Merge pull request #2482 from SmileGoat/check_ec50
[Example] add tess example, testing paddle.audio features,backend.(paddle >= 2.4)pull/2399/head
TianYuan
3 years ago
committed by
GitHub
commit
62fe3d444d
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data:
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dataset: 'paddle.audio.datasets:TESS'
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num_classes: 7
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train:
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mode: 'train'
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split: 1
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feat_type: 'logmelspectrogram'
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dev:
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mode: 'dev'
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split: 1
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feat_type: 'logmelspectrogram'
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model:
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backbone: 'paddlespeech.cls.models:cnn14'
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feature:
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n_fft: 1024
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hop_length: 320
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window: 'hann'
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win_length: 1024
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f_min: 50.0
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f_max: 14000.0
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n_mels: 64
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training:
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epochs: 5
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learning_rate: 0.0005
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num_workers: 2
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batch_size: 128
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checkpoint_dir: './checkpoint_logmelspectrogram'
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save_freq: 1
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log_freq: 1
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data:
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dataset: 'paddle.audio.datasets:TESS'
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num_classes: 7
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train:
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mode: 'train'
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split: 1
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feat_type: 'melspectrogram'
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dev:
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mode: 'dev'
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split: 1
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feat_type: 'melspectrogram'
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model:
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backbone: 'paddlespeech.cls.models:cnn14'
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feature:
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n_fft: 1024
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hop_length: 320
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window: 'hann'
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win_length: 1024
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f_min: 50.0
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f_max: 14000.0
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n_mels: 64
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training:
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epochs: 10
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learning_rate: 0.0005
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num_workers: 2
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batch_size: 128
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checkpoint_dir: './checkpoint_melspectrogram'
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save_freq: 1
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log_freq: 1
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data:
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dataset: 'paddle.audio.datasets:TESS'
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num_classes: 7
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train:
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mode: 'train'
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split: 1
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feat_type: 'mfcc'
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dev:
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mode: 'dev'
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split: 1
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feat_type: 'mfcc'
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model:
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backbone: 'paddlespeech.cls.models:cnn14'
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feature:
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n_fft: 1024
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hop_length: 320
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window: 'hann'
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win_length: 1024
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f_min: 50.0
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f_max: 14000.0
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n_mfcc: 64
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n_mels: 64
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training:
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epochs: 5
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learning_rate: 0.0005
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num_workers: 2
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batch_size: 128
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checkpoint_dir: './checkpoint_mfcc'
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save_freq: 1
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log_freq: 1
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data:
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dataset: 'paddle.audio.datasets:TESS'
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num_classes: 7
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train:
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mode: 'train'
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split: 1
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feat_type: 'spectrogram'
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dev:
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mode: 'dev'
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split: 1
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feat_type: 'spectrogram'
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model:
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backbone: 'paddlespeech.cls.models:cnn14'
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feature:
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n_fft: 126
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hop_length: 320
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window: 'hann'
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training:
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epochs: 10
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learning_rate: 0.0005
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num_workers: 2
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batch_size: 128
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checkpoint_dir: './checkpoint_spectrogram'
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save_freq: 1
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log_freq: 1
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# Copyright (c) 2022 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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import paddle
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import yaml
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from paddleaudio.utils import logger
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from paddleaudio.utils import Timer
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from paddlespeech.cls.models import SoundClassifier
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from paddlespeech.utils.dynamic_import import dynamic_import
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument("--cfg_path", type=str, required=True)
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args = parser.parse_args()
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# yapf: enable
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def _collate_features(batch):
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# (feat, label)
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# (( n_mels, length), label)
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feats = []
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labels = []
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lengths = []
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for sample in batch:
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feats.append(paddle.transpose(sample[0], perm=[1,0]))
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lengths.append(sample[0].shape[1])
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labels.append(sample[1])
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max_length = max(lengths)
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for i in range(len(feats)):
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feats[i] = paddle.nn.functional.pad(
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feats[i], [0, max_length - feats[i].shape[0], 0, 0],
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data_format='NLC')
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return paddle.stack(feats), paddle.to_tensor(
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labels), paddle.to_tensor(lengths)
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if __name__ == "__main__":
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nranks = paddle.distributed.get_world_size()
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if paddle.distributed.get_world_size() > 1:
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paddle.distributed.init_parallel_env()
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local_rank = paddle.distributed.get_rank()
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args.cfg_path = os.path.abspath(os.path.expanduser(args.cfg_path))
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with open(args.cfg_path, 'r') as f:
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config = yaml.safe_load(f)
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model_conf = config['model']
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data_conf = config['data']
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feat_conf = config['feature']
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feat_type = data_conf['train']['feat_type']
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training_conf = config['training']
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# Dataset
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# set audio backend, make sure paddleaudio >= 1.0.2 installed.
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paddle.audio.backends.set_backend('soundfile')
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ds_class = dynamic_import(data_conf['dataset'])
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train_ds = ds_class(**data_conf['train'], **feat_conf)
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dev_ds = ds_class(**data_conf['dev'], **feat_conf)
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train_sampler = paddle.io.DistributedBatchSampler(
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train_ds,
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batch_size=training_conf['batch_size'],
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shuffle=True,
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drop_last=False)
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train_loader = paddle.io.DataLoader(
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train_ds,
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batch_sampler=train_sampler,
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num_workers=training_conf['num_workers'],
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return_list=True,
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use_buffer_reader=True,
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collate_fn=_collate_features)
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# Model
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backbone_class = dynamic_import(model_conf['backbone'])
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backbone = backbone_class(pretrained=True, extract_embedding=True)
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model = SoundClassifier(backbone, num_class=data_conf['num_classes'])
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model = paddle.DataParallel(model)
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optimizer = paddle.optimizer.Adam(
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learning_rate=training_conf['learning_rate'],
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parameters=model.parameters())
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criterion = paddle.nn.loss.CrossEntropyLoss()
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steps_per_epoch = len(train_sampler)
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timer = Timer(steps_per_epoch * training_conf['epochs'])
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timer.start()
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for epoch in range(1, training_conf['epochs'] + 1):
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model.train()
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avg_loss = 0
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num_corrects = 0
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num_samples = 0
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for batch_idx, batch in enumerate(train_loader):
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feats, labels, length = batch # feats-->(N, length, n_mels)
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logits = model(feats)
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loss = criterion(logits, labels)
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loss.backward()
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optimizer.step()
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if isinstance(optimizer._learning_rate,
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paddle.optimizer.lr.LRScheduler):
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optimizer._learning_rate.step()
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optimizer.clear_grad()
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# Calculate loss
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avg_loss += loss.numpy()[0]
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# Calculate metrics
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preds = paddle.argmax(logits, axis=1)
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num_corrects += (preds == labels).numpy().sum()
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num_samples += feats.shape[0]
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timer.count()
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if (batch_idx + 1
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) % training_conf['log_freq'] == 0 and local_rank == 0:
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lr = optimizer.get_lr()
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avg_loss /= training_conf['log_freq']
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avg_acc = num_corrects / num_samples
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print_msg = feat_type + ' Epoch={}/{}, Step={}/{}'.format(
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epoch, training_conf['epochs'], batch_idx + 1,
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steps_per_epoch)
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print_msg += ' loss={:.4f}'.format(avg_loss)
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print_msg += ' acc={:.4f}'.format(avg_acc)
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print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format(
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lr, timer.timing, timer.eta)
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logger.train(print_msg)
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avg_loss = 0
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num_corrects = 0
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num_samples = 0
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if epoch % training_conf[
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'save_freq'] == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
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dev_sampler = paddle.io.BatchSampler(
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dev_ds,
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batch_size=training_conf['batch_size'],
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shuffle=False,
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drop_last=False)
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dev_loader = paddle.io.DataLoader(
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dev_ds,
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batch_sampler=dev_sampler,
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num_workers=training_conf['num_workers'],
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return_list=True,
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use_buffer_reader=True,
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collate_fn=_collate_features)
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model.eval()
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num_corrects = 0
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num_samples = 0
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with logger.processing('Evaluation on validation dataset'):
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for batch_idx, batch in enumerate(dev_loader):
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feats, labels, length = batch
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logits = model(feats)
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preds = paddle.argmax(logits, axis=1)
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num_corrects += (preds == labels).numpy().sum()
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num_samples += feats.shape[0]
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print_msg = '[Evaluation result] ' + str(feat_type)
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print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples)
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logger.eval(print_msg)
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# Save model
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save_dir = os.path.join(training_conf['checkpoint_dir'],
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'epoch_{}'.format(epoch))
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logger.info('Saving model checkpoint to {}'.format(save_dir))
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paddle.save(model.state_dict(),
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os.path.join(save_dir, 'model.pdparams'))
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paddle.save(optimizer.state_dict(),
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os.path.join(save_dir, 'model.pdopt'))
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#!/bin/bash
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ngpu=$1
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cfg_path=$2
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if [ ${ngpu} -gt 0 ]; then
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python3 -m paddle.distributed.launch --gpus $CUDA_VISIBLE_DEVICES local/train.py \
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--cfg_path ${cfg_path}
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else
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python3 local/train.py \
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--cfg_path ${cfg_path}
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fi
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#!/bin/bash
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export MAIN_ROOT=`realpath ${PWD}/../../../`
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export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
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export LC_ALL=C
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export PYTHONDONTWRITEBYTECODE=1
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# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
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export PYTHONIOENCODING=UTF-8
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export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
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MODEL=panns
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export BIN_DIR=${MAIN_ROOT}/paddlespeech/cls/exps/${MODEL}
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#!/bin/bash
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set -e
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source path.sh
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ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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stage=$1
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stop_stage=100
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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cfg_path=$2
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./local/train.sh ${ngpu} ${cfg_path} || exit -1
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exit 0
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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cfg_path=$2
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./local/infer.sh ${cfg_path} || exit -1
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exit 0
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fi
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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ckpt=$2
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output_dir=$3
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./local/export.sh ${ckpt} ${output_dir} || exit -1
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exit 0
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fi
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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infer_device=$2
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graph_dir=$3
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audio_file=$4
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./local/static_model_infer.sh ${infer_device} ${graph_dir} ${audio_file} || exit -1
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exit 0
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fi
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