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164 lines
6.1 KiB
164 lines
6.1 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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"""Inferer for DeepSpeech2 model."""
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import argparse
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import functools
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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from data_utils.data import DataGenerator
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from model_utils.model_check import check_cuda
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from model_utils.model_check import check_version
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from deepspeech.models.ds2 import DeepSpeech2Model as DS2
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from utils.error_rate import cer
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from utils.error_rate import wer
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from utils.utility import add_arguments
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from utils.utility import print_arguments
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parser = argparse.ArgumentParser(description=__doc__)
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add_arg = functools.partial(add_arguments, argparser=parser)
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# yapf: disable
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add_arg('num_samples', int, 10, "# of samples to infer.")
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add_arg('beam_size', int, 500, "Beam search width.")
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add_arg('feat_dim', int, 161, "Feature dim.")
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add_arg('num_proc_bsearch', int, 8, "# of CPUs for beam search.")
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add_arg('num_conv_layers', int, 2, "# of convolution layers.")
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add_arg('num_rnn_layers', int, 3, "# of recurrent layers.")
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add_arg('rnn_layer_size', int, 2048, "# of recurrent cells per layer.")
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add_arg('alpha', float, 2.5, "Coef of LM for beam search.")
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add_arg('beta', float, 0.3, "Coef of WC for beam search.")
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add_arg('cutoff_prob', float, 1.0, "Cutoff probability for pruning.")
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add_arg('cutoff_top_n', int, 40, "Cutoff number for pruning.")
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add_arg('use_gru', bool, False, "Use GRUs instead of simple RNNs.")
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add_arg('use_gpu', bool, True, "Use GPU or not.")
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add_arg('share_rnn_weights', bool, True, "Share input-hidden weights across bi-directional RNNs. Not for GRU.")
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add_arg('infer_manifest', str,
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'data/librispeech/manifest.dev-clean',
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"Filepath of manifest to infer.")
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add_arg('mean_std_path', str,
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'data/librispeech/mean_std.npz',
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"Filepath of normalizer's mean & std.")
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add_arg('vocab_path', str,
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'data/librispeech/vocab.txt',
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"Filepath of vocabulary.")
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add_arg('lang_model_path', str,
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'models/lm/common_crawl_00.prune01111.trie.klm',
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"Filepath for language model.")
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add_arg('model_path', str,
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'./checkpoints/libri/step_final',
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"If None, the training starts from scratch, "
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"otherwise, it resumes from the pre-trained model.")
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add_arg('decoding_method', str,
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'ctc_beam_search',
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"Decoding method. Options: ctc_beam_search, ctc_greedy",
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choices=['ctc_beam_search', 'ctc_greedy'])
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add_arg('error_rate_type', str,
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'wer',
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"Error rate type for evaluation.",
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choices=['wer', 'cer'])
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add_arg('specgram_type', str,
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'linear',
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"Audio feature type. Options: linear, mfcc.",
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choices=['linear', 'mfcc'])
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# yapf: disable
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args = parser.parse_args()
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def infer():
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"""Inference for DeepSpeech2."""
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# check if set use_gpu=True in paddlepaddle cpu version
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check_cuda(args.use_gpu)
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# check if paddlepaddle version is satisfied
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check_version()
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if args.use_gpu:
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place = fluid.CUDAPlace(0)
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else:
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place = fluid.CPUPlace()
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data_generator = DataGenerator(
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vocab_filepath=args.vocab_path,
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mean_std_filepath=args.mean_std_path,
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augmentation_config='{}',
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specgram_type=args.specgram_type,
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keep_transcription_text=True,
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place=place,
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is_training=False)
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batch_reader = data_generator.batch_reader_creator(
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manifest_path=args.infer_manifest,
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batch_size=args.num_samples,
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sortagrad=False,
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shuffle_method=None)
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# decoders only accept string encoded in utf-8
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vocab_list = [chars for chars in data_generator.vocab_list]
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for i, char in enumerate(vocab_list):
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if vocab_list[i] == '':
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vocab_list[i] = " "
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model = DS2(
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feat_size=args.feat_dim,
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dict_size=len(vocab_list),
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num_conv_layers=args.num_conv_layers,
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num_rnn_layers=args.num_rnn_layers,
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rnn_size=args.rnn_layer_size,
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use_gru=args.use_gru,
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share_rnn_weights=args.share_rnn_weights,
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blank_id=len(vocab_list) - 1
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)
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params_path = args.model_path
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model_dict = paddle.load(params_path)
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model.set_state_dict(model_dict)
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model.eval()
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error_rate_func = cer if args.error_rate_type == 'cer' else wer
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print("start inference ...")
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for infer_data in batch_reader():
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target_transcripts = infer_data[1]
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audio, target_transcripts, audio_len, mask = infer_data
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audio = np.transpose(audio, (0, 2, 1))
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audio_len = audio_len.reshape(-1)
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audio = paddle.to_tensor(audio)
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audio_len = paddle.to_tensor(audio_len)
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result_transcripts = model.decode(
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audio=audio,
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audio_len=audio_len,
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lang_model_path=args.lang_model_path,
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decoding_method=args.decoding_method,
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beam_alpha=args.alpha,
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beam_beta=args.beta,
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beam_size=args.beam_size,
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cutoff_prob=args.cutoff_prob,
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cutoff_top_n=args.cutoff_top_n,
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vocab_list=vocab_list,
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num_processes=args.num_proc_bsearch
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)
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for target, result in zip(target_transcripts, result_transcripts):
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print("\nTarget Transcription: %s\nOutput Transcription: %s" %
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(target, result))
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print("Current error rate [%s] = %f" %
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(args.error_rate_type, error_rate_func(target, result)))
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print("finish inference")
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def main():
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print_arguments(args)
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infer()
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if __name__ == '__main__':
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main()
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