# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Evaluation for DeepSpeech2 model.""" import os import sys from pathlib import Path import paddle import soundfile from paddlespeech.s2t.exps.deepspeech2.config import get_cfg_defaults from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer from paddlespeech.s2t.io.collator import SpeechCollator from paddlespeech.s2t.models.ds2 import DeepSpeech2Model from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline from paddlespeech.s2t.training.cli import default_argument_parser from paddlespeech.s2t.utils import mp_tools from paddlespeech.s2t.utils.checkpoint import Checkpoint from paddlespeech.s2t.utils.log import Log from paddlespeech.s2t.utils.utility import print_arguments from paddlespeech.s2t.utils.utility import UpdateConfig logger = Log(__name__).getlog() class DeepSpeech2Tester_hub(): def __init__(self, config, args): self.args = args self.config = config self.audio_file = args.audio_file self.collate_fn_test = SpeechCollator.from_config(config) self._text_featurizer = TextFeaturizer( unit_type=config.collator.unit_type, vocab_filepath=None) def compute_result_transcripts(self, audio, audio_len, vocab_list, cfg): result_transcripts = self.model.decode( audio, audio_len, vocab_list, decoding_method=cfg.decoding_method, lang_model_path=cfg.lang_model_path, beam_alpha=cfg.alpha, beam_beta=cfg.beta, beam_size=cfg.beam_size, cutoff_prob=cfg.cutoff_prob, cutoff_top_n=cfg.cutoff_top_n, num_processes=cfg.num_proc_bsearch) return result_transcripts @mp_tools.rank_zero_only @paddle.no_grad() def test(self): self.model.eval() cfg = self.config audio_file = self.audio_file collate_fn_test = self.collate_fn_test audio, _ = collate_fn_test.process_utterance( audio_file=audio_file, transcript=" ") audio_len = audio.shape[0] audio = paddle.to_tensor(audio, dtype='float32') audio_len = paddle.to_tensor(audio_len) audio = paddle.unsqueeze(audio, axis=0) vocab_list = collate_fn_test.vocab_list result_transcripts = self.compute_result_transcripts( audio, audio_len, vocab_list, cfg.decoding) logger.info("result_transcripts: " + result_transcripts[0]) def run_test(self): self.resume() try: self.test() except KeyboardInterrupt: exit(-1) def setup(self): """Setup the experiment. """ paddle.set_device('gpu' if self.args.ngpu > 0 else 'cpu') self.setup_output_dir() self.setup_checkpointer() self.setup_model() def setup_output_dir(self): """Create a directory used for output. """ # output dir if self.args.output: output_dir = Path(self.args.output).expanduser() output_dir.mkdir(parents=True, exist_ok=True) else: output_dir = Path( self.args.checkpoint_path).expanduser().parent.parent output_dir.mkdir(parents=True, exist_ok=True) self.output_dir = output_dir def setup_model(self): config = self.config.clone() with UpdateConfig(config): config.model.feat_size = self.collate_fn_test.feature_size config.model.dict_size = self.collate_fn_test.vocab_size if self.args.model_type == 'offline': model = DeepSpeech2Model.from_config(config.model) elif self.args.model_type == 'online': model = DeepSpeech2ModelOnline.from_config(config.model) else: raise Exception("wrong model type") self.model = model def setup_checkpointer(self): """Create a directory used to save checkpoints into. It is "checkpoints" inside the output directory. """ # checkpoint dir checkpoint_dir = self.output_dir / "checkpoints" checkpoint_dir.mkdir(exist_ok=True) self.checkpoint_dir = checkpoint_dir self.checkpoint = Checkpoint( kbest_n=self.config.training.checkpoint.kbest_n, latest_n=self.config.training.checkpoint.latest_n) def resume(self): """Resume from the checkpoint at checkpoints in the output directory or load a specified checkpoint. """ params_path = self.args.checkpoint_path + ".pdparams" model_dict = paddle.load(params_path) self.model.set_state_dict(model_dict) def check(audio_file): logger.info("checking the audio file format......") try: sig, sample_rate = soundfile.read(audio_file) except Exception as e: logger.error(str(e)) logger.error( "can not open the wav file, please check the audio file format") sys.exit(-1) logger.info("The sample rate is %d" % sample_rate) assert (sample_rate == 16000) logger.info("The audio file format is right") def main_sp(config, args): exp = DeepSpeech2Tester_hub(config, args) exp.setup() exp.run_test() def main(config, args): main_sp(config, args) if __name__ == "__main__": parser = default_argument_parser() parser.add_argument( "--model_type", type=str, default='offline', help='offline/online') parser.add_argument("--audio_file", type=str, help='audio file path') # save asr result to parser.add_argument( "--result_file", type=str, help="path of save the asr result") args = parser.parse_args() print_arguments(args, globals()) if not os.path.isfile(args.audio_file): print("Please input the audio file path") sys.exit(-1) check(args.audio_file) print("model_type:{}".format(args.model_type)) # https://yaml.org/type/float.html config = get_cfg_defaults(args.model_type) if args.config: config.merge_from_file(args.config) if args.opts: config.merge_from_list(args.opts) config.freeze() print(config) if args.dump_config: with open(args.dump_config, 'w') as f: print(config, file=f) main(config, args)