args = parser.parse_args()
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s')
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
# Set random seed
torch.manual_seed(777)
print(args)
with open(args.config, 'r') as fin:
configs = yaml.load(fin, Loader=yaml.FullLoader)
distributed = args.world_size > 1
raw_wav = configs['raw_wav']
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)
args.mode))
sys.exit(1)
with open(args.config, 'r') as fin:
configs = yaml.load(fin)
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)
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
if args.mode in ['ctc_prefix_beam_search', 'attention_rescoring'
] and args.batch_size > 1:
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':
'alignment mode must be running with batch_size == 1')
sys.exit(1)
with open(args.config, 'r') as fin:
configs = yaml.load(fin)
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)
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s')
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
# Set random seed
torch.manual_seed(777)
with open(args.config, 'r') as fin:
configs = yaml.load(fin)
distributed = args.world_size > 1
raw_wav = configs['raw_wav']
train_collate_func = CollateFunc(**configs['collate_conf'],
raw_wav=raw_wav,
cmvn=args.cmvn)
cv_collate_conf = copy.deepcopy(configs['collate_conf'])
# no augmenation on cv set
cv_collate_conf['spec_aug'] = 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,
cmvn=args.cmvn)
dataset_conf = configs.get('dataset_conf', {})
train_dataset = AudioDataset(args.train_data,
args.mode))
sys.exit(1)
with open(args.config, 'r') as fin:
configs = yaml.load(fin)
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['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,
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, raw_wav=raw_wav)
test_data_loader = DataLoader(test_dataset,
collate_fn=test_collate_func,
shuffle=False,
batch_size=1,
num_workers=0)
# Init transformer model
if raw_wav:
input_dim = configs['collate_conf']['feature_extraction_conf']['mel_bins']
else:
args = parser.parse_args()
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s')
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
# Set random seed
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(