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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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import argparse
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import io
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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 librosa
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import numpy as np
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import paddle
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import soundfile
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import yaml
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from yacs.config import CfgNode
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from ..executor import BaseExecutor
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from ..log import logger
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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 MODEL_HOME
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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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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# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
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# e.g. "conformer_wenetspeech-zh-16k" and "panns_cnn6-32k".
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# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
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# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
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"conformer_wenetspeech-zh-16k": {
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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='conformer_wenetspeech',
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choices=[tag[:tag.index('-')] for tag in pretrained_models.keys()],
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help='Choose model type of asr task.')
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self.parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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help='Choose model language. zh or en')
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self.parser.add_argument(
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"--sample_rate",
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type=int,
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default=16000,
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choices=[8000, 16000],
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help='Choose the audio sample rate of the model. 8000 or 16000')
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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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'--yes',
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'-y',
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action="store_true",
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default=False,
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help='No additional parameters required. Once set this parameter, it means accepting the request of the program by default, which includes transforming the audio sample rate'
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)
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self.parser.add_argument(
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'--device',
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type=str,
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default=paddle.get_device(),
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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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sample_rate: int=16000,
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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 hasattr(self, 'model'):
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logger.info('Model had been initialized.')
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return
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if cfg_path is None or ckpt_path is None:
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sample_rate_str = '16k' if sample_rate == 16000 else '8k'
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tag = model_type + '-' + lang + '-' + sample_rate_str
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res_path = self._get_pretrained_path(tag) # wenetspeech_zh
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self.res_path = res_path
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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(
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res_path, pretrained_models[tag]['ckpt_path'] + ".pdparams")
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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 + ".pdparams")
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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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#Init body.
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self.config = CfgNode(new_allowed=True)
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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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with UpdateConfig(self.config):
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if "ds2_online" in model_type or "ds2_offline" in model_type:
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from paddlespeech.s2t.io.collator import SpeechCollator
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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.mean_std_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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self.text_feature = TextFeaturizer(
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unit_type=self.config.collator.unit_type,
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vocab=self.config.collator.vocab_filepath,
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spm_model_prefix=self.config.collator.spm_model_prefix)
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self.config.model.input_dim = self.collate_fn_test.feature_size
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self.config.model.output_dim = self.text_feature.vocab_size
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elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
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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.augmentation_config = os.path.join(
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res_path, self.config.collator.augmentation_config)
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self.config.collator.spm_model_prefix = os.path.join(
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res_path, self.config.collator.spm_model_prefix)
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self.text_feature = TextFeaturizer(
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unit_type=self.config.collator.unit_type,
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vocab=self.config.collator.vocab_filepath,
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spm_model_prefix=self.config.collator.spm_model_prefix)
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self.config.model.input_dim = self.config.collator.feat_dim
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self.config.model.output_dim = self.text_feature.vocab_size
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else:
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raise Exception("wrong type")
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# Enter the path of model root
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model_name = model_type[:model_type.rindex(
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'_')] # model_type: {model_name}_{dataset}
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model_class = dynamic_import(model_name, model_alias)
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model_conf = self.config.model
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logger.info(model_conf)
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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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model_dict = paddle.load(self.ckpt_path)
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self.model.set_state_dict(model_dict)
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def preprocess(self, model_type: str, 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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audio_file = input
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logger.info("Preprocess audio_file:" + audio_file)
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# Get the object for feature extraction
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if "ds2_online" in model_type or "ds2_offline" in model_type:
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audio, _ = self.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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self._inputs["audio"] = audio
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self._inputs["audio_len"] = audio_len
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logger.info(f"audio feat shape: {audio.shape}")
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elif "conformer" in model_type or "transformer" in model_type or "wenetspeech" in model_type:
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logger.info("get the preprocess conf")
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preprocess_conf_file = self.config.collator.augmentation_config
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# redirect the cmvn path
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with io.open(preprocess_conf_file, encoding="utf-8") as f:
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preprocess_conf = yaml.safe_load(f)
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for idx, process in enumerate(preprocess_conf["process"]):
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if process['type'] == "cmvn_json":
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preprocess_conf["process"][idx][
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"cmvn_path"] = os.path.join(
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self.res_path,
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preprocess_conf["process"][idx]["cmvn_path"])
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break
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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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logger.info("read the audio file")
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audio, audio_sample_rate = soundfile.read(
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audio_file, dtype="int16", always_2d=True)
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if self.change_format:
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if audio.shape[1] >= 2:
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audio = audio.mean(axis=1, dtype=np.int16)
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else:
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audio = audio[:, 0]
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# pcm16 -> pcm 32
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audio = self._pcm16to32(audio)
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audio = librosa.resample(audio, audio_sample_rate,
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self.sample_rate)
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audio_sample_rate = self.sample_rate
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# pcm32 -> pcm 16
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audio = self._pcm32to16(audio)
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else:
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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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audio_len = paddle.to_tensor(audio.shape[0])
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audio = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
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self._inputs["audio"] = audio
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self._inputs["audio_len"] = audio_len
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logger.info(f"audio feat shape: {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, model_type: str):
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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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audio = self._inputs["audio"]
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audio_len = self._inputs["audio_len"]
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if "ds2_online" in model_type or "ds2_offline" in model_type:
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result_transcripts = self.model.decode(
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audio,
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audio_len,
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self.text_feature.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._outputs["result"] = result_transcripts[0]
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elif "conformer" in model_type or "transformer" in model_type:
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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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beam_size=cfg.beam_size,
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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._outputs["result"] = result_transcripts[0][0]
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else:
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raise Exception("invalid model name")
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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._outputs["result"]
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def _pcm16to32(self, audio):
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assert (audio.dtype == np.int16)
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audio = audio.astype("float32")
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bits = np.iinfo(np.int16).bits
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audio = audio / (2**(bits - 1))
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return audio
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def _pcm32to16(self, audio):
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assert (audio.dtype == np.float32)
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bits = np.iinfo(np.int16).bits
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audio = audio * (2**(bits - 1))
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audio = np.round(audio).astype("int16")
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return audio
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def _check(self, audio_file: str, sample_rate: int, force_yes: bool):
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self.sample_rate = sample_rate
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if self.sample_rate != 16000 and self.sample_rate != 8000:
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logger.error("please input --sr 8000 or --sr 16000")
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raise Exception("invalid sample rate")
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sys.exit(-1)
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if not os.path.isfile(audio_file):
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logger.error("Please input the right audio file path")
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sys.exit(-1)
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logger.info("checking the audio file format......")
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try:
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audio, audio_sample_rate = soundfile.read(
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audio_file, dtype="int16", always_2d=True)
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except Exception as e:
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logger.exception(e)
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logger.error(
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"can not open the audio file, please check the audio file format is 'wav'. \n \
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you can try to use sox to change the file format.\n \
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For example: \n \
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sample rate: 16k \n \
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sox input_audio.xx --rate 16k --bits 16 --channels 1 output_audio.wav \n \
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sample rate: 8k \n \
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sox input_audio.xx --rate 8k --bits 16 --channels 1 output_audio.wav \n \
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")
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sys.exit(-1)
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logger.info("The sample rate is %d" % audio_sample_rate)
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if audio_sample_rate != self.sample_rate:
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logger.warning("The sample rate of the input file is not {}.\n \
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The program will resample the wav file to {}.\n \
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If the result does not meet your expectations，\n \
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Please input the 16k 16 bit 1 channel wav file. \
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".format(self.sample_rate, self.sample_rate))
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if force_yes is False:
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while (True):
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logger.info(
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"Whether to change the sample rate and the channel. Y: change the sample. N: exit the prgream."
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)
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content = input("Input(Y/N):")
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if content.strip() == "Y" or content.strip(
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) == "y" or content.strip() == "yes" or content.strip(
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) == "Yes":
|
|
|
logger.info(
|
|
|
"change the sampele rate, channel to 16k and 1 channel"
|
|
|
)
|
|
|
break
|
|
|
elif content.strip() == "N" or content.strip(
|
|
|
) == "n" or content.strip() == "no" or content.strip(
|
|
|
) == "No":
|
|
|
logger.info("Exit the program")
|
|
|
exit(1)
|
|
|
else:
|
|
|
logger.warning("Not regular input, please input again")
|
|
|
|
|
|
self.change_format = True
|
|
|
else:
|
|
|
logger.info("The audio file format is right")
|
|
|
self.change_format = False
|
|
|
|
|
|
def execute(self, argv: List[str]) -> bool:
|
|
|
"""
|
|
|
Command line entry.
|
|
|
"""
|
|
|
parser_args = self.parser.parse_args(argv)
|
|
|
|
|
|
model = parser_args.model
|
|
|
lang = parser_args.lang
|
|
|
sample_rate = parser_args.sample_rate
|
|
|
config = parser_args.config
|
|
|
ckpt_path = parser_args.ckpt_path
|
|
|
audio_file = parser_args.input
|
|
|
force_yes = parser_args.yes
|
|
|
device = parser_args.device
|
|
|
|
|
|
try:
|
|
|
res = self(audio_file, model, lang, sample_rate, config, ckpt_path,
|
|
|
force_yes, device)
|
|
|
logger.info('ASR Result: {}'.format(res))
|
|
|
return True
|
|
|
except Exception as e:
|
|
|
logger.exception(e)
|
|
|
return False
|
|
|
|
|
|
def __call__(self,
|
|
|
audio_file: os.PathLike,
|
|
|
model: str='conformer_wenetspeech',
|
|
|
lang: str='zh',
|
|
|
sample_rate: int=16000,
|
|
|
config: os.PathLike=None,
|
|
|
ckpt_path: os.PathLike=None,
|
|
|
force_yes: bool=False,
|
|
|
device=paddle.get_device()):
|
|
|
"""
|
|
|
Python API to call an executor.
|
|
|
"""
|
|
|
audio_file = os.path.abspath(audio_file)
|
|
|
self._check(audio_file, sample_rate, force_yes)
|
|
|
paddle.set_device(device)
|
|
|
self._init_from_path(model, lang, sample_rate, config, ckpt_path)
|
|
|
self.preprocess(model, audio_file)
|
|
|
self.infer(model)
|
|
|
res = self.postprocess() # Retrieve result of asr.
|
|
|
|
|
|
return res
|
