Merge branch 'develop' of https://github.com/LittleChenCc/DeepSpeech into develop
Junkun
3 years ago
commit
46df01151f
@ -0,0 +1,191 @@
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# 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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"""Evaluation for DeepSpeech2 model."""
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import os
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import sys
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from pathlib import Path
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import paddle
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from deepspeech.exps.deepspeech2.config import get_cfg_defaults
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from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
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from deepspeech.io.collator import SpeechCollator
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from deepspeech.models.ds2 import DeepSpeech2Model
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from deepspeech.models.ds2_online import DeepSpeech2ModelOnline
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from deepspeech.training.cli import default_argument_parser
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from deepspeech.utils import mp_tools
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from deepspeech.utils.checkpoint import Checkpoint
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from deepspeech.utils.log import Log
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from deepspeech.utils.utility import print_arguments
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from deepspeech.utils.utility import UpdateConfig
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logger = Log(__name__).getlog()
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class DeepSpeech2Tester_hub():
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def __init__(self, config, args):
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self.args = args
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self.config = config
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self.audio_file = args.audio_file
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self.collate_fn_test = SpeechCollator.from_config(config)
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self._text_featurizer = TextFeaturizer(
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unit_type=config.collator.unit_type, vocab_filepath=None)
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def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
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result_transcripts = self.model.decode(
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audio,
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audio_len,
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vocab_list,
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decoding_method=cfg.decoding_method,
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lang_model_path=cfg.lang_model_path,
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beam_alpha=cfg.alpha,
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beam_beta=cfg.beta,
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beam_size=cfg.beam_size,
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cutoff_prob=cfg.cutoff_prob,
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cutoff_top_n=cfg.cutoff_top_n,
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num_processes=cfg.num_proc_bsearch)
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#replace the '<space>' with ' '
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result_transcripts = [
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self._text_featurizer.detokenize(sentence)
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for sentence in result_transcripts
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]
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return result_transcripts
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@mp_tools.rank_zero_only
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@paddle.no_grad()
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def test(self):
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self.model.eval()
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cfg = self.config
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audio_file = self.audio_file
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collate_fn_test = self.collate_fn_test
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audio, _ = collate_fn_test.process_utterance(
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audio_file=audio_file, transcript=" ")
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audio_len = audio.shape[0]
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audio = paddle.to_tensor(audio, dtype='float32')
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audio_len = paddle.to_tensor(audio_len)
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audio = paddle.unsqueeze(audio, axis=0)
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vocab_list = collate_fn_test.vocab_list
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result_transcripts = self.compute_result_transcripts(
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audio, audio_len, vocab_list, cfg.decoding)
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logger.info("result_transcripts: " + result_transcripts[0])
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def run_test(self):
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self.resume()
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try:
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self.test()
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except KeyboardInterrupt:
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exit(-1)
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def setup(self):
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"""Setup the experiment.
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"""
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paddle.set_device('gpu' if self.args.nprocs > 0 else 'cpu')
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self.setup_output_dir()
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self.setup_checkpointer()
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self.setup_model()
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def setup_output_dir(self):
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"""Create a directory used for output.
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"""
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# output dir
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if self.args.output:
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output_dir = Path(self.args.output).expanduser()
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output_dir.mkdir(parents=True, exist_ok=True)
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else:
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output_dir = Path(
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self.args.checkpoint_path).expanduser().parent.parent
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output_dir.mkdir(parents=True, exist_ok=True)
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self.output_dir = output_dir
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def setup_model(self):
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config = self.config.clone()
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with UpdateConfig(config):
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config.model.feat_size = self.collate_fn_test.feature_size
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config.model.dict_size = self.collate_fn_test.vocab_size
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if self.args.model_type == 'offline':
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model = DeepSpeech2Model.from_config(config.model)
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elif self.args.model_type == 'online':
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model = DeepSpeech2ModelOnline.from_config(config.model)
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else:
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raise Exception("wrong model type")
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self.model = model
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def setup_checkpointer(self):
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"""Create a directory used to save checkpoints into.
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It is "checkpoints" inside the output directory.
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"""
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# checkpoint dir
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checkpoint_dir = self.output_dir / "checkpoints"
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checkpoint_dir.mkdir(exist_ok=True)
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self.checkpoint_dir = checkpoint_dir
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self.checkpoint = Checkpoint(
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kbest_n=self.config.training.checkpoint.kbest_n,
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latest_n=self.config.training.checkpoint.latest_n)
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def resume(self):
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"""Resume from the checkpoint at checkpoints in the output
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directory or load a specified checkpoint.
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"""
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params_path = self.args.checkpoint_path + ".pdparams"
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model_dict = paddle.load(params_path)
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self.model.set_state_dict(model_dict)
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def main_sp(config, args):
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exp = DeepSpeech2Tester_hub(config, args)
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exp.setup()
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exp.run_test()
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def main(config, args):
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main_sp(config, args)
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if __name__ == "__main__":
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parser = default_argument_parser()
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parser.add_argument("--model_type")
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parser.add_argument("--audio_file")
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# save asr result to
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parser.add_argument(
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"--result_file", type=str, help="path of save the asr result")
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args = parser.parse_args()
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print_arguments(args, globals())
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if args.model_type is None:
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args.model_type = 'offline'
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if not os.path.isfile(args.audio_file):
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print("Please input the audio file path")
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sys.exit(-1)
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print("model_type:{}".format(args.model_type))
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# https://yaml.org/type/float.html
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config = get_cfg_defaults(args.model_type)
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if args.config:
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config.merge_from_file(args.config)
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if args.opts:
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config.merge_from_list(args.opts)
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config.freeze()
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print(config)
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if args.dump_config:
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with open(args.dump_config, 'w') as f:
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print(config, file=f)
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main(config, args)
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@ -1,631 +0,0 @@
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# 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
|
||||
# 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 io
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from collections import namedtuple
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from typing import Optional
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import kaldiio
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import numpy as np
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from yacs.config import CfgNode
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from deepspeech.frontend.augmentor.augmentation import AugmentationPipeline
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from deepspeech.frontend.featurizer.speech_featurizer import SpeechFeaturizer
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from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
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from deepspeech.frontend.normalizer import FeatureNormalizer
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from deepspeech.frontend.speech import SpeechSegment
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from deepspeech.frontend.utility import IGNORE_ID
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from deepspeech.io.utility import pad_sequence
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from deepspeech.utils.log import Log
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__all__ = ["SpeechCollator", "KaldiPrePorocessedCollator"]
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logger = Log(__name__).getlog()
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# namedtupe need global for pickle.
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TarLocalData = namedtuple('TarLocalData', ['tar2info', 'tar2object'])
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class SpeechCollator():
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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default = CfgNode(
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dict(
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augmentation_config="",
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random_seed=0,
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mean_std_filepath="",
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unit_type="char",
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vocab_filepath="",
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spm_model_prefix="",
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specgram_type='linear', # 'linear', 'mfcc', 'fbank'
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feat_dim=0, # 'mfcc', 'fbank'
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delta_delta=False, # 'mfcc', 'fbank'
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stride_ms=10.0, # ms
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window_ms=20.0, # ms
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n_fft=None, # fft points
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max_freq=None, # None for samplerate/2
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target_sample_rate=16000, # target sample rate
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use_dB_normalization=True,
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target_dB=-20,
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dither=1.0, # feature dither
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keep_transcription_text=False))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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@classmethod
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def from_config(cls, config):
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"""Build a SpeechCollator object from a config.
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Args:
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config (yacs.config.CfgNode): configs object.
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Returns:
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SpeechCollator: collator object.
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"""
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assert 'augmentation_config' in config.collator
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assert 'keep_transcription_text' in config.collator
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assert 'mean_std_filepath' in config.collator
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assert 'vocab_filepath' in config.collator
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assert 'specgram_type' in config.collator
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assert 'n_fft' in config.collator
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assert config.collator
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if isinstance(config.collator.augmentation_config, (str, bytes)):
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if config.collator.augmentation_config:
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aug_file = io.open(
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config.collator.augmentation_config,
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mode='r',
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encoding='utf8')
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else:
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aug_file = io.StringIO(initial_value='{}', newline='')
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else:
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aug_file = config.collator.augmentation_config
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assert isinstance(aug_file, io.StringIO)
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speech_collator = cls(
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aug_file=aug_file,
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random_seed=0,
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mean_std_filepath=config.collator.mean_std_filepath,
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unit_type=config.collator.unit_type,
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vocab_filepath=config.collator.vocab_filepath,
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spm_model_prefix=config.collator.spm_model_prefix,
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specgram_type=config.collator.specgram_type,
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feat_dim=config.collator.feat_dim,
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delta_delta=config.collator.delta_delta,
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stride_ms=config.collator.stride_ms,
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window_ms=config.collator.window_ms,
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n_fft=config.collator.n_fft,
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max_freq=config.collator.max_freq,
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target_sample_rate=config.collator.target_sample_rate,
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use_dB_normalization=config.collator.use_dB_normalization,
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target_dB=config.collator.target_dB,
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dither=config.collator.dither,
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keep_transcription_text=config.collator.keep_transcription_text)
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return speech_collator
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def __init__(
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self,
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aug_file,
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mean_std_filepath,
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vocab_filepath,
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spm_model_prefix,
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random_seed=0,
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unit_type="char",
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specgram_type='linear', # 'linear', 'mfcc', 'fbank'
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feat_dim=0, # 'mfcc', 'fbank'
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delta_delta=False, # 'mfcc', 'fbank'
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stride_ms=10.0, # ms
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window_ms=20.0, # ms
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n_fft=None, # fft points
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max_freq=None, # None for samplerate/2
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target_sample_rate=16000, # target sample rate
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use_dB_normalization=True,
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target_dB=-20,
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dither=1.0,
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keep_transcription_text=True):
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"""SpeechCollator Collator
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Args:
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unit_type(str): token unit type, e.g. char, word, spm
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vocab_filepath (str): vocab file path.
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mean_std_filepath (str): mean and std file path, which suffix is *.npy
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spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
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augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
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stride_ms (float, optional): stride size in ms. Defaults to 10.0.
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window_ms (float, optional): window size in ms. Defaults to 20.0.
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n_fft (int, optional): fft points for rfft. Defaults to None.
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max_freq (int, optional): max cut freq. Defaults to None.
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target_sample_rate (int, optional): target sample rate which used for training. Defaults to 16000.
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specgram_type (str, optional): 'linear', 'mfcc' or 'fbank'. Defaults to 'linear'.
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feat_dim (int, optional): audio feature dim, using by 'mfcc' or 'fbank'. Defaults to None.
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delta_delta (bool, optional): audio feature with delta-delta, using by 'fbank' or 'mfcc'. Defaults to False.
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use_dB_normalization (bool, optional): do dB normalization. Defaults to True.
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target_dB (int, optional): target dB. Defaults to -20.
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random_seed (int, optional): for random generator. Defaults to 0.
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keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
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if ``keep_transcription_text`` is False, text is token ids else is raw string.
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Do augmentations
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Padding audio features with zeros to make them have the same shape (or
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a user-defined shape) within one batch.
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"""
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self._keep_transcription_text = keep_transcription_text
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self._local_data = TarLocalData(tar2info={}, tar2object={})
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self._augmentation_pipeline = AugmentationPipeline(
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augmentation_config=aug_file.read(), random_seed=random_seed)
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self._normalizer = FeatureNormalizer(
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mean_std_filepath) if mean_std_filepath else None
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self._stride_ms = stride_ms
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self._target_sample_rate = target_sample_rate
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self._speech_featurizer = SpeechFeaturizer(
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unit_type=unit_type,
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vocab_filepath=vocab_filepath,
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spm_model_prefix=spm_model_prefix,
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specgram_type=specgram_type,
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feat_dim=feat_dim,
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delta_delta=delta_delta,
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stride_ms=stride_ms,
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window_ms=window_ms,
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n_fft=n_fft,
|
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max_freq=max_freq,
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target_sample_rate=target_sample_rate,
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use_dB_normalization=use_dB_normalization,
|
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target_dB=target_dB,
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dither=dither)
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def _parse_tar(self, file):
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"""Parse a tar file to get a tarfile object
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and a map containing tarinfoes
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"""
|
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result = {}
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f = tarfile.open(file)
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for tarinfo in f.getmembers():
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result[tarinfo.name] = tarinfo
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return f, result
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def _subfile_from_tar(self, file):
|
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"""Get subfile object from tar.
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||||
It will return a subfile object from tar file
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and cached tar file info for next reading request.
|
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"""
|
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tarpath, filename = file.split(':', 1)[1].split('#', 1)
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if 'tar2info' not in self._local_data.__dict__:
|
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self._local_data.tar2info = {}
|
||||
if 'tar2object' not in self._local_data.__dict__:
|
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self._local_data.tar2object = {}
|
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if tarpath not in self._local_data.tar2info:
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object, infoes = self._parse_tar(tarpath)
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self._local_data.tar2info[tarpath] = infoes
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self._local_data.tar2object[tarpath] = object
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return self._local_data.tar2object[tarpath].extractfile(
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self._local_data.tar2info[tarpath][filename])
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|
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@property
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def manifest(self):
|
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return self._manifest
|
||||
|
||||
@property
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def vocab_size(self):
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return self._speech_featurizer.vocab_size
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@property
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def vocab_list(self):
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return self._speech_featurizer.vocab_list
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|
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@property
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def vocab_dict(self):
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return self._speech_featurizer.vocab_dict
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|
||||
@property
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def text_feature(self):
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return self._speech_featurizer.text_feature
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|
||||
@property
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def feature_size(self):
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return self._speech_featurizer.feature_size
|
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|
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@property
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def stride_ms(self):
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return self._speech_featurizer.stride_ms
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def process_utterance(self, audio_file, translation):
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"""Load, augment, featurize and normalize for speech data.
|
||||
|
||||
:param audio_file: Filepath or file object of audio file.
|
||||
:type audio_file: str | file
|
||||
:param translation: translation text.
|
||||
:type translation: str
|
||||
:return: Tuple of audio feature tensor and data of translation part,
|
||||
where translation part could be token ids or text.
|
||||
:rtype: tuple of (2darray, list)
|
||||
"""
|
||||
if isinstance(audio_file, str) and audio_file.startswith('tar:'):
|
||||
speech_segment = SpeechSegment.from_file(
|
||||
self._subfile_from_tar(audio_file), translation)
|
||||
else:
|
||||
speech_segment = SpeechSegment.from_file(audio_file, translation)
|
||||
|
||||
# audio augment
|
||||
self._augmentation_pipeline.transform_audio(speech_segment)
|
||||
|
||||
specgram, translation_part = self._speech_featurizer.featurize(
|
||||
speech_segment, self._keep_transcription_text)
|
||||
if self._normalizer:
|
||||
specgram = self._normalizer.apply(specgram)
|
||||
|
||||
# specgram augment
|
||||
specgram = self._augmentation_pipeline.transform_feature(specgram)
|
||||
return specgram, translation_part
|
||||
|
||||
def __call__(self, batch):
|
||||
"""batch examples
|
||||
|
||||
Args:
|
||||
batch ([List]): batch is (audio, text)
|
||||
audio (np.ndarray) shape (T, D)
|
||||
text (List[int] or str): shape (U,)
|
||||
|
||||
Returns:
|
||||
tuple(audio, text, audio_lens, text_lens): batched data.
|
||||
audio : (B, Tmax, D)
|
||||
audio_lens: (B)
|
||||
text : (B, Umax)
|
||||
text_lens: (B)
|
||||
"""
|
||||
audios = []
|
||||
audio_lens = []
|
||||
texts = []
|
||||
text_lens = []
|
||||
utts = []
|
||||
for utt, audio, text in batch:
|
||||
audio, text = self.process_utterance(audio, text)
|
||||
#utt
|
||||
utts.append(utt)
|
||||
# audio
|
||||
audios.append(audio) # [T, D]
|
||||
audio_lens.append(audio.shape[0])
|
||||
# text
|
||||
# for training, text is token ids
|
||||
# else text is string, convert to unicode ord
|
||||
tokens = []
|
||||
if self._keep_transcription_text:
|
||||
assert isinstance(text, str), (type(text), text)
|
||||
tokens = [ord(t) for t in text]
|
||||
else:
|
||||
tokens = text # token ids
|
||||
tokens = tokens if isinstance(tokens, np.ndarray) else np.array(
|
||||
tokens, dtype=np.int64)
|
||||
texts.append(tokens)
|
||||
text_lens.append(tokens.shape[0])
|
||||
|
||||
padded_audios = pad_sequence(
|
||||
audios, padding_value=0.0).astype(np.float32) #[B, T, D]
|
||||
audio_lens = np.array(audio_lens).astype(np.int64)
|
||||
padded_texts = pad_sequence(
|
||||
texts, padding_value=IGNORE_ID).astype(np.int64)
|
||||
text_lens = np.array(text_lens).astype(np.int64)
|
||||
return utts, padded_audios, audio_lens, padded_texts, text_lens
|
||||
|
||||
|
||||
class TripletSpeechCollator(SpeechCollator):
|
||||
def process_utterance(self, audio_file, translation, transcript):
|
||||
"""Load, augment, featurize and normalize for speech data.
|
||||
|
||||
:param audio_file: Filepath or file object of audio file.
|
||||
:type audio_file: str | file
|
||||
:param translation: translation text.
|
||||
:type translation: str
|
||||
:return: Tuple of audio feature tensor and data of translation part,
|
||||
where translation part could be token ids or text.
|
||||
:rtype: tuple of (2darray, list)
|
||||
"""
|
||||
if isinstance(audio_file, str) and audio_file.startswith('tar:'):
|
||||
speech_segment = SpeechSegment.from_file(
|
||||
self._subfile_from_tar(audio_file), translation)
|
||||
else:
|
||||
speech_segment = SpeechSegment.from_file(audio_file, translation)
|
||||
|
||||
# audio augment
|
||||
self._augmentation_pipeline.transform_audio(speech_segment)
|
||||
|
||||
specgram, translation_part = self._speech_featurizer.featurize(
|
||||
speech_segment, self._keep_transcription_text)
|
||||
transcript_part = self._speech_featurizer._text_featurizer.featurize(
|
||||
transcript)
|
||||
if self._normalizer:
|
||||
specgram = self._normalizer.apply(specgram)
|
||||
|
||||
# specgram augment
|
||||
specgram = self._augmentation_pipeline.transform_feature(specgram)
|
||||
return specgram, translation_part, transcript_part
|
||||
|
||||
def __call__(self, batch):
|
||||
"""batch examples
|
||||
|
||||
Args:
|
||||
batch ([List]): batch is (audio, text)
|
||||
audio (np.ndarray) shape (T, D)
|
||||
text (List[int] or str): shape (U,)
|
||||
|
||||
Returns:
|
||||
tuple(audio, text, audio_lens, text_lens): batched data.
|
||||
audio : (B, Tmax, D)
|
||||
audio_lens: (B)
|
||||
text : (B, Umax)
|
||||
text_lens: (B)
|
||||
"""
|
||||
audios = []
|
||||
audio_lens = []
|
||||
translation_text = []
|
||||
translation_text_lens = []
|
||||
transcription_text = []
|
||||
transcription_text_lens = []
|
||||
|
||||
utts = []
|
||||
for utt, audio, translation, transcription in batch:
|
||||
audio, translation, transcription = self.process_utterance(
|
||||
audio, translation, transcription)
|
||||
#utt
|
||||
utts.append(utt)
|
||||
# audio
|
||||
audios.append(audio) # [T, D]
|
||||
audio_lens.append(audio.shape[0])
|
||||
# text
|
||||
# for training, text is token ids
|
||||
# else text is string, convert to unicode ord
|
||||
tokens = [[], []]
|
||||
for idx, text in enumerate([translation, transcription]):
|
||||
if self._keep_transcription_text:
|
||||
assert isinstance(text, str), (type(text), text)
|
||||
tokens[idx] = [ord(t) for t in text]
|
||||
else:
|
||||
tokens[idx] = text # token ids
|
||||
tokens[idx] = tokens[idx] if isinstance(
|
||||
tokens[idx], np.ndarray) else np.array(
|
||||
tokens[idx], dtype=np.int64)
|
||||
translation_text.append(tokens[0])
|
||||
translation_text_lens.append(tokens[0].shape[0])
|
||||
transcription_text.append(tokens[1])
|
||||
transcription_text_lens.append(tokens[1].shape[0])
|
||||
|
||||
padded_audios = pad_sequence(
|
||||
audios, padding_value=0.0).astype(np.float32) #[B, T, D]
|
||||
audio_lens = np.array(audio_lens).astype(np.int64)
|
||||
padded_translation = pad_sequence(
|
||||
translation_text, padding_value=IGNORE_ID).astype(np.int64)
|
||||
translation_lens = np.array(translation_text_lens).astype(np.int64)
|
||||
padded_transcription = pad_sequence(
|
||||
transcription_text, padding_value=IGNORE_ID).astype(np.int64)
|
||||
transcription_lens = np.array(transcription_text_lens).astype(np.int64)
|
||||
return utts, padded_audios, audio_lens, (
|
||||
padded_translation, padded_transcription), (translation_lens,
|
||||
transcription_lens)
|
||||
|
||||
|
||||
class KaldiPrePorocessedCollator(SpeechCollator):
|
||||
@classmethod
|
||||
def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
|
||||
default = CfgNode(
|
||||
dict(
|
||||
augmentation_config="",
|
||||
random_seed=0,
|
||||
unit_type="char",
|
||||
vocab_filepath="",
|
||||
spm_model_prefix="",
|
||||
feat_dim=0,
|
||||
stride_ms=10.0,
|
||||
keep_transcription_text=False))
|
||||
|
||||
if config is not None:
|
||||
config.merge_from_other_cfg(default)
|
||||
return default
|
||||
|
||||
@classmethod
|
||||
def from_config(cls, config):
|
||||
"""Build a SpeechCollator object from a config.
|
||||
|
||||
Args:
|
||||
config (yacs.config.CfgNode): configs object.
|
||||
|
||||
Returns:
|
||||
SpeechCollator: collator object.
|
||||
"""
|
||||
assert 'augmentation_config' in config.collator
|
||||
assert 'keep_transcription_text' in config.collator
|
||||
assert 'vocab_filepath' in config.collator
|
||||
assert config.collator
|
||||
|
||||
if isinstance(config.collator.augmentation_config, (str, bytes)):
|
||||
if config.collator.augmentation_config:
|
||||
aug_file = io.open(
|
||||
config.collator.augmentation_config,
|
||||
mode='r',
|
||||
encoding='utf8')
|
||||
else:
|
||||
aug_file = io.StringIO(initial_value='{}', newline='')
|
||||
else:
|
||||
aug_file = config.collator.augmentation_config
|
||||
assert isinstance(aug_file, io.StringIO)
|
||||
|
||||
speech_collator = cls(
|
||||
aug_file=aug_file,
|
||||
random_seed=0,
|
||||
unit_type=config.collator.unit_type,
|
||||
vocab_filepath=config.collator.vocab_filepath,
|
||||
spm_model_prefix=config.collator.spm_model_prefix,
|
||||
feat_dim=config.collator.feat_dim,
|
||||
stride_ms=config.collator.stride_ms,
|
||||
keep_transcription_text=config.collator.keep_transcription_text)
|
||||
return speech_collator
|
||||
|
||||
def __init__(self,
|
||||
aug_file,
|
||||
vocab_filepath,
|
||||
spm_model_prefix,
|
||||
random_seed=0,
|
||||
unit_type="char",
|
||||
feat_dim=0,
|
||||
stride_ms=10.0,
|
||||
keep_transcription_text=True):
|
||||
"""SpeechCollator Collator
|
||||
|
||||
Args:
|
||||
unit_type(str): token unit type, e.g. char, word, spm
|
||||
vocab_filepath (str): vocab file path.
|
||||
spm_model_prefix (str): spm model prefix, need if `unit_type` is spm.
|
||||
augmentation_config (str, optional): augmentation json str. Defaults to '{}'.
|
||||
random_seed (int, optional): for random generator. Defaults to 0.
|
||||
keep_transcription_text (bool, optional): True, when not in training mode, will not do tokenizer; Defaults to False.
|
||||
if ``keep_transcription_text`` is False, text is token ids else is raw string.
|
||||
|
||||
Do augmentations
|
||||
Padding audio features with zeros to make them have the same shape (or
|
||||
a user-defined shape) within one batch.
|
||||
"""
|
||||
self._keep_transcription_text = keep_transcription_text
|
||||
self._feat_dim = feat_dim
|
||||
self._stride_ms = stride_ms
|
||||
|
||||
self._local_data = TarLocalData(tar2info={}, tar2object={})
|
||||
self._augmentation_pipeline = AugmentationPipeline(
|
||||
augmentation_config=aug_file.read(), random_seed=random_seed)
|
||||
|
||||
self._text_featurizer = TextFeaturizer(unit_type, vocab_filepath,
|
||||
spm_model_prefix)
|
||||
|
||||
def process_utterance(self, audio_file, translation):
|
||||
"""Load, augment, featurize and normalize for speech data.
|
||||
|
||||
:param audio_file: Filepath or file object of kaldi processed feature.
|
||||
:type audio_file: str | file
|
||||
:param translation: Translation text.
|
||||
:type translation: str
|
||||
:return: Tuple of audio feature tensor and data of translation part,
|
||||
where translation part could be token ids or text.
|
||||
:rtype: tuple of (2darray, list)
|
||||
"""
|
||||
specgram = kaldiio.load_mat(audio_file)
|
||||
assert specgram.shape[
|
||||
1] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
|
||||
self._feat_dim, specgram.shape[1])
|
||||
|
||||
# specgram augment
|
||||
specgram = self._augmentation_pipeline.transform_feature(specgram)
|
||||
|
||||
if self._keep_transcription_text:
|
||||
return specgram, translation
|
||||
else:
|
||||
text_ids = self._text_featurizer.featurize(translation)
|
||||
return specgram, text_ids
|
||||
|
||||
|
||||
class TripletKaldiPrePorocessedCollator(KaldiPrePorocessedCollator):
|
||||
def process_utterance(self, audio_file, translation, transcript):
|
||||
"""Load, augment, featurize and normalize for speech data.
|
||||
|
||||
:param audio_file: Filepath or file object of kali processed feature.
|
||||
:type audio_file: str | file
|
||||
:param translation: Translation text.
|
||||
:type translation: str
|
||||
:param transcript: Transcription text.
|
||||
:type transcript: str
|
||||
:return: Tuple of audio feature tensor and data of translation and transcription parts,
|
||||
where translation and transcription parts could be token ids or text.
|
||||
:rtype: tuple of (2darray, (list, list))
|
||||
"""
|
||||
specgram = kaldiio.load_mat(audio_file)
|
||||
assert specgram.shape[
|
||||
1] == self._feat_dim, 'expect feat dim {}, but got {}'.format(
|
||||
self._feat_dim, specgram.shape[1])
|
||||
|
||||
# specgram augment
|
||||
specgram = self._augmentation_pipeline.transform_feature(specgram)
|
||||
|
||||
if self._keep_transcription_text:
|
||||
return specgram, translation, transcript
|
||||
else:
|
||||
translation_text_ids = self._text_featurizer.featurize(translation)
|
||||
transcript_text_ids = self._text_featurizer.featurize(transcript)
|
||||
return specgram, translation_text_ids, transcript_text_ids
|
||||
|
||||
def __call__(self, batch):
|
||||
"""batch examples
|
||||
|
||||
Args:
|
||||
batch ([List]): batch is (audio, text)
|
||||
audio (np.ndarray) shape (T, D)
|
||||
translation (List[int] or str): shape (U,)
|
||||
transcription (List[int] or str): shape (V,)
|
||||
|
||||
Returns:
|
||||
tuple(audio, text, audio_lens, text_lens): batched data.
|
||||
audio : (B, Tmax, D)
|
||||
audio_lens: (B)
|
||||
translation_text : (B, Umax)
|
||||
translation_text_lens: (B)
|
||||
transcription_text : (B, Vmax)
|
||||
transcription_text_lens: (B)
|
||||
"""
|
||||
audios = []
|
||||
audio_lens = []
|
||||
translation_text = []
|
||||
translation_text_lens = []
|
||||
transcription_text = []
|
||||
transcription_text_lens = []
|
||||
|
||||
utts = []
|
||||
for utt, audio, translation, transcription in batch:
|
||||
audio, translation, transcription = self.process_utterance(
|
||||
audio, translation, transcription)
|
||||
#utt
|
||||
utts.append(utt)
|
||||
# audio
|
||||
audios.append(audio) # [T, D]
|
||||
audio_lens.append(audio.shape[0])
|
||||
# text
|
||||
# for training, text is token ids
|
||||
# else text is string, convert to unicode ord
|
||||
tokens = [[], []]
|
||||
for idx, text in enumerate([translation, transcription]):
|
||||
if self._keep_transcription_text:
|
||||
assert isinstance(text, str), (type(text), text)
|
||||
tokens[idx] = [ord(t) for t in text]
|
||||
else:
|
||||
tokens[idx] = text # token ids
|
||||
tokens[idx] = tokens[idx] if isinstance(
|
||||
tokens[idx], np.ndarray) else np.array(
|
||||
tokens[idx], dtype=np.int64)
|
||||
translation_text.append(tokens[0])
|
||||
translation_text_lens.append(tokens[0].shape[0])
|
||||
transcription_text.append(tokens[1])
|
||||
transcription_text_lens.append(tokens[1].shape[0])
|
||||
|
||||
padded_audios = pad_sequence(
|
||||
audios, padding_value=0.0).astype(np.float32) #[B, T, D]
|
||||
audio_lens = np.array(audio_lens).astype(np.int64)
|
||||
padded_translation = pad_sequence(
|
||||
translation_text, padding_value=IGNORE_ID).astype(np.int64)
|
||||
translation_lens = np.array(translation_text_lens).astype(np.int64)
|
||||
padded_transcription = pad_sequence(
|
||||
transcription_text, padding_value=IGNORE_ID).astype(np.int64)
|
||||
transcription_lens = np.array(transcription_text_lens).astype(np.int64)
|
||||
return utts, padded_audios, audio_lens, (
|
||||
padded_translation, padded_transcription), (translation_lens,
|
||||
transcription_lens)
|
||||
@ -0,0 +1,36 @@
|
||||
#!/bin/bash
|
||||
|
||||
if [ $# != 4 ];then
|
||||
echo "usage: ${0} config_path ckpt_path_prefix model_type audio_file"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
|
||||
echo "using $ngpu gpus..."
|
||||
|
||||
config_path=$1
|
||||
ckpt_prefix=$2
|
||||
model_type=$3
|
||||
audio_file=$4
|
||||
|
||||
# download language model
|
||||
bash local/download_lm_ch.sh
|
||||
if [ $? -ne 0 ]; then
|
||||
exit 1
|
||||
fi
|
||||
|
||||
python3 -u ${BIN_DIR}/test_hub.py \
|
||||
--nproc ${ngpu} \
|
||||
--config ${config_path} \
|
||||
--result_file ${ckpt_prefix}.rsl \
|
||||
--checkpoint_path ${ckpt_prefix} \
|
||||
--model_type ${model_type} \
|
||||
--audio_file ${audio_file}
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in evaluation!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
||||
exit 0
|
||||
@ -0,0 +1,26 @@
|
||||
coverage
|
||||
gpustat
|
||||
jsonlines
|
||||
kaldiio
|
||||
llvmlite==0.31.0
|
||||
loguru
|
||||
numba==0.47.0
|
||||
numpy==1.18.5
|
||||
Pillow
|
||||
pre-commit
|
||||
pybind11
|
||||
python-speech-features
|
||||
resampy==0.2.2
|
||||
sacrebleu
|
||||
scipy==1.2.1
|
||||
sentencepiece
|
||||
snakeviz
|
||||
SoundFile==0.9.0.post1
|
||||
sox
|
||||
soxbindings
|
||||
tensorboardX
|
||||
textgrid
|
||||
tqdm
|
||||
typeguard
|
||||
visualdl==2.2.0
|
||||
yacs
|
||||
@ -0,0 +1,66 @@
|
||||
#! /usr/bin/env bash
|
||||
cd .. >> /dev/null
|
||||
source utils/log.sh
|
||||
|
||||
|
||||
SUDO='sudo'
|
||||
if [ $(id -u) -eq 0 ]; then
|
||||
SUDO=''
|
||||
fi
|
||||
|
||||
if [ -e /etc/lsb-release ];then
|
||||
${SUDO} apt-get update -y
|
||||
${SUDO} apt-get install -y jq vim tig tree sox pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev
|
||||
if [ $? != 0 ]; then
|
||||
error_msg "Please using Ubuntu or install pkg-config libflac-dev libogg-dev libvorbis-dev libboost-dev swig python3-dev by user."
|
||||
exit -1
|
||||
fi
|
||||
fi
|
||||
|
||||
|
||||
source tools/venv/bin/activate
|
||||
|
||||
cd -
|
||||
#install python dependencies
|
||||
if [ -f "requirements.txt" ]; then
|
||||
pip3 install -r requirements.txt
|
||||
fi
|
||||
if [ $? != 0 ]; then
|
||||
error_msg "Install python dependencies failed !!!"
|
||||
exit 1
|
||||
fi
|
||||
cd .. >> /dev/null
|
||||
|
||||
# install package libsndfile
|
||||
python3 -c "import soundfile"
|
||||
if [ $? != 0 ]; then
|
||||
info_msg "Install package libsndfile into default system path."
|
||||
wget "http://www.mega-nerd.com/libsndfile/files/libsndfile-1.0.28.tar.gz"
|
||||
if [ $? != 0 ]; then
|
||||
error_msg "Download libsndfile-1.0.28.tar.gz failed !!!"
|
||||
exit 1
|
||||
fi
|
||||
tar -zxvf libsndfile-1.0.28.tar.gz
|
||||
cd libsndfile-1.0.28
|
||||
./configure > /dev/null && make > /dev/null && make install > /dev/null
|
||||
cd ..
|
||||
rm -rf libsndfile-1.0.28
|
||||
rm libsndfile-1.0.28.tar.gz
|
||||
fi
|
||||
|
||||
|
||||
# install decoders
|
||||
python3 -c "import pkg_resources; pkg_resources.require(\"swig_decoders==1.1\")"
|
||||
if [ $? != 0 ]; then
|
||||
cd deepspeech/decoders/swig > /dev/null
|
||||
sh setup.sh
|
||||
cd - > /dev/null
|
||||
fi
|
||||
python3 -c "import pkg_resources; pkg_resources.require(\"swig_decoders==1.1\")"
|
||||
if [ $? != 0 ]; then
|
||||
error_msg "Please check why decoder install error!"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
|
||||
info_msg "Install all dependencies successfully."
|
||||
Loading…
Reference in new issue