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88 lines
3.2 KiB
88 lines
3.2 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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"""format manifest with more metadata."""
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import argparse
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import functools
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import json
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from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
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from deepspeech.frontend.utility import load_cmvn
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from deepspeech.frontend.utility import read_manifest
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from deepspeech.utils.utility import add_arguments
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from deepspeech.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('feat_type', str, "raw", "speech feature type, e.g. raw(wav, flac), kaldi")
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add_arg('cmvn_path', str,
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'examples/librispeech/data/mean_std.json',
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"Filepath of cmvn.")
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add_arg('unit_type', str, "char", "Unit type, e.g. char, word, spm")
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add_arg('vocab_path', str,
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'examples/librispeech/data/vocab.txt',
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"Filepath of the vocabulary.")
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add_arg('manifest_paths', str,
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None,
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"Filepaths of manifests for building vocabulary. "
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"You can provide multiple manifest files.",
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nargs='+',
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required=True)
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# bpe
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add_arg('spm_model_prefix', str, None,
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"spm model prefix, spm_model_%(bpe_mode)_%(count_threshold), only need when `unit_type` is spm")
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add_arg('output_path', str, None, "filepath of formated manifest.", required=True)
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# yapf: disable
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args = parser.parse_args()
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def main():
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print_arguments(args, globals())
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fout = open(args.output_path, 'w', encoding='utf-8')
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# get feat dim
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mean, std = load_cmvn(args.cmvn_path, filetype='json')
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feat_dim = mean.shape[0] #(D)
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print(f"Feature dim: {feat_dim}")
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text_feature = TextFeaturizer(args.unit_type, args.vocab_path, args.spm_model_prefix)
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vocab_size = text_feature.vocab_size
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print(f"Vocab size: {vocab_size}")
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count = 0
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for manifest_path in args.manifest_paths:
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manifest_jsons = read_manifest(manifest_path)
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for line_json in manifest_jsons:
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line = line_json['text']
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tokens = text_feature.tokenize(line)
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tokenids = text_feature.featurize(line)
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line_json['token'] = tokens
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line_json['token_id'] = tokenids
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line_json['token_shape'] = (len(tokenids), vocab_size)
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feat_shape = line_json['feat_shape']
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assert isinstance(feat_shape, (list, tuple)), type(feat_shape)
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if args.feat_type == 'raw':
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feat_shape.append(feat_dim)
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else: # kaldi
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raise NotImplementedError('no support kaldi feat now!')
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fout.write(json.dumps(line_json) + '\n')
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count += 1
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print(f"Examples number: {count}")
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fout.close()
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if __name__ == '__main__':
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main()
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