gongel
3 years ago
commit
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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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from .infer import CLSExecutor
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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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import argparse
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import os
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from typing import List
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from typing import Optional
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from typing import Union
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import numpy as np
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import paddle
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import yaml
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from ..executor import BaseExecutor
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from ..utils import cli_register
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from ..utils import download_and_decompress
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from ..utils import logger
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from ..utils import MODEL_HOME
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from paddleaudio import load
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from paddleaudio.features import LogMelSpectrogram
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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__all__ = ['CLSExecutor']
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pretrained_models = {
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# The tags for pretrained_models should be "{model_name}[_{dataset}][-{lang}][-...]".
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# e.g. "conformer_wenetspeech-zh-16k", "transformer_aishell-zh-16k" and "panns_cnn6-32k".
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# Command line and python api use "{model_name}[_{dataset}]" as --model, usage:
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# "paddlespeech asr --model conformer_wenetspeech --lang zh --sr 16000 --input ./input.wav"
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"panns_cnn6-32k": {
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'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn6.tar.gz',
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'md5': '4cf09194a95df024fd12f84712cf0f9c',
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'cfg_path': 'panns.yaml',
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'ckpt_path': 'cnn6.pdparams',
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'label_file': 'audioset_labels.txt',
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},
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"panns_cnn10-32k": {
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'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn10.tar.gz',
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'md5': 'cb8427b22176cc2116367d14847f5413',
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'cfg_path': 'panns.yaml',
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'ckpt_path': 'cnn10.pdparams',
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'label_file': 'audioset_labels.txt',
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},
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"panns_cnn14-32k": {
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'url': 'https://paddlespeech.bj.bcebos.com/cls/panns_cnn14.tar.gz',
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'md5': 'e3b9b5614a1595001161d0ab95edee97',
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'cfg_path': 'panns.yaml',
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'ckpt_path': 'cnn14.pdparams',
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'label_file': 'audioset_labels.txt',
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},
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}
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model_alias = {
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"panns_cnn6": "paddlespeech.cls.models.panns:CNN6",
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"panns_cnn10": "paddlespeech.cls.models.panns:CNN10",
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"panns_cnn14": "paddlespeech.cls.models.panns:CNN14",
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}
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@cli_register(
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name='paddlespeech.cls', description='Audio classification infer command.')
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class CLSExecutor(BaseExecutor):
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def __init__(self):
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super(CLSExecutor, self).__init__()
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self.parser = argparse.ArgumentParser(
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prog='paddlespeech.cls', add_help=True)
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self.parser.add_argument(
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'--input', type=str, required=True, help='Audio file to classify.')
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self.parser.add_argument(
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'--model',
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type=str,
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default='panns_cnn14',
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help='Choose model type of cls task.')
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self.parser.add_argument(
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'--config',
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type=str,
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default=None,
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help='Config of cls task. Use deault config when it is None.')
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self.parser.add_argument(
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'--ckpt_path',
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type=str,
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default=None,
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help='Checkpoint file of model.')
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self.parser.add_argument(
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'--label_file',
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type=str,
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default=None,
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help='Label file of cls task.')
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self.parser.add_argument(
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'--topk',
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type=int,
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default=1,
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help='Return topk scores of classification result.')
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self.parser.add_argument(
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'--device',
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type=str,
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default=paddle.get_device(),
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help='Choose device to execute model inference.')
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def _get_pretrained_path(self, tag: str) -> os.PathLike:
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"""
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Download and returns pretrained resources path of current task.
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"""
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assert tag in pretrained_models, 'Can not find pretrained resources of {}.'.format(
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tag)
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res_path = os.path.join(MODEL_HOME, tag)
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decompressed_path = download_and_decompress(pretrained_models[tag],
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res_path)
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decompressed_path = os.path.abspath(decompressed_path)
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logger.info(
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'Use pretrained model stored in: {}'.format(decompressed_path))
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return decompressed_path
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def _init_from_path(self,
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model_type: str='panns_cnn14',
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cfg_path: Optional[os.PathLike]=None,
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ckpt_path: Optional[os.PathLike]=None,
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label_file: Optional[os.PathLike]=None):
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"""
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Init model and other resources from a specific path.
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"""
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if hasattr(self, 'model'):
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logger.info('Model had been initialized.')
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return
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if label_file is None or ckpt_path is None:
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tag = model_type + '-' + '32k' # panns_cnn14-32k
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self.res_path = self._get_pretrained_path(tag)
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self.cfg_path = os.path.join(self.res_path,
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pretrained_models[tag]['cfg_path'])
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self.label_file = os.path.join(self.res_path,
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pretrained_models[tag]['label_file'])
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self.ckpt_path = os.path.join(self.res_path,
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pretrained_models[tag]['ckpt_path'])
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else:
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self.cfg_path = os.path.abspath(cfg_path)
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self.label_file = os.path.abspath(label_file)
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self.ckpt_path = os.path.abspath(ckpt_path)
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# config
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with open(self.cfg_path, 'r') as f:
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self._conf = yaml.safe_load(f)
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# labels
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self._label_list = []
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with open(self.label_file, 'r') as f:
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for line in f:
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self._label_list.append(line.strip())
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# model
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model_class = dynamic_import(model_type, model_alias)
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model_dict = paddle.load(self.ckpt_path)
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self.model = model_class(extract_embedding=False)
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self.model.set_state_dict(model_dict)
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self.model.eval()
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def preprocess(self, audio_file: Union[str, os.PathLike]):
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"""
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Input preprocess and return paddle.Tensor stored in self.input.
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Input content can be a text(tts), a file(asr, cls) or a streaming(not supported yet).
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"""
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feat_conf = self._conf['feature']
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logger.info(feat_conf)
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waveform, _ = load(
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file=audio_file,
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sr=feat_conf['sample_rate'],
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mono=True,
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dtype='float32')
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logger.info("Preprocessing audio_file:" + audio_file)
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# Feature extraction
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feature_extractor = LogMelSpectrogram(
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sr=feat_conf['sample_rate'],
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n_fft=feat_conf['n_fft'],
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hop_length=feat_conf['hop_length'],
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window=feat_conf['window'],
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win_length=feat_conf['window_length'],
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f_min=feat_conf['f_min'],
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f_max=feat_conf['f_max'],
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n_mels=feat_conf['n_mels'], )
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feats = feature_extractor(
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paddle.to_tensor(paddle.to_tensor(waveform).unsqueeze(0)))
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self._inputs['feats'] = paddle.transpose(feats, [0, 2, 1]).unsqueeze(
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1) # [B, N, T] -> [B, 1, T, N]
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@paddle.no_grad()
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def infer(self):
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"""
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Model inference and result stored in self.output.
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"""
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self._outputs['logits'] = self.model(self._inputs['feats'])
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def _generate_topk_label(self, result: np.ndarray, topk: int) -> str:
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assert topk <= len(
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self._label_list), 'Value of topk is larger than number of labels.'
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topk_idx = (-result).argsort()[:topk]
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ret = ''
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for idx in topk_idx:
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label, score = self._label_list[idx], result[idx]
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ret += f'{label}: {score}\n'
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return ret
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def postprocess(self, topk: int) -> Union[str, os.PathLike]:
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"""
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Output postprocess and return human-readable results such as texts and audio files.
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"""
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return self._generate_topk_label(
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result=self._outputs['logits'].squeeze(0).numpy(), topk=topk)
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def execute(self, argv: List[str]) -> bool:
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"""
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Command line entry.
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"""
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parser_args = self.parser.parse_args(argv)
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model_type = parser_args.model
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label_file = parser_args.label_file
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cfg_path = parser_args.config
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ckpt_path = parser_args.ckpt_path
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audio_file = parser_args.input
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topk = parser_args.topk
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device = parser_args.device
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try:
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res = self(model_type, cfg_path, label_file, ckpt_path, audio_file,
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topk, device)
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logger.info('CLS Result:\n{}'.format(res))
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return True
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except Exception as e:
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logger.exception(e)
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return False
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def __call__(self, model, config, ckpt_path, label_file, audio_file, topk,
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device):
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"""
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Python API to call an executor.
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"""
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audio_file = os.path.abspath(audio_file)
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# self._check(audio_file, sample_rate)
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paddle.set_device(device)
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self._init_from_path(model, config, ckpt_path, label_file)
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self.preprocess(audio_file)
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self.infer()
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res = self.postprocess(topk) # Retrieve result of cls.
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return res
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