Hui Zhang
3 years ago
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GitHub
commit
77c38dd521
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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 DeepSpeech2 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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from deepspeech.exps.deepspeech2.config import get_cfg_defaults
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from deepspeech.frontend.featurizer.text_featurizer import TextFeaturizer
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from deepspeech.io.collator import SpeechCollator
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from deepspeech.models.ds2 import DeepSpeech2Model
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from deepspeech.models.ds2_online import DeepSpeech2ModelOnline
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from deepspeech.training.cli import default_argument_parser
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from deepspeech.utils import mp_tools
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from deepspeech.utils.checkpoint import Checkpoint
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from deepspeech.utils.log import Log
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from deepspeech.utils.utility import print_arguments
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from deepspeech.utils.utility import UpdateConfig
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logger = Log(__name__).getlog()
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class DeepSpeech2Tester_hub():
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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.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, vocab_filepath=None)
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def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg):
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result_transcripts = self.model.decode(
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audio,
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audio_len,
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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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#replace the '<space>' with ' '
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result_transcripts = [
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self._text_featurizer.detokenize(sentence)
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for sentence in result_transcripts
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]
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return result_transcripts
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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
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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=" ")
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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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result_transcripts = self.compute_result_transcripts(
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audio, audio_len, vocab_list, cfg.decoding)
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logger.info("result_transcripts: " + result_transcripts[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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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_model()
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def setup_output_dir(self):
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"""Create a directory used for output.
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"""
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# output dir
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if self.args.output:
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output_dir = Path(self.args.output).expanduser()
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output_dir.mkdir(parents=True, exist_ok=True)
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else:
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output_dir = Path(
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self.args.checkpoint_path).expanduser().parent.parent
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output_dir.mkdir(parents=True, exist_ok=True)
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self.output_dir = output_dir
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def setup_model(self):
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config = self.config.clone()
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with UpdateConfig(config):
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config.model.feat_size = self.collate_fn_test.feature_size
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config.model.dict_size = self.collate_fn_test.vocab_size
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if self.args.model_type == 'offline':
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model = DeepSpeech2Model.from_config(config.model)
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elif self.args.model_type == 'online':
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model = DeepSpeech2ModelOnline.from_config(config.model)
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else:
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raise Exception("wrong model type")
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self.model = model
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def setup_checkpointer(self):
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"""Create a directory used to save checkpoints into.
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It is "checkpoints" inside the output directory.
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"""
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# checkpoint dir
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checkpoint_dir = self.output_dir / "checkpoints"
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checkpoint_dir.mkdir(exist_ok=True)
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self.checkpoint_dir = checkpoint_dir
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self.checkpoint = Checkpoint(
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kbest_n=self.config.training.checkpoint.kbest_n,
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latest_n=self.config.training.checkpoint.latest_n)
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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 main_sp(config, args):
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exp = DeepSpeech2Tester_hub(config, args)
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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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parser.add_argument("--model_type")
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parser.add_argument("--audio_file")
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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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args = parser.parse_args()
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print_arguments(args, globals())
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if args.model_type is None:
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args.model_type = 'offline'
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if not os.path.isfile(args.audio_file):
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print("Please input the audio file path")
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sys.exit(-1)
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print("model_type:{}".format(args.model_type))
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# https://yaml.org/type/float.html
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config = get_cfg_defaults(args.model_type)
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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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main(config, args)
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@ -0,0 +1,36 @@
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#!/bin/bash
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if [ $# != 4 ];then
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echo "usage: ${0} config_path ckpt_path_prefix model_type audio_file"
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exit -1
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fi
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ngpu=$(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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model_type=$3
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audio_file=$4
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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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python3 -u ${BIN_DIR}/test_hub.py \
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--nproc ${ngpu} \
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--config ${config_path} \
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--result_file ${ckpt_prefix}.rsl \
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--checkpoint_path ${ckpt_prefix} \
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--model_type ${model_type} \
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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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exit 0
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