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1d44da845b
* Begin to add RNN-T training for librispeech. * Copy files from conformer_ctc. Will edit it. * Use conformer/transformer model as encoder. * Begin to add training script. * Add training code. * Remove long utterances to avoid OOM when a large max_duraiton is used. * Begin to add decoding script. * Add decoding script. * Minor fixes. * Add beam search. * Use LSTM layers for the encoder. Need more tunings. * Use stateless decoder. * Minor fixes to make it ready for merge. * Fix README. * Update RESULT.md to include RNN-T Conformer. * Minor fixes. * Fix tests. * Minor fixes. * Minor fixes. * Fix tests.
105 lines
3.0 KiB
Python
105 lines
3.0 KiB
Python
# Copyright 2021 University of Chinese Academy of Sciences (author: Han Zhu)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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class Noam(object):
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"""
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Implements Noam optimizer.
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Proposed in
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"Attention Is All You Need", https://arxiv.org/pdf/1706.03762.pdf
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Modified from
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https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/optimizer.py # noqa
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Args:
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params:
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iterable of parameters to optimize or dicts defining parameter groups
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model_size:
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attention dimension of the transformer model
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factor:
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learning rate factor
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warm_step:
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warmup steps
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"""
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def __init__(
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self,
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params,
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model_size: int = 256,
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factor: float = 10.0,
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warm_step: int = 25000,
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weight_decay=0,
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) -> None:
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"""Construct an Noam object."""
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self.optimizer = torch.optim.Adam(
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params, lr=0, betas=(0.9, 0.98), eps=1e-9, weight_decay=weight_decay
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)
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self._step = 0
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self.warmup = warm_step
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self.factor = factor
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self.model_size = model_size
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self._rate = 0
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@property
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def param_groups(self):
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"""Return param_groups."""
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return self.optimizer.param_groups
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def step(self):
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"""Update parameters and rate."""
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self._step += 1
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rate = self.rate()
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for p in self.optimizer.param_groups:
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p["lr"] = rate
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self._rate = rate
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self.optimizer.step()
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def rate(self, step=None):
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"""Implement `lrate` above."""
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if step is None:
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step = self._step
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return (
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self.factor
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* self.model_size ** (-0.5)
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* min(step ** (-0.5), step * self.warmup ** (-1.5))
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)
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def zero_grad(self):
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"""Reset gradient."""
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self.optimizer.zero_grad()
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def state_dict(self):
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"""Return state_dict."""
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return {
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"_step": self._step,
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"warmup": self.warmup,
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"factor": self.factor,
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"model_size": self.model_size,
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"_rate": self._rate,
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"optimizer": self.optimizer.state_dict(),
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}
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def load_state_dict(self, state_dict):
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"""Load state_dict."""
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for key, value in state_dict.items():
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if key == "optimizer":
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self.optimizer.load_state_dict(state_dict["optimizer"])
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else:
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setattr(self, key, value)
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