default=0.0003,
help='learning rate for optimization')
args = parser.parse_args()
use_gpu = torch.cuda.is_available()
print('use_gpu', use_gpu)
if use_gpu:
torch.backends.cudnn.benchmark = True
if args.dataset == 'librispeech':
from datasets.libri_speech import LibriSpeech as SpeechDataset, vocab, idx2char
else:
from datasets.mb_speech import MBSpeech as SpeechDataset, vocab, idx2char
train_dataset = SpeechDataset(
transform=Compose([LoadAudio(
), SpeedChange(), ExtractSpeechFeatures()]))
valid_dataset = SpeechDataset(
transform=Compose([LoadAudio(), ExtractSpeechFeatures()]))
indices = list(range(len(train_dataset)))
train_sampler = SubsetRandomSampler(indices[:-args.batch_size])
valid_sampler = SubsetRandomSampler(indices[-args.batch_size:])
train_data_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=False,
collate_fn=collate_fn,
num_workers=args.dataload_workers_nums,
sampler=train_sampler)
valid_data_loader = DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
alpha=args.alpha, beta=args.beta, cutoff_top_n=40, cutoff_prob=1.0, beam_width=1000)
else:
decoder = GreedyDecoder(labels=vocab)
t = time.time()
decoded_output, _ = decoder.decode(outputs)
print("decode time: %.3fs" % (time.time() - t))
return decoded_output[0][0]
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--checkpoint", type=str, required=True, help='checkpoint file to test')
parser.add_argument("--lm", type=str, required=False, help='link to KenLM 5-gram binary language model')
parser.add_argument("--alpha", type=float, default=0.3, help='alpha for CTC decode')
parser.add_argument("--beta", type=float, default=1.85, help='beta for CTC decode')
parser.add_argument("audio", help='a WAV file')
args = parser.parse_args()
data = {
'fname': args.audio,
'text': ''
}
data = Compose([LoadAudio(), ExtractSpeechFeatures()])(data)
result = transcribe(data, args)
print("Predicted:")
print(result)
LibriSpeech(name='train-clean-100'),
LibriSpeech(name='train-clean-360'),
LibriSpeech(name='train-other-500'),
LibriSpeech(name='dev-clean',)
])
elif args.dataset == 'backgroundsounds':
from datasets.background_sounds import BackgroundSounds
dataset = BackgroundSounds(is_random=False)
elif args.dataset == 'bolorspeech':
from datasets.bolor_speech import BolorSpeech
dataset = ConcatDataset([
BolorSpeech(name='train'),
BolorSpeech(name='train2'),
BolorSpeech(name='test'),
BolorSpeech(name='demo'),
BolorSpeech(name='annotation'),
BolorSpeech(name='annotation-1111')
])
else:
print("unknown dataset!")
import sys
sys.exit(1)
transform=Compose([LoadAudio(), ComputeMagSpectrogram()])
for data in tqdm(dataset):
fname = data['fname']
data = transform(data)
mel_spectrogram = data['input']
np.save(fname.replace('.wav', '.npy'), mel_spectrogram)
type=float,
default=0.001,
help='learning rate for optimization')
args = parser.parse_args()
use_gpu = torch.cuda.is_available()
print('use_gpu', use_gpu)
if use_gpu:
torch.backends.cudnn.benchmark = True
if args.dataset == 'librispeech':
from datasets.libri_speech import LibriSpeech as SpeechDataset, vocab, idx2char
else:
from datasets.mb_speech import MBSpeech as SpeechDataset, vocab, idx2char
dataset = SpeechDataset(transform=LoadAudio())
indices = list(range(len(dataset)))
train_sampler = SubsetRandomSampler(indices[:-args.batch_size])
valid_sampler = SubsetRandomSampler(indices[-args.batch_size:])
train_data_loader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
collate_fn=collate_fn,
num_workers=args.dataload_workers_nums,
sampler=train_sampler)
valid_data_loader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
collate_fn=collate_fn,
num_workers=args.dataload_workers_nums,