def add_arguments(parser):
"""Add arguments."""
return E2E_pytorch.add_arguments(parser)
def add_arguments(parser):
return E2E_pytorch.add_arguments(parser)
assert len(devset[0]) == batch_size
devset[0][:3]
"""### Build neural networks (3/4)
For simplicity, we use a predefined model: [Transformer](https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf).
NOTE: You can also use your custom model in command line tools as `asr_train.py --model-module your_module:YourModel`
"""
import argparse
from espnet.bin.asr_train import get_parser
from espnet.nets.pytorch_backend.e2e_asr import E2E
parser = get_parser()
parser = E2E.add_arguments(parser)
config = parser.parse_args([
"--mtlalpha", "0.0", # weight for cross entropy and CTC loss
"--outdir", "out", "--dict", ""]) # TODO: allow no arg
idim = info["input"][0]["shape"][1]
odim = info["output"][0]["shape"][1]
setattr(config, "char_list", [])
model = E2E(idim, odim, config)
model
"""### Update neural networks by iterating datasets (4/4)
Finaly, we got the training part.
"""
