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131 lines
4.5 KiB
131 lines
4.5 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 U2 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 numpy as np
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import paddle
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import soundfile
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from paddlespeech.audio.transform.transformation import Transformation
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from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
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from paddlespeech.s2t.models.u2 import U2Model
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from paddlespeech.s2t.training.cli import config_from_args
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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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# TODO(hui zhang): dynamic load
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class U2Infer():
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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.preprocess_conf = config.preprocess_config
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self.preprocess_args = {"train": False}
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self.preprocessing = Transformation(self.preprocess_conf)
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self.text_feature = TextFeaturizer(
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unit_type=config.unit_type,
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vocab=config.vocab_filepath,
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spm_model_prefix=config.spm_model_prefix)
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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.input_dim = config.feat_dim
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model_conf.output_dim = self.text_feature.vocab_size
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model = U2Model.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, sample_rate = soundfile.read(
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self.audio_file, dtype="int16", always_2d=True)
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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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feat = self.preprocessing(audio, **self.preprocess_args)
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logger.info(f"feat shape: {feat.shape}")
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if self.args.debug:
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np.savetxt("feat.transform.txt", feat)
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ilen = paddle.to_tensor(feat.shape[0])
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xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(0)
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decode_config = self.config.decode
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logger.info(f"decode cfg: {decode_config}")
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reverse_weight = getattr(decode_config, 'reverse_weight', 0.0)
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result_transcripts = self.model.decode(
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xs,
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ilen,
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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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ctc_weight=decode_config.ctc_weight,
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decoding_chunk_size=decode_config.decoding_chunk_size,
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num_decoding_left_chunks=decode_config.num_decoding_left_chunks,
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simulate_streaming=decode_config.simulate_streaming,
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reverse_weight=reverse_weight)
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rsl = result_transcripts[0][0]
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utt = Path(self.audio_file).name
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logger.info(f"hyp: {utt} {result_transcripts[0][0]}")
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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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U2Infer(config, args).run()
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if __name__ == "__main__":
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parser = default_argument_parser()
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args = parser.parse_args()
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config = config_from_args(args)
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main(config, args)
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