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https://github.com/k2-fsa/icefall.git
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ece728d895
* add init files * fix bug, apply delay penalty * fix decoding code and getting timestamps * add option applying delay penalty on ctc log-prob * fix bug of streaming decoding * minor change for bpe-based case * add test_model.py * add README.md * add CI
123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang,
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# Wei Kang,
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# 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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import math
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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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from scaling import ScaledLinear
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class CTCModel(nn.Module):
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"""It implements https://www.cs.toronto.edu/~graves/icml_2006.pdf
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"Connectionist Temporal Classification: Labelling Unsegmented
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Sequence Data with Recurrent Neural Networks"
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"""
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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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encoder_dim:
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The feature embedding dimension.
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vocab_size:
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The vocabulary size.
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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_module = nn.Sequential(
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nn.Dropout(p=0.1),
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ScaledLinear(encoder_dim, vocab_size),
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)
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def get_ctc_output(
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self,
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encoder_out: torch.Tensor,
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delay_penalty: float = 0.0,
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blank_threshold: float = 0.99,
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):
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"""Compute ctc log-prob and optionally (delay_penalty > 0) apply delay penalty.
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We first split utterance into sub-utterances according to the
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blank probs, and then add sawtooth-like "blank-bonus" values to
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the blank probs.
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See https://github.com/k2-fsa/icefall/pull/669 for details.
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Args:
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encoder_out:
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A tensor with shape of (N, T, C).
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delay_penalty:
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A constant used to scale the delay penalty score.
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blank_threshold:
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The threshold used to split utterance into sub-utterances.
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"""
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output = self.ctc_output_module(encoder_out)
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log_prob = nn.functional.log_softmax(output, dim=-1)
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if self.training and delay_penalty > 0:
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T_arange = torch.arange(encoder_out.shape[1]).to(device=encoder_out.device)
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# split into sub-utterances using the blank-id
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mask = log_prob[:, :, 0] >= math.log(blank_threshold) # (B, T)
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mask[:, 0] = True
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cummax_out = (T_arange * mask).cummax(dim=-1)[0] # (B, T)
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# the sawtooth "blank-bonus" value
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penalty = T_arange - cummax_out # (B, T)
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penalty_all = torch.zeros_like(log_prob)
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penalty_all[:, :, 0] = delay_penalty * penalty
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# apply latency penalty on probs
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log_prob = log_prob + penalty_all
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return log_prob
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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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warmup: float = 1.0,
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delay_penalty: float = 0.0,
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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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warmup: a floating point value which increases throughout training;
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values >= 1.0 are fully warmed up and have all modules present.
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delay_penalty:
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A constant used to scale the delay penalty score.
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"""
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encoder_out, encoder_out_lens = self.encoder(x, x_lens, warmup=warmup)
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assert torch.all(encoder_out_lens > 0)
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nnet_output = self.get_ctc_output(encoder_out, delay_penalty=delay_penalty)
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return nnet_output, encoder_out_lens
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