def main(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
with open(args.config, 'r') as f:
params = yaml.load(f, Loader=yaml.FullLoader)
expdir = os.path.join('egs', params['data']['name'], 'exp',
params['train']['save_name'])
if not os.path.exists(expdir):
os.makedirs(expdir)
model_type = params['model']['type']
if model_type == 'transformer':
model = Transformer(params['model'])
elif model_type == 'transformer_lm':
model = TransformerLanguageModel(params['model'])
else:
raise NotADirectoryError
exit(0)
if args.ngpu >= 1:
model.cuda()
# build optimizer
optimizer = TransformerOptimizer(model,
params['train'],
model_size=params['model']['d_model'],
parallel_mode=args.parallel_mode)
trainer = Trainer(params,
model=model,
optimizer=optimizer,
is_visual=True,
expdir=expdir,
ngpu=args.ngpu,
parallel_mode=args.parallel_mode,
local_rank=args.local_rank)
train_loader = FeatureLoader(params,
'train',
shuffle=params['train']['shuffle'],
ngpu=args.ngpu,
mode=args.parallel_mode)
trainer.train(train_loader=train_loader)
def main(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
with open(args.config, 'r') as f:
params = yaml.load(f, Loader=yaml.FullLoader)
expdir = os.path.join('egs', params['data']['name'], 'exp',
params['train']['save_name'])
if not os.path.exists(expdir):
os.makedirs(expdir)
model = Transformer(params['model'])
if args.ngpu >= 1:
model.cuda()
print(model)
# build optimizer
optimizer = TransformerOptimizer(model,
params['train'],
model_size=params['model']['d_model'],
parallel_mode=args.parallel_mode)
trainer = Trainer(params,
model=model,
optimizer=optimizer,
is_visual=True,
expdir=expdir,
ngpu=args.ngpu,
parallel_mode=args.parallel_mode,
local_rank=args.local_rank)
train_dataset = AudioDataset(params['data'], 'train')
trainer.train(train_dataset=train_dataset)
def main(args):
checkpoint = torch.load(args.load_model)
if 'params' in checkpoint:
params = checkpoint['params']
else:
assert os.path.isfile(args.config), 'please specify a configure file.'
with open(args.config, 'r') as f:
params = yaml.load(f)
params['data']['shuffle'] = False
params['data']['spec_argument'] = False
params['data']['short_first'] = False
params['data']['batch_size'] = args.batch_size
model = Transformer(params['model'])
model.load_state_dict(checkpoint['model'])
print('Load pre-trained model from %s' % args.load_model)
model.eval()
if args.ngpu > 0:
model.cuda()
char2unit = load_vocab(params['data']['vocab'])
unit2char = {i: c for c, i in char2unit.items()}
recognizer = TransformerRecognizer(model,
unit2char=unit2char,
beam_width=args.beam_width,
max_len=args.max_len,
penalty=args.penalty,
lamda=args.lamda,
ngpu=args.ngpu)
# inputs_length: [len]
inputs, inputs_length = calc_fbank(args.file, params['data'])
if args.ngpu > 0:
inputs = inputs.cuda()
inputs_length = inputs_length.cuda()
preds = recognizer.recognize(inputs, inputs_length)
print('preds: {}'.format(preds[0].replace(' ', '')))
def main(args):
checkpoint = torch.load(args.load_model)
if 'params' in checkpoint:
params = checkpoint['params']
else:
assert os.path.isfile(args.config), 'please specify a configure file.'
with open(args.config, 'r') as f:
params = yaml.load(f)
params['data']['shuffle'] = False
params['data']['spec_augment'] = False
params['data']['short_first'] = False
params['data']['batch_size'] = args.batch_size
expdir = os.path.join('egs', params['data']['name'], 'exp', params['train']['save_name'])
decoder_set_name = 'decode_%s' % args.decode_set
if args.load_language_model is not None:
decoder_set_name += '_lm_lmw%.2f' % args.lm_weight
if args.suffix is not None:
decoder_set_name += '_%s' % args.suffix
decode_dir = os.path.join(expdir, decoder_set_name)
if not os.path.exists(decode_dir):
os.makedirs(decode_dir)
model = Transformer(params['model'])
model.load_state_dict(checkpoint['model'])
print('Load pre-trained model from %s' % args.load_model)
model.eval()
if args.ngpu > 0:
model.cuda()
if args.load_language_model is not None:
lm_chkpt = torch.load(args.load_language_model)
lm = TransformerLanguageModel(lm_chkpt['params']['model'])
lm.load_state_dict(lm_chkpt['model'])
lm.eval()
if args.ngpu > 0: lm.cuda()
print('Load pre-trained transformer language model from %s' % args.load_language_model)
else:
lm = None
char2unit = load_vocab(params['data']['vocab'])
unit2char = {i:c for c, i in char2unit.items()}
data_loader = FeatureLoader(params, args.decode_set, is_eval=True)
recognizer = TransformerRecognizer(
model, lm=lm, lm_weight=args.lm_weight, unit2char=unit2char, beam_width=args.beam_width,
max_len=args.max_len, penalty=args.penalty, lamda=args.lamda, ngpu=args.ngpu)
totals = len(data_loader.dataset)
batch_size = params['data']['batch_size']
writer = open(os.path.join(decode_dir, 'predict.txt'), 'w')
for step, (utt_id, batch) in enumerate(data_loader.loader):
if args.ngpu > 0:
inputs = batch['inputs'].cuda()
inputs_length = batch['inputs_length'].cuda()
else:
inputs = batch['inputs']
inputs_length = batch['inputs_length']
preds = recognizer.recognize(inputs, inputs_length)
targets = batch['targets']
targets_length = batch['targets_length']
for b in range(len(preds)):
n = step * batch_size + b
truth = ' '.join([unit2char[i.item()] for i in targets[b][1:targets_length[b]+1]])
print('[%d / %d ] %s - pred : %s' % (n, totals, utt_id[b], preds[b]))
print('[%d / %d ] %s - truth: %s' % (n, totals, utt_id[b], truth))
writer.write(utt_id[b] + ' ' + preds[b] + '\n')
writer.close()
def main(args):
checkpoint = torch.load(args.load_model)
if 'params' in checkpoint:
params = checkpoint['params']
else:
assert os.path.isfile(args.config), 'please specify a configure file.'
with open(args.config, 'r') as f:
params = yaml.load(f)
params['data']['shuffle'] = False
params['data']['spec_argument'] = False
params['data']['short_first'] = False
params['data']['batch_size'] = args.batch_size
expdir = os.path.join('egs', params['data']['name'], 'exp',
params['train']['save_name'])
if args.suffix is None:
decode_dir = os.path.join(expdir, 'decode_%s' % args.decode_set)
else:
decode_dir = os.path.join(
expdir, 'decode_%s_%s' % (args.decode_set, args.suffix))
if not os.path.exists(decode_dir):
os.makedirs(decode_dir)
model = Transformer(params['model'])
model.load_state_dict(checkpoint['model'])
print('Load pre-trained model from %s' % args.load_model)
model.eval()
if args.ngpu > 0:
model.cuda()
char2unit = load_vocab(params['data']['vocab'])
unit2char = {i: c for c, i in char2unit.items()}
dataset = AudioDataset(params['data'], args.decode_set)
data_loader = FeatureLoader(dataset)
recognizer = TransformerRecognizer(model,
unit2char=unit2char,
beam_width=args.beam_width,
max_len=args.max_len,
penalty=args.penalty,
lamda=args.lamda,
ngpu=args.ngpu)
totals = len(dataset)
batch_size = params['data']['batch_size']
writer = open(os.path.join(decode_dir, 'predict.txt'), 'w')
cers = []
for step, (utt_id, batch) in enumerate(data_loader.loader):
if args.ngpu > 0:
inputs = batch['inputs'].cuda()
inputs_length = batch['inputs_length'].cuda()
preds = recognizer.recognize(inputs, inputs_length)
targets = batch['targets']
targets_length = batch['targets_length']
for b in range(len(preds)):
n = step * batch_size + b
truth = ' '.join([
unit2char[i.item()]
for i in targets[b][1:targets_length[b] + 1]
])
print('[%d / %d] %s - pred : %s' %
(n, totals, utt_id[b], preds[b]))
print('[%d / %d] %s - truth: %s' % (n, totals, utt_id[b], truth))
cer = calc_cer(truth.split(), preds[b].split())
cers.append(cer)
print('cer: {:.2f}'.format(cer))
writer.write(utt_id[b] + ' ' + preds[b] + '\n')
print('cer avg: {:.2f}'.format(sum(cers) / len(cers)))
writer.close()