raw_wav = configs['raw_wav']
# Init dataset and data loader
# Init dataset and data loader
test_collate_conf = copy.deepcopy(configs['collate_conf'])
test_collate_conf['spec_aug'] = False
test_collate_conf['spec_sub'] = False
test_collate_conf['feature_dither'] = False
test_collate_conf['speed_perturb'] = False
test_collate_conf['wav_distortion_conf']['wav_distortion_rate'] = 0
test_collate_func = CollateFunc(**test_collate_conf, raw_wav=raw_wav)
dataset_conf = configs.get('dataset_conf', {})
dataset_conf['batch_size'] = args.batch_size
dataset_conf['batch_type'] = 'static'
dataset_conf['sort'] = False
test_dataset = AudioDataset(args.test_data,
**dataset_conf,
raw_wav=raw_wav)
test_data_loader = DataLoader(test_dataset,
collate_fn=test_collate_func,
shuffle=False,
batch_size=1,
num_workers=0)
# Init asr model from configs
model = init_asr_model(configs)
# Load dict
char_dict = {}
with open(args.dict, 'r') as fin:
for line in fin:
arr = line.strip().split()
train_collate_func = CollateFunc(**configs['collate_conf'],
raw_wav=raw_wav)
cv_collate_conf = copy.deepcopy(configs['collate_conf'])
# no augmenation on cv set
cv_collate_conf['spec_aug'] = False
cv_collate_conf['spec_sub'] = False
if raw_wav:
cv_collate_conf['feature_dither'] = 0.0
cv_collate_conf['speed_perturb'] = False
cv_collate_conf['wav_distortion_conf']['wav_distortion_rate'] = 0
cv_collate_func = CollateFunc(**cv_collate_conf, raw_wav=raw_wav)
dataset_conf = configs.get('dataset_conf', {})
train_dataset = AudioDataset(args.train_data,
**dataset_conf,
raw_wav=raw_wav)
cv_dataset = AudioDataset(args.cv_data, **dataset_conf, raw_wav=raw_wav)
if distributed:
logging.info('training on multiple gpus, this gpu {}'.format(args.gpu))
dist.init_process_group(args.dist_backend,
init_method=args.init_method,
world_size=args.world_size,
rank=args.rank)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, shuffle=True)
cv_sampler = torch.utils.data.distributed.DistributedSampler(
cv_dataset, shuffle=False)
else:
train_sampler = None
raw_wav = configs['raw_wav']
# Init dataset and data loader
ali_collate_conf = copy.deepcopy(configs['collate_conf'])
ali_collate_conf['spec_aug'] = False
ali_collate_conf['spec_sub'] = False
ali_collate_conf['feature_dither'] = False
ali_collate_conf['speed_perturb'] = False
if raw_wav:
ali_collate_conf['wav_distortion_conf']['wav_distortion_rate'] = 0
ali_collate_func = CollateFunc(**ali_collate_conf,
raw_wav=raw_wav)
dataset_conf = configs.get('dataset_conf', {})
dataset_conf['batch_size'] = args.batch_size
dataset_conf['batch_type'] = 'static'
dataset_conf['sort'] = False
ali_dataset = AudioDataset(args.input_file, **dataset_conf, raw_wav=raw_wav)
ali_data_loader = DataLoader(ali_dataset,
collate_fn=ali_collate_func,
shuffle=False,
batch_size=1,
num_workers=0)
# Init asr model from configs
model = init_asr_model(configs)
load_checkpoint(model, args.checkpoint)
use_cuda = args.gpu >= 0 and torch.cuda.is_available()
device = torch.device('cuda' if use_cuda else 'cpu')
model = model.to(device)
model.eval()
logging.fatal(
'decoding mode {} must be running with batch_size == 1'.format(
args.mode))
sys.exit(1)
with open(args.config, 'r') as fin:
configs = yaml.load(fin)
# Init dataset and data loader
test_collate_conf = copy.copy(configs['collate_conf'])
test_collate_conf['spec_aug'] = False
test_collate_func = CollateFunc(**test_collate_conf, cmvn=args.cmvn)
dataset_conf = configs.get('dataset_conf', {})
dataset_conf['batch_size'] = args.batch_size
dataset_conf['sort'] = False
test_dataset = AudioDataset(args.test_data, **dataset_conf)
test_data_loader = DataLoader(test_dataset,
collate_fn=test_collate_func,
shuffle=False,
batch_size=1,
num_workers=0)
# Init transformer model
input_dim = test_dataset.input_dim
vocab_size = test_dataset.output_dim
encoder_type = configs.get('encoder', 'conformer')
if encoder_type == 'conformer':
encoder = ConformerEncoder(input_dim, **configs['encoder_conf'])
else:
encoder = TransformerEncoder(input_dim, **configs['encoder_conf'])
decoder = TransformerDecoder(vocab_size, encoder.output_size(),
torch.manual_seed(777)
with open(args.config, 'r') as fin:
configs = yaml.load(fin)
distributed = args.world_size > 1
# Init dataset and data loader
collate_func = CollateFunc(**configs['collate_conf'],
**configs['spec_aug_conf'],
cmvn=args.cmvn)
cv_collate_conf = copy.copy(configs['collate_conf'])
cv_collate_conf['spec_aug'] = False
cv_collate_func = CollateFunc(**cv_collate_conf, cmvn=args.cmvn)
dataset_conf = configs.get('dataset_conf', {})
train_dataset = AudioDataset(args.train_data, **dataset_conf)
cv_dataset = AudioDataset(args.cv_data, **dataset_conf)
if distributed:
logging.info('training on multiple gpu, this gpu {}'.format(args.gpu))
dist.init_process_group(args.dist_backend,
init_method=args.init_method,
world_size=args.world_size,
rank=args.rank)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset, shuffle=True)
cv_sampler = torch.utils.data.distributed.DistributedSampler(
cv_dataset, shuffle=False)
else:
train_sampler = None
cv_sampler = None
