316 lines
12 KiB
316 lines
12 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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import argparse
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import os
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import sys
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from typing import List
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from typing import Optional
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from typing import Union
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import paddle
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import soundfile
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from ..executor import BaseExecutor
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from ..utils import cli_register
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from ..utils import download_and_decompress
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from ..utils import logger
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from ..utils import MODEL_HOME
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from paddlespeech.s2t.exps.u2.config import get_cfg_defaults
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.io.collator import SpeechCollator
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from paddlespeech.s2t.transform.transformation import Transformation
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.s2t.utils.utility import UpdateConfig
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__all__ = ['ASRExecutor']
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pretrained_models = {
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"wenetspeech_zh": {
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'url':
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'https://paddlespeech.bj.bcebos.com/s2t/wenetspeech/conformer.model.tar.gz',
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'md5':
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'54e7a558a6e020c2f5fb224874943f97',
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'cfg_path':
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'conf/conformer.yaml',
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'ckpt_path':
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'exp/conformer/checkpoints/wenetspeech',
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}
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}
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model_alias = {
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"ds2_offline": "paddlespeech.s2t.models.ds2:DeepSpeech2Model",
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"ds2_online": "paddlespeech.s2t.models.ds2_online:DeepSpeech2ModelOnline",
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"conformer": "paddlespeech.s2t.models.u2:U2Model",
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"transformer": "paddlespeech.s2t.models.u2:U2Model",
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"wenetspeech": "paddlespeech.s2t.models.u2:U2Model",
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}
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@cli_register(
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name='paddlespeech.asr', description='Speech to text infer command.')
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class ASRExecutor(BaseExecutor):
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def __init__(self):
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super(ASRExecutor, self).__init__()
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self.parser = argparse.ArgumentParser(
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prog='paddlespeech.asr', add_help=True)
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self.parser.add_argument(
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'--input', type=str, required=True, help='Audio file to recognize.')
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self.parser.add_argument(
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'--model',
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type=str,
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default='wenetspeech',
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help='Choose model type of asr task.')
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self.parser.add_argument(
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'--lang', type=str, default='zh', help='Choose model language.')
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self.parser.add_argument(
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'--config',
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type=str,
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default=None,
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help='Config of asr task. Use deault config when it is None.')
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self.parser.add_argument(
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'--ckpt_path',
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type=str,
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default=None,
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help='Checkpoint file of model.')
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self.parser.add_argument(
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'--device',
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type=str,
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default='cpu',
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help='Choose device to execute model inference.')
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def _get_pretrained_path(self, tag: str) -> os.PathLike:
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"""
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Download and returns pretrained resources path of current task.
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"""
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assert tag in pretrained_models, 'Can not find pretrained resources of {}.'.format(
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tag)
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res_path = os.path.join(MODEL_HOME, tag)
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decompressed_path = download_and_decompress(pretrained_models[tag],
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res_path)
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decompressed_path = os.path.abspath(decompressed_path)
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logger.info(
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'Use pretrained model stored in: {}'.format(decompressed_path))
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return decompressed_path
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def _init_from_path(self,
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model_type: str='wenetspeech',
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lang: str='zh',
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cfg_path: Optional[os.PathLike]=None,
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ckpt_path: Optional[os.PathLike]=None):
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"""
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Init model and other resources from a specific path.
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"""
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if cfg_path is None or ckpt_path is None:
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tag = model_type + '_' + lang
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res_path = self._get_pretrained_path(tag) # wenetspeech_zh
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self.cfg_path = os.path.join(res_path,
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pretrained_models[tag]['cfg_path'])
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self.ckpt_path = os.path.join(res_path,
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pretrained_models[tag]['ckpt_path'])
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logger.info(res_path)
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logger.info(self.cfg_path)
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logger.info(self.ckpt_path)
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.ckpt_path = os.path.abspath(ckpt_path)
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res_path = os.path.dirname(
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os.path.dirname(os.path.abspath(self.cfg_path)))
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# Enter the path of model root
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os.chdir(res_path)
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#Init body.
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parser_args = self.parser_args
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paddle.set_device(parser_args.device)
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self.config = get_cfg_defaults()
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self.config.merge_from_file(self.cfg_path)
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self.config.decoding.decoding_method = "attention_rescoring"
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#self.config.freeze()
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model_conf = self.config.model
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logger.info(model_conf)
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with UpdateConfig(model_conf):
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if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
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self.config.collator.vocab_filepath = os.path.join(
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res_path, self.config.collator.vocab_filepath)
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self.config.collator.vocab_filepath = os.path.join(
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res_path, self.config.collator.cmvn_path)
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self.collate_fn_test = SpeechCollator.from_config(self.config)
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model_conf.feat_size = self.collate_fn_test.feature_size
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model_conf.dict_size = self.text_feature.vocab_size
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elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
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self.config.collator.vocab_filepath = os.path.join(
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res_path, self.config.collator.vocab_filepath)
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self.text_feature = TextFeaturizer(
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unit_type=self.config.collator.unit_type,
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vocab_filepath=self.config.collator.vocab_filepath,
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spm_model_prefix=self.config.collator.spm_model_prefix)
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model_conf.input_dim = self.config.collator.feat_dim
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model_conf.output_dim = self.text_feature.vocab_size
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else:
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raise Exception("wrong type")
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model_class = dynamic_import(parser_args.model, model_alias)
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model = model_class.from_config(model_conf)
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self.model = model
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self.model.eval()
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# load model
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params_path = self.ckpt_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 preprocess(self, input: Union[str, os.PathLike]):
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"""
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Input preprocess and return paddle.Tensor stored in self.input.
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Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
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"""
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parser_args = self.parser_args
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config = self.config
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audio_file = input
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logger.info("audio_file" + audio_file)
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self.sr = config.collator.target_sample_rate
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# Get the object for feature extraction
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if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
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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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self.audio_len = paddle.to_tensor(audio_len)
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self.audio = paddle.unsqueeze(audio, axis=0)
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self.vocab_list = collate_fn_test.vocab_list
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logger.info(f"audio feat shape: {self.audio.shape}")
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elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
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logger.info("get the preprocess conf")
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preprocess_conf = os.path.join(
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os.path.dirname(os.path.abspath(self.cfg_path)),
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"preprocess.yaml")
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cmvn_path: data / mean_std.json
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logger.info(preprocess_conf)
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preprocess_args = {"train": False}
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preprocessing = Transformation(preprocess_conf)
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audio, sample_rate = soundfile.read(
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audio_file, dtype="int16", always_2d=True)
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if sample_rate != self.sr:
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logger.error(
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f"sample rate error: {sample_rate}, need {self.sr} ")
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sys.exit(-1)
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audio = audio[:, 0]
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logger.info(f"audio shape: {audio.shape}")
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# fbank
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audio = preprocessing(audio, **preprocess_args)
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self.audio_len = paddle.to_tensor(audio.shape[0])
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self.audio = paddle.to_tensor(
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audio, dtype='float32').unsqueeze(axis=0)
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logger.info(f"audio feat shape: {self.audio.shape}")
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else:
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raise Exception("wrong type")
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@paddle.no_grad()
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def infer(self):
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"""
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Model inference and result stored in self.output.
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"""
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cfg = self.config.decoding
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parser_args = self.parser_args
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audio = self.audio
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audio_len = self.audio_len
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if parser_args.model == "ds2_online" or parser_args.model == "ds2_offline":
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vocab_list = self.vocab_list
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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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self.result_transcripts = result_transcripts[0]
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elif parser_args.model == "conformer" or parser_args.model == "transformer" or parser_args.model == "wenetspeech":
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text_feature = self.text_feature
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result_transcripts = self.model.decode(
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audio,
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audio_len,
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text_feature=self.text_feature,
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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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ctc_weight=cfg.ctc_weight,
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decoding_chunk_size=cfg.decoding_chunk_size,
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num_decoding_left_chunks=cfg.num_decoding_left_chunks,
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simulate_streaming=cfg.simulate_streaming)
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self.result_transcripts = result_transcripts[0][0]
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else:
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raise Exception("invalid model name")
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pass
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def postprocess(self) -> Union[str, os.PathLike]:
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"""
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Output postprocess and return human-readable results such as texts and audio files.
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"""
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return self.result_transcripts
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def execute(self, argv: List[str]) -> bool:
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"""
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Command line entry.
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"""
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self.parser_args = self.parser.parse_args(argv)
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model = self.parser_args.model
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lang = self.parser_args.lang
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config = self.parser_args.config
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ckpt_path = self.parser_args.ckpt_path
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audio_file = os.path.abspath(self.parser_args.input)
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device = self.parser_args.device
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try:
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res = self(model, lang, config, ckpt_path, audio_file, device)
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logger.info('ASR Result: {}'.format(res))
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return True
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except Exception as e:
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print(e)
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return False
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def __call__(self, model, lang, config, ckpt_path, audio_file, device):
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"""
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Python API to call an executor.
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"""
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self._init_from_path(model, lang, config, ckpt_path)
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self.preprocess(audio_file)
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self.infer()
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res = self.postprocess() # Retrieve result of asr.
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return res
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