Exemplos de AudioDataset.filter_by_conv em Python

Exemplo n.º 1

Arquivo: train_rnnt.py Projeto: cloudthink/open_stt_e2e

model = Transducer(128,
                   len(labels),
                   512,
                   256,
                   am_layers=3,
                   lm_layers=3,
                   dropout=0.3,
                   am_checkpoint='exp/am.bin',
                   lm_checkpoint='exp/lm.bin')

train = AudioDataset(
    '/media/lytic/STORE/ru_open_stt_wav/public_youtube1120_hq.txt', labels)
test = AudioDataset(
    '/media/lytic/STORE/ru_open_stt_wav/public_youtube700_val.txt', labels)

train.filter_by_conv(model.encoder.conv)
train.filter_by_length(400)

test.filter_by_conv(model.encoder.conv)
test.filter_by_length(200)

optimizer = torch.optim.Adam(model.parameters(), lr=3e-4, weight_decay=1e-5)

model.cuda()

sampler = BucketingSampler(train, 32)

train = DataLoader(train,
                   pin_memory=True,
                   num_workers=4,
                   collate_fn=collate_fn_rnnt,

Exemplo n.º 2

from torch_baidu_ctc import ctc_loss

torch.backends.cudnn.benchmark = True
torch.manual_seed(0)
np.random.seed(0)

labels = Labels()

model = AcousticModel(40, 512, 256, len(labels), n_layers=3, dropout=0.3)

train = AudioDataset(
    '/media/lytic/STORE/ru_open_stt_wav/public_youtube1120_hq.txt', labels)
test = AudioDataset(
    '/media/lytic/STORE/ru_open_stt_wav/public_youtube700_val.txt', labels)

train.filter_by_conv(model.conv)
train.filter_by_length(400)

test.filter_by_conv(model.conv)
test.filter_by_length(200)

optimizer = torch.optim.Adam(model.parameters(), lr=3e-4, weight_decay=1e-5)
scheduler = StepLR(optimizer, step_size=500, gamma=0.99)

model.cuda()

sampler = BucketingSampler(train, 32)

train = DataLoader(train,
                   pin_memory=True,
                   num_workers=4,