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265 lines
10 KiB
265 lines
10 KiB
# 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=False) # 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=False)
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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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