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120 lines
4.3 KiB
120 lines
4.3 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 os
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import time
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import paddle
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from paddleaudio.backends import soundfile_load as load_audio
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from paddleaudio.compliance.librosa import melspectrogram
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from yacs.config import CfgNode
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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.seeding import seed_everything
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logger = Log(__name__).getlog()
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def extract_audio_embedding(args, config):
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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(config.seed)
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# stage 1: build the dnn backbone model network
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ecapa_tdnn = EcapaTdnn(**config.model)
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# stage4: build the speaker verification train instance with backbone model
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model = SpeakerIdetification(
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backbone=ecapa_tdnn, num_class=config.num_speakers)
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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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start_time = time.time()
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feat = melspectrogram(
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x=waveform,
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sr=config.sr,
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n_mels=config.n_mels,
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window_size=config.window_size,
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hop_length=config.hop_size)
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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(feat, mean_norm=True, std_norm=False)
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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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elapsed_time = time.time() - start_time
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audio_length = waveform.shape[0] / sr
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# stage 5: do global norm with external mean and std
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rtf = elapsed_time / audio_length
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logger.info(f"{args.device} rft={rtf}")
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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="cpu",
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help="Select which device to train model, defaults to gpu.")
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parser.add_argument("--config",
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default=None,
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type=str,
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help="configuration file")
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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("--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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# https://yaml.org/type/float.html
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config = CfgNode(new_allowed=True)
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if args.config:
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config.merge_from_file(args.config)
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config.freeze()
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print(config)
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extract_audio_embedding(args, config)
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