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124 lines
4.0 KiB
124 lines
4.0 KiB
# Copyright (c) 2022 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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# Modified from Whisper (https://github.com/openai/whisper/whisper/)
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import os.path
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import sys
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import distutils
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import numpy as np
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import paddle
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import soundfile
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from yacs.config import CfgNode
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from paddlespeech.s2t.models.whisper import log_mel_spectrogram
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from paddlespeech.s2t.models.whisper import ModelDimensions
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from paddlespeech.s2t.models.whisper import transcribe
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from paddlespeech.s2t.models.whisper import Whisper
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from paddlespeech.s2t.training.cli import default_argument_parser
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from paddlespeech.s2t.utils.log import Log
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logger = Log(__name__).getlog()
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class WhisperInfer():
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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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paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
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config.pop("ngpu")
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#load_model
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model_dict = paddle.load(self.config.model_file)
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config.pop("model_file")
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dims = ModelDimensions(**model_dict["dims"])
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self.model = Whisper(dims)
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self.model.load_dict(model_dict)
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def run(self):
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check(args.audio_file)
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with paddle.no_grad():
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temperature = config.pop("temperature")
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temperature_increment_on_fallback = config.pop(
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"temperature_increment_on_fallback")
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if temperature_increment_on_fallback is not None:
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temperature = tuple(
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np.arange(temperature, 1.0 + 1e-6,
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temperature_increment_on_fallback))
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else:
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temperature = [temperature]
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#load audio
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mel = log_mel_spectrogram(
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args.audio_file, resource_path=config.resource_path)
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result = transcribe(
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self.model, mel, temperature=temperature, **config)
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if args.result_file is not None:
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with open(args.result_file, 'w') as f:
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f.write(str(result))
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return result
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def check(audio_file: str):
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if not os.path.isfile(audio_file):
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print("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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_, sample_rate = soundfile.read(audio_file)
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except Exception as e:
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logger.error(str(e))
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logger.error(
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"can not open the wav file, please check the audio file format")
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sys.exit(-1)
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logger.info("The sample rate is %d" % sample_rate)
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assert (sample_rate == 16000)
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logger.info("The audio file format is right")
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def main(config, args):
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WhisperInfer(config, args).run()
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if __name__ == "__main__":
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parser = default_argument_parser()
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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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parser.add_argument(
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"--audio_file", type=str, help="path of the input audio file")
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parser.add_argument(
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"--debug",
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type=distutils.util.strtobool,
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default=False,
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help="for debug.")
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args = parser.parse_args()
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config = CfgNode(new_allowed=True)
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if args.config:
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config.merge_from_file(args.config)
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if args.decode_cfg:
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decode_confs = CfgNode(new_allowed=True)
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decode_confs.merge_from_file(args.decode_cfg)
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config.decode = decode_confs
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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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main(config, args)
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