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119 lines
3.9 KiB
119 lines
3.9 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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"""Evaluation for wav2vec2.0 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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import soundfile
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from yacs.config import CfgNode
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.models.wav2vec2.wav2vec2_ASR import Wav2vec2ASR
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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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from paddlespeech.s2t.utils.utility import UpdateConfig
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logger = Log(__name__).getlog()
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class Wav2vec2Infer():
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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.text_feature = TextFeaturizer(
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unit_type=config.unit_type, vocab=config.vocab_filepath)
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paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu')
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# model
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model_conf = config
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with UpdateConfig(model_conf):
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model_conf.output_dim = self.text_feature.vocab_size
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model = Wav2vec2ASR.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.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 run(self):
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check(args.audio_file)
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with paddle.no_grad():
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# read
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audio, _ = soundfile.read(
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self.audio_file, dtype="int16", always_2d=True)
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logger.info(f"audio shape: {audio.shape}")
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xs = paddle.to_tensor(audio, dtype='float32').unsqueeze(axis=0)
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decode_config = self.config.decode
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result_transcripts, result_tokenids = self.model.decode(
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xs,
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text_feature=self.text_feature,
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decoding_method=decode_config.decoding_method,
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beam_size=decode_config.beam_size)
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rsl = result_transcripts[0]
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utt = Path(self.audio_file).name
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logger.info(f"hyp: {utt} {rsl}")
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return rsl
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def check(audio_file):
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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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sig, 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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Wav2vec2Infer(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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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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