Jackwaterveg
3 years ago
committed by
GitHub
commit
31926dfee6
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#!/bin/bash
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if [ $# != 3 ];then
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echo "usage: ${0} config_path ckpt_path_prefix audio_file"
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exit -1
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fi
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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echo "using $ngpu gpus..."
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config_path=$1
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ckpt_prefix=$2
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audio_file=$3
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chunk_mode=false
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if [[ ${config_path} =~ ^.*chunk_.*yaml$ ]];then
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chunk_mode=true
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fi
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# download language model
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#bash local/download_lm_ch.sh
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#if [ $? -ne 0 ]; then
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# exit 1
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#fi
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for type in attention_rescoring; do
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echo "decoding ${type}"
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batch_size=1
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output_dir=${ckpt_prefix}
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mkdir -p ${output_dir}
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python3 -u ${BIN_DIR}/test_wav.py \
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--nproc ${ngpu} \
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--config ${config_path} \
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--result_file ${output_dir}/${type}.rsl \
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--checkpoint_path ${ckpt_prefix} \
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--opts decoding.decoding_method ${type} \
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--opts decoding.batch_size ${batch_size} \
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--audio_file ${audio_file}
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if [ $? -ne 0 ]; then
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echo "Failed in evaluation!"
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exit 1
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fi
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done
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exit 0
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@ -1,187 +0,0 @@
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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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"""Evaluation for U2 model."""
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import cProfile
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import os
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import sys
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import paddle
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import soundfile
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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.models.u2 import U2Model
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from paddlespeech.s2t.training.cli import default_argument_parser
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from paddlespeech.s2t.training.trainer import Trainer
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from paddlespeech.s2t.utils import layer_tools
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from paddlespeech.s2t.utils import mp_tools
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.s2t.utils.utility import print_arguments
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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 U2Tester_Hub(Trainer):
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def __init__(self, config, args):
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# super().__init__(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.collate_fn_test = SpeechCollator.from_config(config)
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self._text_featurizer = TextFeaturizer(
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unit_type=config.collator.unit_type,
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vocab_filepath=None,
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spm_model_prefix=config.collator.spm_model_prefix)
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def setup_model(self):
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config = self.config
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model_conf = config.model
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with UpdateConfig(model_conf):
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model_conf.input_dim = self.collate_fn_test.feature_size
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model_conf.output_dim = self.collate_fn_test.vocab_size
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model = U2Model.from_config(model_conf)
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if self.parallel:
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model = paddle.DataParallel(model)
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logger.info(f"{model}")
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layer_tools.print_params(model, logger.info)
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self.model = model
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logger.info("Setup model")
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@mp_tools.rank_zero_only
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@paddle.no_grad()
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def test(self):
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self.model.eval()
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cfg = self.config.decoding
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audio_file = self.audio_file
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collate_fn_test = self.collate_fn_test
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audio, _ = collate_fn_test.process_utterance(
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audio_file=audio_file, transcript="Hello")
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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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text_feature = self.collate_fn_test.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=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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logger.info("The result_transcripts: " + result_transcripts[0][0])
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def run_test(self):
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self.resume()
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try:
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self.test()
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except KeyboardInterrupt:
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sys.exit(-1)
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def setup(self):
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"""Setup the experiment.
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"""
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paddle.set_device('gpu' if self.args.nprocs > 0 else 'cpu')
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#self.setup_output_dir()
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#self.setup_checkpointer()
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#self.setup_dataloader()
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self.setup_model()
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self.iteration = 0
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self.epoch = 0
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def resume(self):
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"""Resume from the checkpoint at checkpoints in the output
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directory or load a specified checkpoint.
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"""
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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 check(audio_file):
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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_sp(config, args):
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exp = U2Tester_Hub(config, args)
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with exp.eval():
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exp.setup()
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exp.run_test()
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def main(config, args):
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main_sp(config, args)
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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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print_arguments(args, globals())
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if not os.path.isfile(args.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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check(args.audio_file)
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# https://yaml.org/type/float.html
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config = get_cfg_defaults()
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if args.config:
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config.merge_from_file(args.config)
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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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print(config)
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if args.dump_config:
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with open(args.dump_config, 'w') as f:
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print(config, file=f)
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# Setting for profiling
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pr = cProfile.Profile()
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pr.runcall(main, config, args)
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pr.dump_stats('test.profile')
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@ -0,0 +1,148 @@
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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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"""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 paddle
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import soundfile
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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.models.u2 import U2Model
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from paddlespeech.s2t.training.cli import default_argument_parser
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from paddlespeech.s2t.transform.transformation import Transformation
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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.sr = config.collator.target_sample_rate
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self.preprocess_conf = config.collator.augmentation_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.collator.unit_type,
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vocab_filepath=config.collator.vocab_filepath,
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spm_model_prefix=config.collator.spm_model_prefix)
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paddle.set_device('gpu' if self.args.nprocs > 0 else 'cpu')
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# model
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model_conf = config.model
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with UpdateConfig(model_conf):
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model_conf.input_dim = config.collator.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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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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feat = self.preprocessing(audio, **self.preprocess_args)
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logger.info(f"feat shape: {feat.shape}")
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ilen = paddle.to_tensor(feat.shape[0])
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xs = paddle.to_tensor(feat, dtype='float32').unsqueeze(axis=0)
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cfg = self.config.decoding
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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=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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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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|
# 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 = get_cfg_defaults()
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|
if args.config:
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|
config.merge_from_file(args.config)
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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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||||||
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