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PaddleSpeech/paddlespeech/vector/exps/ecapa_tdnn/extract_emb.py

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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import time
import paddle
from yacs.config import CfgNode
from paddlespeech.audio.backends import load as load_audio
from paddlespeech.audio.compliance.librosa import melspectrogram
from paddlespeech.s2t.utils.log import Log
from paddlespeech.vector.io.batch import feature_normalize
from paddlespeech.vector.models.ecapa_tdnn import EcapaTdnn
from paddlespeech.vector.modules.sid_model import SpeakerIdetification
from paddlespeech.vector.training.seeding import seed_everything
logger = Log(__name__).getlog()
def extract_audio_embedding(args, config):
# stage 0: set the training device, cpu or gpu
paddle.set_device(args.device)
# set the random seed, it is a must for multiprocess training
seed_everything(config.seed)
# stage 1: build the dnn backbone model network
ecapa_tdnn = EcapaTdnn(**config.model)
# stage4: build the speaker verification train instance with backbone model
model = SpeakerIdetification(
backbone=ecapa_tdnn, num_class=config.num_speakers)
# stage 2: load the pre-trained model
args.load_checkpoint = os.path.abspath(
os.path.expanduser(args.load_checkpoint))
# load model checkpoint to sid model
state_dict = paddle.load(
os.path.join(args.load_checkpoint, 'model.pdparams'))
model.set_state_dict(state_dict)
logger.info(f'Checkpoint loaded from {args.load_checkpoint}')
# stage 3: we must set the model to eval mode
model.eval()
# stage 4: read the audio data and extract the embedding
# wavform is one dimension numpy array
waveform, sr = load_audio(args.audio_path)
# feat type is numpy array, whose shape is [dim, time]
# we need convert the audio feat to one-batch shape [batch, dim, time], where the batch is one
# so the final shape is [1, dim, time]
start_time = time.time()
feat = melspectrogram(
x=waveform,
sr=config.sr,
n_mels=config.n_mels,
window_size=config.window_size,
hop_length=config.hop_size)
feat = paddle.to_tensor(feat).unsqueeze(0)
# in inference period, the lengths is all one without padding
lengths = paddle.ones([1])
feat = feature_normalize(feat, mean_norm=True, std_norm=False)
# model backbone network forward the feats and get the embedding
embedding = model.backbone(
feat, lengths).squeeze().numpy() # (1, emb_size, 1) -> (emb_size)
elapsed_time = time.time() - start_time
audio_length = waveform.shape[0] / sr
# stage 5: do global norm with external mean and std
rtf = elapsed_time / audio_length
logger.info(f"{args.device} rft={rtf}")
return embedding
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("--config",
default=None,
type=str,
help="configuration file")
parser.add_argument("--load-checkpoint",
type=str,
default='',
help="Directory to load model checkpoint to contiune trainning.")
parser.add_argument("--audio-path",
default="./data/demo.wav",
type=str,
help="Single audio file path")
args = parser.parse_args()
# yapf: enable
# https://yaml.org/type/float.html
config = CfgNode(new_allowed=True)
if args.config:
config.merge_from_file(args.config)
config.freeze()
print(config)
extract_audio_embedding(args, config)