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7934b29 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# 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 torch
from nemo.core.classes import NeuralModule, typecheck
from nemo.core.neural_types import LengthsType, LogprobsType, NeuralType, PredictionsType
class BeamSearchDecoderWithLM(NeuralModule):
"""Neural Module that does CTC beam search with a N-gram language model.
It takes a batch of log_probabilities. Note the bigger the batch, the
better as processing is parallelized. Outputs a list of size batch_size.
Each element in the list is a list of size beam_search, and each element
in that list is a tuple of (final_log_prob, hyp_string).
Args:
vocab (list): List of characters that can be output by the ASR model. For English, this is the 28 character set
{a-z '}. The CTC blank symbol is automatically added.
beam_width (int): Size of beams to keep and expand upon. Larger beams result in more accurate but slower
predictions
alpha (float): The amount of importance to place on the N-gram language model. Larger alpha means more
importance on the LM and less importance on the acoustic model.
beta (float): A penalty term given to longer word sequences. Larger beta will result in shorter sequences.
lm_path (str): Path to N-gram language model
num_cpus (int): Number of CPUs to use
cutoff_prob (float): Cutoff probability in vocabulary pruning, default 1.0, no pruning
cutoff_top_n (int): Cutoff number in pruning, only top cutoff_top_n characters with highest probs in
vocabulary will be used in beam search, default 40.
input_tensor (bool): Set to True if you intend to pass PyTorch Tensors, set to False if you intend to pass
NumPy arrays.
"""
@property
def input_types(self):
"""Returns definitions of module input ports.
"""
return {
"log_probs": NeuralType(('B', 'T', 'D'), LogprobsType()),
"log_probs_length": NeuralType(tuple('B'), LengthsType()),
}
@property
def output_types(self):
"""Returns definitions of module output ports.
"""
return {"predictions": NeuralType(('B', 'T'), PredictionsType())}
def __init__(
self, vocab, beam_width, alpha, beta, lm_path, num_cpus, cutoff_prob=1.0, cutoff_top_n=40, input_tensor=False
):
try:
from ctc_decoders import Scorer, ctc_beam_search_decoder_batch
except ModuleNotFoundError:
raise ModuleNotFoundError(
"BeamSearchDecoderWithLM requires the installation of ctc_decoders "
"from scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh"
)
super().__init__()
if lm_path is not None:
self.scorer = Scorer(alpha, beta, model_path=lm_path, vocabulary=vocab)
else:
self.scorer = None
self.beam_search_func = ctc_beam_search_decoder_batch
self.vocab = vocab
self.beam_width = beam_width
self.num_cpus = num_cpus
self.cutoff_prob = cutoff_prob
self.cutoff_top_n = cutoff_top_n
self.input_tensor = input_tensor
@typecheck(ignore_collections=True)
@torch.no_grad()
def forward(self, log_probs, log_probs_length):
probs_list = log_probs
if self.input_tensor:
probs = torch.exp(log_probs)
probs_list = []
for i, prob in enumerate(probs):
probs_list.append(prob[: log_probs_length[i], :])
res = self.beam_search_func(
probs_list,
self.vocab,
beam_size=self.beam_width,
num_processes=self.num_cpus,
ext_scoring_func=self.scorer,
cutoff_prob=self.cutoff_prob,
cutoff_top_n=self.cutoff_top_n,
)
return res
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