dsp = DSP.from_config(tts_config)
config['git_hash'] = try_get_git_hash()
dsp = DSP.from_config(config)
paths = Paths(config['data_path'], config['voc_model_id'],
config['tts_model_id'])
assert len(os.listdir(paths.alg)) > 0, f'Could not find alignment files in {paths.alg}, please predict ' \
f'alignments first with python train_tacotron.py --force_align!'
force_gta = args.force_gta
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
print('Using device:', device)
# Instantiate Forward TTS Model
print('\nInitialising Forward TTS Model...\n')
model = ForwardTacotron.from_config(config).to(device)
optimizer = optim.Adam(model.parameters())
restore_checkpoint(model=model,
optim=optimizer,
path=paths.forward_checkpoints / 'latest_model.pt',
device=device)
if force_gta:
print('Creating Ground Truth Aligned Dataset...\n')
train_set, val_set = get_tts_datasets(paths.data,
8,
r=1,
model_type='forward',
filter_attention=False,
max_mel_len=None)
create_gta_features(model, train_set, val_set, paths.gta)