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https://github.com/k2-fsa/icefall.git
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b25c234c51
* Minor fix to conformer-mmi * Minor fixes * Fix decode.py * add training files * train with ctc warmup * add pruned_transducer_stateless7_mmi * add zipformer_mmi/mmi_decode.py, using HP as decoding graph * add mmi_decode.py * remove pruned_transducer_stateless7_mmi * rename zipformer_mmi/train_with_ctc.py as zipformer_mmi/train.py * remove unused method * rename mmi_decode.py * add export.py pretrained.py jit_pretrained.py ... * add RESULTS.md * add CI test * add docs * add README.md Co-authored-by: pkufool <wkang.pku@gmail.com>
76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
# Copyright 2022 Xiaomi Corp. (authors: Zengwei Yao)
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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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from typing import Tuple
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import torch
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import torch.nn as nn
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from encoder_interface import EncoderInterface
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class CTCModel(nn.Module):
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def __init__(
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self,
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encoder: EncoderInterface,
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encoder_dim: int,
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vocab_size: int,
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):
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"""
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Args:
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encoder:
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It is the transcription network in the paper. Its accepts
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two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
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It returns two tensors: `logits` of shape (N, T, encoder_dm) and
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`logit_lens` of shape (N,).
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"""
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super().__init__()
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assert isinstance(encoder, EncoderInterface), type(encoder)
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self.encoder = encoder
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self.ctc_output = nn.Sequential(
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nn.Dropout(p=0.1),
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nn.Linear(encoder_dim, vocab_size),
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nn.LogSoftmax(dim=-1),
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)
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def forward(
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self,
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x: torch.Tensor,
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x_lens: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Args:
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x:
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A 3-D tensor of shape (N, T, C).
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x_lens:
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A 1-D tensor of shape (N,). It contains the number of frames in `x`
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before padding.
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Returns:
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Return the ctc outputs and encoder output lengths.
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"""
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assert x.ndim == 3, x.shape
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assert x_lens.ndim == 1, x_lens.shape
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encoder_out, x_lens = self.encoder(x, x_lens)
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assert torch.all(x_lens > 0)
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# compute ctc log-probs
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ctc_output = self.ctc_output(encoder_out)
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return ctc_output, x_lens
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