xiongxinlei
3 years ago
parent
4473405f82
commit
0e87037f2c
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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 ast
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import os
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle.io import BatchSampler
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from paddle.io import DataLoader
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from tqdm import tqdm
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from paddleaudio.backends import load as load_audio
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from paddleaudio.datasets.voxceleb import VoxCeleb1
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from paddleaudio.features.core import melspectrogram
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.vector.io.batch import feature_normalize
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from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
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from paddlespeech.vector.modules.sid_model import SpeakerIdetification
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from paddlespeech.vector.training.metrics import compute_eer
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from paddlespeech.vector.training.seeding import seed_everything
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logger = Log(__name__).getlog()
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# feat configuration
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cpu_feat_conf = {
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'n_mels': 80,
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'window_size': 400, #ms
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'hop_length': 160, #ms
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}
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def extract_audio_embedding(args):
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# stage 0: set the training device, cpu or gpu
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paddle.set_device(args.device)
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# set the random seed, it is a must for multiprocess training
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seed_everything(args.seed)
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# stage 1: build the dnn backbone model network
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##"channels": [1024, 1024, 1024, 1024, 3072],
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model_conf = {
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"input_size": 80,
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"channels": [512, 512, 512, 512, 1536],
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"kernel_sizes": [5, 3, 3, 3, 1],
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"dilations": [1, 2, 3, 4, 1],
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"attention_channels": 128,
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"lin_neurons": 192,
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}
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ecapa_tdnn = EcapaTdnn(**model_conf)
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# stage4: build the speaker verification train instance with backbone model
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model = SpeakerIdetification(backbone=ecapa_tdnn, num_class=1211)
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# stage 2: load the pre-trained model
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args.load_checkpoint = os.path.abspath(
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os.path.expanduser(args.load_checkpoint))
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# load model checkpoint to sid model
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state_dict = paddle.load(
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os.path.join(args.load_checkpoint, 'model.pdparams'))
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model.set_state_dict(state_dict)
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logger.info(f'Checkpoint loaded from {args.load_checkpoint}')
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# stage 3: we must set the model to eval mode
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model.eval()
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# stage 4: read the audio data and extract the embedding
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# wavform is one dimension numpy array
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waveform, sr = load_audio(args.audio_path)
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# feat type is numpy array, whose shape is [dim, time]
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# we need convert the audio feat to one-batch shape [batch, dim, time], where the batch is one
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# so the final shape is [1, dim, time]
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feat = melspectrogram(x=waveform, **cpu_feat_conf)
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feat = paddle.to_tensor(feat).unsqueeze(0)
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# in inference period, the lengths is all one without padding
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lengths = paddle.ones([1])
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feat = feature_normalize(
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feat, mean_norm=True, std_norm=False, convert_to_numpy=True)
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# model backbone network forward the feats and get the embedding
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embedding = model.backbone(
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feat, lengths).squeeze().numpy() # (1, emb_size, 1) -> (emb_size)
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# stage 5: do global norm with external mean and std
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# todo
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# np.save("audio-embedding", embedding)
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return embedding
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if __name__ == "__main__":
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument('--device',
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choices=['cpu', 'gpu'],
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default="gpu",
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help="Select which device to train model, defaults to gpu.")
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parser.add_argument("--seed",
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default=0,
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type=int,
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help="random seed for paddle, numpy and python random package")
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parser.add_argument("--load-checkpoint",
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type=str,
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default='',
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help="Directory to load model checkpoint to contiune trainning.")
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parser.add_argument("--global-embedding-norm",
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type=str,
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default=None,
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help="Apply global normalization on speaker embeddings.")
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parser.add_argument("--audio-path",
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default="./data/demo.wav",
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type=str,
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help="Single audio file path")
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args = parser.parse_args()
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# yapf: enable
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extract_audio_embedding(args)
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@ -1,264 +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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import argparse
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import ast
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import os
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle.io import BatchSampler
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from paddle.io import DataLoader
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from tqdm import tqdm
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from paddleaudio.datasets.voxceleb import VoxCeleb1
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
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from paddlespeech.vector.modules.sid_model import SpeakerIdetification
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from paddlespeech.vector.training.metrics import compute_eer
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from paddlespeech.vector.training.seeding import seed_everything
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logger = Log(__name__).getlog()
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def pad_right_2d(x, target_length, axis=-1, mode='constant', **kwargs):
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x = np.asarray(x)
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assert len(
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x.shape) == 2, f'Only 2D arrays supported, but got shape: {x.shape}'
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w = target_length - x.shape[axis]
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assert w >= 0, f'Target length {target_length} is less than origin length {x.shape[axis]}'
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if axis == 0:
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pad_width = [[0, w], [0, 0]]
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else:
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pad_width = [[0, 0], [0, w]]
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return np.pad(x, pad_width, mode=mode, **kwargs)
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def feature_normalize(batch, mean_norm: bool=True, std_norm: bool=True):
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ids = [item['id'] for item in batch]
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lengths = np.asarray([item['feat'].shape[1] for item in batch])
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feats = list(
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map(lambda x: pad_right_2d(x, lengths.max()),
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[item['feat'] for item in batch]))
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feats = np.stack(feats)
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# Features normalization if needed
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for i in range(len(feats)):
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feat = feats[i][:, :lengths[i]] # Excluding pad values.
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mean = feat.mean(axis=-1, keepdims=True) if mean_norm else 0
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std = feat.std(axis=-1, keepdims=True) if std_norm else 1
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feats[i][:, :lengths[i]] = (feat - mean) / std
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assert feats[i][:, lengths[
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i]:].sum() == 0 # Padding valus should all be 0.
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# Converts into ratios.
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lengths = (lengths / lengths.max()).astype(np.float32)
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return {'ids': ids, 'feats': feats, 'lengths': lengths}
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# feat configuration
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cpu_feat_conf = {
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'n_mels': 80,
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'window_size': 400, #ms
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'hop_length': 160, #ms
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}
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def main(args):
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# stage0: set the training device, cpu or gpu
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paddle.set_device(args.device)
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# set the random seed, it is a must for multiprocess training
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seed_everything(args.seed)
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# stage1: build the dnn backbone model network
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##"channels": [1024, 1024, 1024, 1024, 3072],
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model_conf = {
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"input_size": 80,
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"channels": [512, 512, 512, 512, 1536],
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"kernel_sizes": [5, 3, 3, 3, 1],
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"dilations": [1, 2, 3, 4, 1],
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"attention_channels": 128,
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"lin_neurons": 192,
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}
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ecapa_tdnn = EcapaTdnn(**model_conf)
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# stage2: build the speaker verification eval instance with backbone model
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model = SpeakerIdetification(
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backbone=ecapa_tdnn, num_class=VoxCeleb1.num_speakers)
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# stage3: load the pre-trained model
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args.load_checkpoint = os.path.abspath(
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os.path.expanduser(args.load_checkpoint))
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# load model checkpoint to sid model
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state_dict = paddle.load(
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os.path.join(args.load_checkpoint, 'model.pdparams'))
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model.set_state_dict(state_dict)
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logger.info(f'Checkpoint loaded from {args.load_checkpoint}')
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# stage4: construct the enroll and test dataloader
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enrol_ds = VoxCeleb1(
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subset='enrol',
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target_dir=args.data_dir,
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feat_type='melspectrogram',
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random_chunk=False,
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**cpu_feat_conf)
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enrol_sampler = BatchSampler(
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enrol_ds, batch_size=args.batch_size,
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shuffle=True) # Shuffle to make embedding normalization more robust.
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enrol_loader = DataLoader(enrol_ds,
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batch_sampler=enrol_sampler,
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collate_fn=lambda x: feature_normalize(
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x, mean_norm=True, std_norm=False),
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num_workers=args.num_workers,
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return_list=True,)
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test_ds = VoxCeleb1(
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subset='test',
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target_dir=args.data_dir,
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feat_type='melspectrogram',
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random_chunk=False,
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**cpu_feat_conf)
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test_sampler = BatchSampler(
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test_ds, batch_size=args.batch_size, shuffle=True)
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test_loader = DataLoader(test_ds,
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batch_sampler=test_sampler,
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collate_fn=lambda x: feature_normalize(
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x, mean_norm=True, std_norm=False),
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num_workers=args.num_workers,
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return_list=True,)
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# stage6: we must set the model to eval mode
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model.eval()
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# stage7: global embedding norm to imporve the performance
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if args.global_embedding_norm:
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global_embedding_mean = None
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global_embedding_std = None
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mean_norm_flag = args.embedding_mean_norm
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std_norm_flag = args.embedding_std_norm
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batch_count = 0
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# stage8: Compute embeddings of audios in enrol and test dataset from model.
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id2embedding = {}
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# Run multi times to make embedding normalization more stable.
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for i in range(2):
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for dl in [enrol_loader, test_loader]:
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logger.info(
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f'Loop {[i+1]}: Computing embeddings on {dl.dataset.subset} dataset'
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)
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with paddle.no_grad():
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for batch_idx, batch in enumerate(tqdm(dl)):
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# stage 8-1: extrac the audio embedding
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ids, feats, lengths = batch['ids'], batch['feats'], batch[
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'lengths']
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embeddings = model.backbone(feats, lengths).squeeze(
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-1).numpy() # (N, emb_size, 1) -> (N, emb_size)
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# Global embedding normalization.
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if args.global_embedding_norm:
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batch_count += 1
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current_mean = embeddings.mean(
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axis=0) if mean_norm_flag else 0
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current_std = embeddings.std(
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axis=0) if std_norm_flag else 1
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# Update global mean and std.
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if global_embedding_mean is None and global_embedding_std is None:
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global_embedding_mean, global_embedding_std = current_mean, current_std
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else:
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weight = 1 / batch_count # Weight decay by batches.
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global_embedding_mean = (
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1 - weight
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) * global_embedding_mean + weight * current_mean
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global_embedding_std = (
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1 - weight
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) * global_embedding_std + weight * current_std
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# Apply global embedding normalization.
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embeddings = (embeddings - global_embedding_mean
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) / global_embedding_std
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# Update embedding dict.
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id2embedding.update(dict(zip(ids, embeddings)))
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# stage 9: Compute cosine scores.
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labels = []
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enrol_ids = []
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test_ids = []
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with open(VoxCeleb1.veri_test_file, 'r') as f:
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for line in f.readlines():
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label, enrol_id, test_id = line.strip().split(' ')
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labels.append(int(label))
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enrol_ids.append(enrol_id.split('.')[0].replace('/', '-'))
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test_ids.append(test_id.split('.')[0].replace('/', '-'))
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cos_sim_func = paddle.nn.CosineSimilarity(axis=1)
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enrol_embeddings, test_embeddings = map(lambda ids: paddle.to_tensor(
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np.asarray([id2embedding[id] for id in ids], dtype='float32')),
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[enrol_ids, test_ids
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]) # (N, emb_size)
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scores = cos_sim_func(enrol_embeddings, test_embeddings)
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EER, threshold = compute_eer(np.asarray(labels), scores.numpy())
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logger.info(
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f'EER of verification test: {EER*100:.4f}%, score threshold: {threshold:.5f}'
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)
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if __name__ == "__main__":
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# yapf: disable
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parser = argparse.ArgumentParser(__doc__)
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parser.add_argument('--device',
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choices=['cpu', 'gpu'],
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default="gpu",
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help="Select which device to train model, defaults to gpu.")
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parser.add_argument("--seed",
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default=0,
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type=int,
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help="random seed for paddle, numpy and python random package")
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parser.add_argument("--data-dir",
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default="./data/",
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type=str,
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help="data directory")
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parser.add_argument("--batch-size",
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type=int,
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default=16,
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help="Total examples' number in batch for extract the embedding.")
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parser.add_argument("--num-workers",
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type=int,
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default=0,
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help="Number of workers in dataloader.")
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parser.add_argument("--load-checkpoint",
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type=str,
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default='',
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help="Directory to load model checkpoint to contiune trainning.")
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parser.add_argument("--global-embedding-norm",
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type=bool,
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default=True,
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help="Apply global normalization on speaker embeddings.")
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parser.add_argument("--embedding-mean-norm",
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type=bool,
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default=True,
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help="Apply mean normalization on speaker embeddings.")
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parser.add_argument("--embedding-std-norm",
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type=bool,
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default=False,
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help="Apply std normalization on speaker embeddings.")
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args = parser.parse_args()
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# yapf: enable
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main(args)
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@ -1,326 +0,0 @@
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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 numpy as np
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import paddle
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from paddle.io import BatchSampler
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from paddle.io import DataLoader
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from paddle.io import DistributedBatchSampler
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from paddleaudio.datasets.voxceleb import VoxCeleb1
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from paddleaudio.features.core import melspectrogram
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from paddlespeech.s2t.utils.log import Log
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from paddlespeech.vector.io.augment import build_augment_pipeline
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from paddlespeech.vector.io.augment import waveform_augment
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from paddlespeech.vector.io.batch import feature_normalize
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from paddlespeech.vector.io.batch import waveform_collate_fn
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from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
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from paddlespeech.vector.modules.loss import AdditiveAngularMargin
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from paddlespeech.vector.modules.loss import LogSoftmaxWrapper
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from paddlespeech.vector.modules.lr import CyclicLRScheduler
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from paddlespeech.vector.modules.sid_model import SpeakerIdetification
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from paddlespeech.vector.training.seeding import seed_everything
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from paddlespeech.vector.utils.time import Timer
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logger = Log(__name__).getlog()
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# feat configuration
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cpu_feat_conf = {
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'n_mels': 80,
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'window_size': 400, #ms
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'hop_length': 160, #ms
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}
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def main(args):
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# stage0: set the training device, cpu or gpu
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paddle.set_device(args.device)
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# stage1: we must call the paddle.distributed.init_parallel_env() api at the begining
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paddle.distributed.init_parallel_env()
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nranks = paddle.distributed.get_world_size()
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local_rank = paddle.distributed.get_rank()
|
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# set the random seed, it is a must for multiprocess training
|
||||
seed_everything(args.seed)
|
||||
|
||||
# stage2: data prepare, such vox1 and vox2 data, and augment noise data and pipline
|
||||
# note: some cmd must do in rank==0, so wo will refactor the data prepare code
|
||||
train_dataset = VoxCeleb1('train', target_dir=args.data_dir)
|
||||
dev_dataset = VoxCeleb1('dev', target_dir=args.data_dir)
|
||||
|
||||
if args.augment:
|
||||
augment_pipeline = build_augment_pipeline(target_dir=args.data_dir)
|
||||
else:
|
||||
augment_pipeline = []
|
||||
|
||||
# stage3: build the dnn backbone model network
|
||||
#"channels": [1024, 1024, 1024, 1024, 3072],
|
||||
model_conf = {
|
||||
"input_size": 80,
|
||||
"channels": [512, 512, 512, 512, 1536],
|
||||
"kernel_sizes": [5, 3, 3, 3, 1],
|
||||
"dilations": [1, 2, 3, 4, 1],
|
||||
"attention_channels": 128,
|
||||
"lin_neurons": 192,
|
||||
}
|
||||
ecapa_tdnn = EcapaTdnn(**model_conf)
|
||||
|
||||
# stage4: build the speaker verification train instance with backbone model
|
||||
model = SpeakerIdetification(
|
||||
backbone=ecapa_tdnn, num_class=VoxCeleb1.num_speakers)
|
||||
|
||||
# stage5: build the optimizer, we now only construct the AdamW optimizer
|
||||
lr_schedule = CyclicLRScheduler(
|
||||
base_lr=args.learning_rate, max_lr=1e-3, step_size=140000 // nranks)
|
||||
optimizer = paddle.optimizer.AdamW(
|
||||
learning_rate=lr_schedule, parameters=model.parameters())
|
||||
|
||||
# stage6: build the loss function, we now only support LogSoftmaxWrapper
|
||||
criterion = LogSoftmaxWrapper(
|
||||
loss_fn=AdditiveAngularMargin(margin=0.2, scale=30))
|
||||
|
||||
# stage7: confirm training start epoch
|
||||
# if pre-trained model exists, start epoch confirmed by the pre-trained model
|
||||
start_epoch = 0
|
||||
if args.load_checkpoint:
|
||||
logger.info("load the check point")
|
||||
args.load_checkpoint = os.path.abspath(
|
||||
os.path.expanduser(args.load_checkpoint))
|
||||
try:
|
||||
# load model checkpoint
|
||||
state_dict = paddle.load(
|
||||
os.path.join(args.load_checkpoint, 'model.pdparams'))
|
||||
model.set_state_dict(state_dict)
|
||||
|
||||
# load optimizer checkpoint
|
||||
state_dict = paddle.load(
|
||||
os.path.join(args.load_checkpoint, 'model.pdopt'))
|
||||
optimizer.set_state_dict(state_dict)
|
||||
if local_rank == 0:
|
||||
logger.info(f'Checkpoint loaded from {args.load_checkpoint}')
|
||||
except FileExistsError:
|
||||
if local_rank == 0:
|
||||
logger.info('Train from scratch.')
|
||||
|
||||
try:
|
||||
start_epoch = int(args.load_checkpoint[-1])
|
||||
logger.info(f'Restore training from epoch {start_epoch}.')
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
# stage8: we build the batch sampler for paddle.DataLoader
|
||||
train_sampler = DistributedBatchSampler(
|
||||
train_dataset, batch_size=args.batch_size, shuffle=True, drop_last=False)
|
||||
train_loader = DataLoader(
|
||||
train_dataset,
|
||||
batch_sampler=train_sampler,
|
||||
num_workers=args.num_workers,
|
||||
collate_fn=waveform_collate_fn,
|
||||
return_list=True,
|
||||
use_buffer_reader=True, )
|
||||
|
||||
# stage9: start to train
|
||||
# we will comment the training process
|
||||
steps_per_epoch = len(train_sampler)
|
||||
timer = Timer(steps_per_epoch * args.epochs)
|
||||
timer.start()
|
||||
|
||||
for epoch in range(start_epoch + 1, args.epochs + 1):
|
||||
# at the begining, model must set to train mode
|
||||
model.train()
|
||||
|
||||
avg_loss = 0
|
||||
num_corrects = 0
|
||||
num_samples = 0
|
||||
for batch_idx, batch in enumerate(train_loader):
|
||||
# stage 9-1: batch data is audio sample points and speaker id label
|
||||
waveforms, labels = batch['waveforms'], batch['labels']
|
||||
|
||||
# stage 9-2: audio sample augment method, which is done on the audio sample point
|
||||
if len(augment_pipeline) != 0:
|
||||
waveforms = waveform_augment(waveforms, augment_pipeline)
|
||||
labels = paddle.concat(
|
||||
[labels for i in range(len(augment_pipeline) + 1)])
|
||||
|
||||
# stage 9-3: extract the audio feats,such fbank, mfcc, spectrogram
|
||||
feats = []
|
||||
for waveform in waveforms.numpy():
|
||||
feat = melspectrogram(x=waveform, **cpu_feat_conf)
|
||||
feats.append(feat)
|
||||
feats = paddle.to_tensor(np.asarray(feats))
|
||||
|
||||
# stage 9-4: feature normalize, which help converge and imporve the performance
|
||||
feats = feature_normalize(
|
||||
feats, mean_norm=True, std_norm=False) # Features normalization
|
||||
|
||||
# stage 9-5: model forward, such ecapa-tdnn, x-vector
|
||||
logits = model(feats)
|
||||
|
||||
# stage 9-6: loss function criterion, such AngularMargin, AdditiveAngularMargin
|
||||
loss = criterion(logits, labels)
|
||||
|
||||
# stage 9-7: update the gradient and clear the gradient cache
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
if isinstance(optimizer._learning_rate,
|
||||
paddle.optimizer.lr.LRScheduler):
|
||||
optimizer._learning_rate.step()
|
||||
optimizer.clear_grad()
|
||||
|
||||
# stage 9-8: Calculate average loss per batch
|
||||
avg_loss += loss.numpy()[0]
|
||||
|
||||
# stage 9-9: Calculate metrics, which is one-best accuracy
|
||||
preds = paddle.argmax(logits, axis=1)
|
||||
num_corrects += (preds == labels).numpy().sum()
|
||||
num_samples += feats.shape[0]
|
||||
timer.count() # step plus one in timer
|
||||
|
||||
# stage 9-10: print the log information only on 0-rank per log-freq batchs
|
||||
if (batch_idx + 1) % args.log_freq == 0 and local_rank == 0:
|
||||
lr = optimizer.get_lr()
|
||||
avg_loss /= args.log_freq
|
||||
avg_acc = num_corrects / num_samples
|
||||
|
||||
print_msg = 'Train Epoch={}/{}, Step={}/{}'.format(
|
||||
epoch, args.epochs, batch_idx + 1, steps_per_epoch)
|
||||
print_msg += ' loss={:.4f}'.format(avg_loss)
|
||||
print_msg += ' acc={:.4f}'.format(avg_acc)
|
||||
print_msg += ' lr={:.4E} step/sec={:.2f} | ETA {}'.format(
|
||||
lr, timer.timing, timer.eta)
|
||||
logger.info(print_msg)
|
||||
|
||||
avg_loss = 0
|
||||
num_corrects = 0
|
||||
num_samples = 0
|
||||
|
||||
# stage 9-11: save the model parameters only on 0-rank per save-freq batchs
|
||||
if epoch % args.save_freq == 0 and batch_idx + 1 == steps_per_epoch:
|
||||
if local_rank != 0:
|
||||
paddle.distributed.barrier(
|
||||
) # Wait for valid step in main process
|
||||
continue # Resume trainning on other process
|
||||
|
||||
# stage 9-12: construct the valid dataset dataloader
|
||||
dev_sampler = BatchSampler(
|
||||
dev_dataset,
|
||||
batch_size=args.batch_size // 4,
|
||||
shuffle=False,
|
||||
drop_last=False)
|
||||
dev_loader = DataLoader(
|
||||
dev_dataset,
|
||||
batch_sampler=dev_sampler,
|
||||
collate_fn=waveform_collate_fn,
|
||||
num_workers=args.num_workers,
|
||||
return_list=True, )
|
||||
|
||||
# set the model to eval mode
|
||||
model.eval()
|
||||
num_corrects = 0
|
||||
num_samples = 0
|
||||
|
||||
# stage 9-13: evaluation the valid dataset batch data
|
||||
logger.info('Evaluate on validation dataset')
|
||||
with paddle.no_grad():
|
||||
for batch_idx, batch in enumerate(dev_loader):
|
||||
waveforms, labels = batch['waveforms'], batch['labels']
|
||||
|
||||
feats = []
|
||||
for waveform in waveforms.numpy():
|
||||
feat = melspectrogram(x=waveform, **cpu_feat_conf)
|
||||
feats.append(feat)
|
||||
|
||||
feats = paddle.to_tensor(np.asarray(feats))
|
||||
feats = feature_normalize(
|
||||
feats, mean_norm=True, std_norm=False)
|
||||
logits = model(feats)
|
||||
|
||||
preds = paddle.argmax(logits, axis=1)
|
||||
num_corrects += (preds == labels).numpy().sum()
|
||||
num_samples += feats.shape[0]
|
||||
|
||||
print_msg = '[Evaluation result]'
|
||||
print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples)
|
||||
logger.info(print_msg)
|
||||
|
||||
# stage 9-14: Save model parameters
|
||||
save_dir = os.path.join(args.checkpoint_dir,
|
||||
'epoch_{}'.format(epoch))
|
||||
logger.info('Saving model checkpoint to {}'.format(save_dir))
|
||||
paddle.save(model.state_dict(),
|
||||
os.path.join(save_dir, 'model.pdparams'))
|
||||
paddle.save(optimizer.state_dict(),
|
||||
os.path.join(save_dir, 'model.pdopt'))
|
||||
|
||||
if nranks > 1:
|
||||
paddle.distributed.barrier() # Main process
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# yapf: disable
|
||||
parser = argparse.ArgumentParser(__doc__)
|
||||
parser.add_argument('--device',
|
||||
choices=['cpu', 'gpu'],
|
||||
default="cpu",
|
||||
help="Select which device to train model, defaults to gpu.")
|
||||
parser.add_argument("--seed",
|
||||
default=0,
|
||||
type=int,
|
||||
help="random seed for paddle, numpy and python random package")
|
||||
parser.add_argument("--data-dir",
|
||||
default="./data/",
|
||||
type=str,
|
||||
help="data directory")
|
||||
parser.add_argument("--learning-rate",
|
||||
type=float,
|
||||
default=1e-8,
|
||||
help="Learning rate used to train with warmup.")
|
||||
parser.add_argument("--load-checkpoint",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Directory to load model checkpoint to contiune trainning.")
|
||||
parser.add_argument("--batch-size",
|
||||
type=int, default=64,
|
||||
help="Total examples' number in batch for training.")
|
||||
parser.add_argument("--num-workers",
|
||||
type=int,
|
||||
default=0,
|
||||
help="Number of workers in dataloader.")
|
||||
parser.add_argument("--epochs",
|
||||
type=int,
|
||||
default=50,
|
||||
help="Number of epoches for fine-tuning.")
|
||||
parser.add_argument("--log-freq",
|
||||
type=int,
|
||||
default=10,
|
||||
help="Log the training infomation every n steps.")
|
||||
parser.add_argument("--save-freq",
|
||||
type=int,
|
||||
default=1,
|
||||
help="Save checkpoint every n epoch.")
|
||||
parser.add_argument("--checkpoint-dir",
|
||||
type=str,
|
||||
default='./checkpoint',
|
||||
help="Directory to save model checkpoints.")
|
||||
parser.add_argument("--augment",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Apply audio augments.")
|
||||
|
||||
args = parser.parse_args()
|
||||
# yapf: enable
|
||||
|
||||
main(args)
|
||||
Loading…
Reference in new issue