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# Copyright 2021-2022 Xiaomi Corp. (authors: Fangjun Kuang,
# Wei Kang,
# Zengwei Yao)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Tuple
import torch
import torch.nn as nn
from encoder_interface import EncoderInterface
from scaling import ScaledLinear
class CTCModel(nn.Module):
"""It implements https://www.cs.toronto.edu/~graves/icml_2006.pdf
"Connectionist Temporal Classification: Labelling Unsegmented
Sequence Data with Recurrent Neural Networks"
"""
def __init__(
self,
encoder: EncoderInterface,
encoder_dim: int,
vocab_size: int,
):
"""
Args:
encoder:
It is the transcription network in the paper. Its accepts
two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
It returns two tensors: `logits` of shape (N, T, encoder_dm) and
`logit_lens` of shape (N,).
encoder_dim:
The feature embedding dimension.
vocab_size:
The vocabulary size.
"""
super().__init__()
assert isinstance(encoder, EncoderInterface), type(encoder)
self.encoder = encoder
self.ctc_output_module = nn.Sequential(
nn.Dropout(p=0.1),
ScaledLinear(encoder_dim, vocab_size),
)
def get_ctc_output(
self,
encoder_out: torch.Tensor,
delay_penalty: float = 0.0,
blank_threshold: float = 0.99,
):
"""Compute ctc log-prob and optionally (delay_penalty > 0) apply delay penalty.
We first split utterance into sub-utterances according to the
blank probs, and then add sawtooth-like "blank-bonus" values to
the blank probs.
See https://github.com/k2-fsa/icefall/pull/669 for details.
Args:
encoder_out:
A tensor with shape of (N, T, C).
delay_penalty:
A constant used to scale the delay penalty score.
blank_threshold:
The threshold used to split utterance into sub-utterances.
"""
output = self.ctc_output_module(encoder_out)
log_prob = nn.functional.log_softmax(output, dim=-1)
if self.training and delay_penalty > 0:
T_arange = torch.arange(encoder_out.shape[1]).to(device=encoder_out.device)
# split into sub-utterances using the blank-id
mask = log_prob[:, :, 0] >= math.log(blank_threshold) # (B, T)
mask[:, 0] = True
cummax_out = (T_arange * mask).cummax(dim=-1)[0] # (B, T)
# the sawtooth "blank-bonus" value
penalty = T_arange - cummax_out # (B, T)
penalty_all = torch.zeros_like(log_prob)
penalty_all[:, :, 0] = delay_penalty * penalty
# apply latency penalty on probs
log_prob = log_prob + penalty_all
return log_prob
def forward(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
warmup: float = 1.0,
delay_penalty: float = 0.0,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Args:
x:
A 3-D tensor of shape (N, T, C).
x_lens:
A 1-D tensor of shape (N,). It contains the number of frames in `x`
before padding.
warmup: a floating point value which increases throughout training;
values >= 1.0 are fully warmed up and have all modules present.
delay_penalty:
A constant used to scale the delay penalty score.
"""
encoder_out, encoder_out_lens = self.encoder(x, x_lens, warmup=warmup)
assert torch.all(encoder_out_lens > 0)
nnet_output = self.get_ctc_output(encoder_out, delay_penalty=delay_penalty)
return nnet_output, encoder_out_lens
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