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本项目使用了EcapaTdnn、ResNetSE、ERes2Net、CAM等多种先进的声纹识别模型#xff0c;不排除以后会支持更多模型#xff0c;同时本项目也支持了MelSpectrogram、Spectrogram、MFCC、Fbank等多种数据预处理方法#xff0c;使用了ArcFace Loss#xff0c;ArcFace loss…前言
本项目使用了EcapaTdnn、ResNetSE、ERes2Net、CAM等多种先进的声纹识别模型不排除以后会支持更多模型同时本项目也支持了MelSpectrogram、Spectrogram、MFCC、Fbank等多种数据预处理方法使用了ArcFace LossArcFace lossAdditive Angular Margin Loss加性角度间隔损失函数对应项目中的AAMLoss对特征向量和权重归一化对θ加上角度间隔m角度间隔比余弦间隔在对角度的影响更加直接除此之外还支持AMLoss、ARMLoss、CELoss等多种损失函数。
源码地址VoiceprintRecognition-Pytorch
使用环境
Anaconda 3Python 3.8Pytorch 1.13.1Windows 10 or Ubuntu 18.04
项目特性
支持模型EcapaTdnn、TDNN、Res2Net、ResNetSE、ERes2Net、CAM支持池化层AttentiveStatsPool(ASP)、SelfAttentivePooling(SAP)、TemporalStatisticsPooling(TSP)、TemporalAveragePooling(TAP)、TemporalStatsPool(TSTP)支持损失函数AAMLoss、AMLoss、ARMLoss、CELoss支持预处理方法MelSpectrogram、Spectrogram、MFCC、Fbank
模型论文
EcapaTdnnECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker VerificationPANNSPANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern RecognitionTDNNPrediction of speech intelligibility with DNN-based performance measuresRes2NetRes2Net: A New Multi-scale Backbone ArchitectureResNetSESqueeze-and-Excitation NetworksCAMPPlusCAM: A Fast and Efficient Network for Speaker Verification Using Context-Aware MaskingERes2NetAn Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification
模型下载
模型Params(M)预处理方法数据集train speakersthresholdEERMinDCFCAM7.5FbankCN-Celeb27960.260.095570.53516ERes2Net8.2FbankCN-Celeb2796ResNetSE9.4FbankCN-Celeb27960.200.101490.55185EcapaTdnn6.7FbankCN-Celeb27960.240.101630.56543TDNN3.2FbankCN-Celeb27960.230.121820.62141Res2Net6.6FbankCN-Celeb27960.220.143900.67961ERes2Net8.2Fbank其他数据集20W0.360.029360.18355CAM7.5Fbank其他数据集20W0.290.047650.31436
说明
评估的测试集为CN-Celeb的测试集包含196个说话人。
安装环境
首先安装的是Pytorch的GPU版本如果已经安装过了请跳过。
conda install pytorch1.13.1 torchvision0.14.1 torchaudio0.13.1 pytorch-cuda11.6 -c pytorch -c nvidia安装ppvector库。
使用pip安装命令如下
python -m pip install mvector -U -i https://pypi.tuna.tsinghua.edu.cn/simple建议源码安装源码安装能保证使用最新代码。
git clone https://github.com/yeyupiaoling/VoiceprintRecognition-Pytorch.git
cd VoiceprintRecognition-Pytorch/
python setup.py install创建数据
本教程笔者使用的是CN-Celeb这个数据集一共有约3000个人的语音数据有65W条语音数据下载之后要解压数据集到dataset目录另外如果要评估还需要下载CN-Celeb的测试集。如果读者有其他更好的数据集可以混合在一起使用但最好是要用python的工具模块aukit处理音频降噪和去除静音。
首先是创建一个数据列表数据列表的格式为语音文件路径\t语音分类标签创建这个列表主要是方便之后的读取也是方便读取使用其他的语音数据集语音分类标签是指说话人的唯一ID不同的语音数据集可以通过编写对应的生成数据列表的函数把这些数据集都写在同一个数据列表中。
执行create_data.py程序完成数据准备。
python create_data.py执行上面的程序之后会生成以下的数据格式如果要自定义数据参考如下数据列表前面是音频的相对路径后面的是该音频对应的说话人的标签就跟分类一样。自定义数据集的注意测试数据列表的ID可以不用跟训练的ID一样也就是说测试的数据的说话人可以不用出现在训练集只要保证测试数据列表中同一个人相同的ID即可。
dataset/CN-Celeb2_flac/data/id11999/recitation-03-019.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-10-023.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-06-025.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-04-014.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-06-030.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-10-032.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-06-028.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-10-031.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-05-003.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-04-017.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-10-016.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-09-001.flac 2795
dataset/CN-Celeb2_flac/data/id11999/recitation-05-010.flac 2795修改预处理方法
配置文件中默认使用的是Fbank预处理方法如果要使用其他预处理方法可以修改配置文件中的安装下面方式修改具体的值可以根据自己情况修改。如果不清楚如何设置参数可以直接删除该部分直接使用默认值。
# 数据预处理参数
preprocess_conf:# 音频预处理方法支持MelSpectrogram、Spectrogram、MFCC、Fbankfeature_method: Fbank# 设置API参数更参数查看对应API不清楚的可以直接删除该部分直接使用默认值method_args:sample_frequency: 16000num_mel_bins: 80训练模型
使用train.py训练模型本项目支持多个音频预处理方式通过configs/ecapa_tdnn.yml配置文件的参数preprocess_conf.feature_method可以指定MelSpectrogram为梅尔频谱Spectrogram为语谱图MFCC梅尔频谱倒谱系数等等。通过参数augment_conf_path可以指定数据增强方式。训练过程中会使用VisualDL保存训练日志通过启动VisualDL可以随时查看训练结果启动命令visualdl --logdirlog --host 0.0.0.0
# 单卡训练
CUDA_VISIBLE_DEVICES0 python train.py
# 多卡训练
CUDA_VISIBLE_DEVICES0,1 torchrun --standalone --nnodes1 --nproc_per_node2 train.py训练输出日志
[2023-08-05 09:52:06.497988 INFO ] utils:print_arguments:13 - ----------- 额外配置参数 -----------
[2023-08-05 09:52:06.498094 INFO ] utils:print_arguments:15 - configs: configs/ecapa_tdnn.yml
[2023-08-05 09:52:06.498149 INFO ] utils:print_arguments:15 - do_eval: True
[2023-08-05 09:52:06.498191 INFO ] utils:print_arguments:15 - local_rank: 0
[2023-08-05 09:52:06.498230 INFO ] utils:print_arguments:15 - pretrained_model: None
[2023-08-05 09:52:06.498269 INFO ] utils:print_arguments:15 - resume_model: None
[2023-08-05 09:52:06.498306 INFO ] utils:print_arguments:15 - save_model_path: models/
[2023-08-05 09:52:06.498342 INFO ] utils:print_arguments:15 - use_gpu: True
[2023-08-05 09:52:06.498378 INFO ] utils:print_arguments:16 - ------------------------------------------------
[2023-08-05 09:52:06.513761 INFO ] utils:print_arguments:18 - ----------- 配置文件参数 -----------
[2023-08-05 09:52:06.513906 INFO ] utils:print_arguments:21 - dataset_conf:
[2023-08-05 09:52:06.513957 INFO ] utils:print_arguments:24 - dataLoader:
[2023-08-05 09:52:06.513995 INFO ] utils:print_arguments:26 - batch_size: 64
[2023-08-05 09:52:06.514031 INFO ] utils:print_arguments:26 - num_workers: 4
[2023-08-05 09:52:06.514066 INFO ] utils:print_arguments:28 - do_vad: False
[2023-08-05 09:52:06.514101 INFO ] utils:print_arguments:28 - enroll_list: dataset/enroll_list.txt
[2023-08-05 09:52:06.514135 INFO ] utils:print_arguments:24 - eval_conf:
[2023-08-05 09:52:06.514169 INFO ] utils:print_arguments:26 - batch_size: 1
[2023-08-05 09:52:06.514203 INFO ] utils:print_arguments:26 - max_duration: 20
[2023-08-05 09:52:06.514237 INFO ] utils:print_arguments:28 - max_duration: 3
[2023-08-05 09:52:06.514274 INFO ] utils:print_arguments:28 - min_duration: 0.5
[2023-08-05 09:52:06.514308 INFO ] utils:print_arguments:28 - noise_aug_prob: 0.2
[2023-08-05 09:52:06.514342 INFO ] utils:print_arguments:28 - noise_dir: dataset/noise
[2023-08-05 09:52:06.514374 INFO ] utils:print_arguments:28 - num_speakers: 3242
[2023-08-05 09:52:06.514408 INFO ] utils:print_arguments:28 - sample_rate: 16000
[2023-08-05 09:52:06.514441 INFO ] utils:print_arguments:28 - speed_perturb: True
[2023-08-05 09:52:06.514475 INFO ] utils:print_arguments:28 - target_dB: -20
[2023-08-05 09:52:06.514508 INFO ] utils:print_arguments:28 - train_list: dataset/train_list.txt
[2023-08-05 09:52:06.514542 INFO ] utils:print_arguments:28 - trials_list: dataset/trials_list.txt
[2023-08-05 09:52:06.514575 INFO ] utils:print_arguments:28 - use_dB_normalization: True
[2023-08-05 09:52:06.514609 INFO ] utils:print_arguments:21 - loss_conf:
[2023-08-05 09:52:06.514643 INFO ] utils:print_arguments:24 - args:
[2023-08-05 09:52:06.514678 INFO ] utils:print_arguments:26 - easy_margin: False
[2023-08-05 09:52:06.514713 INFO ] utils:print_arguments:26 - margin: 0.2
[2023-08-05 09:52:06.514746 INFO ] utils:print_arguments:26 - scale: 32
[2023-08-05 09:52:06.514779 INFO ] utils:print_arguments:24 - margin_scheduler_args:
[2023-08-05 09:52:06.514814 INFO ] utils:print_arguments:26 - final_margin: 0.3
[2023-08-05 09:52:06.514848 INFO ] utils:print_arguments:28 - use_loss: AAMLoss
[2023-08-05 09:52:06.514882 INFO ] utils:print_arguments:28 - use_margin_scheduler: True
[2023-08-05 09:52:06.514915 INFO ] utils:print_arguments:21 - model_conf:
[2023-08-05 09:52:06.514950 INFO ] utils:print_arguments:24 - backbone:
[2023-08-05 09:52:06.514984 INFO ] utils:print_arguments:26 - embd_dim: 192
[2023-08-05 09:52:06.515017 INFO ] utils:print_arguments:26 - pooling_type: ASP
[2023-08-05 09:52:06.515050 INFO ] utils:print_arguments:24 - classifier:
[2023-08-05 09:52:06.515084 INFO ] utils:print_arguments:26 - num_blocks: 0
[2023-08-05 09:52:06.515118 INFO ] utils:print_arguments:21 - optimizer_conf:
[2023-08-05 09:52:06.515154 INFO ] utils:print_arguments:28 - learning_rate: 0.001
[2023-08-05 09:52:06.515188 INFO ] utils:print_arguments:28 - optimizer: Adam
[2023-08-05 09:52:06.515221 INFO ] utils:print_arguments:28 - scheduler: CosineAnnealingLR
[2023-08-05 09:52:06.515254 INFO ] utils:print_arguments:28 - scheduler_args: None
[2023-08-05 09:52:06.515289 INFO ] utils:print_arguments:28 - weight_decay: 1e-06
[2023-08-05 09:52:06.515323 INFO ] utils:print_arguments:21 - preprocess_conf:
[2023-08-05 09:52:06.515357 INFO ] utils:print_arguments:28 - feature_method: MelSpectrogram
[2023-08-05 09:52:06.515390 INFO ] utils:print_arguments:24 - method_args:
[2023-08-05 09:52:06.515426 INFO ] utils:print_arguments:26 - f_max: 14000.0
[2023-08-05 09:52:06.515460 INFO ] utils:print_arguments:26 - f_min: 50.0
[2023-08-05 09:52:06.515493 INFO ] utils:print_arguments:26 - hop_length: 320
[2023-08-05 09:52:06.515527 INFO ] utils:print_arguments:26 - n_fft: 1024
[2023-08-05 09:52:06.515560 INFO ] utils:print_arguments:26 - n_mels: 64
[2023-08-05 09:52:06.515593 INFO ] utils:print_arguments:26 - sample_rate: 16000
[2023-08-05 09:52:06.515626 INFO ] utils:print_arguments:26 - win_length: 1024
[2023-08-05 09:52:06.515660 INFO ] utils:print_arguments:21 - train_conf:
[2023-08-05 09:52:06.515694 INFO ] utils:print_arguments:28 - log_interval: 100
[2023-08-05 09:52:06.515728 INFO ] utils:print_arguments:28 - max_epoch: 30
[2023-08-05 09:52:06.515761 INFO ] utils:print_arguments:30 - use_model: EcapaTdnn
[2023-08-05 09:52:06.515794 INFO ] utils:print_arguments:31 - ------------------------------------------------
······Layer (type:depth-idx) Output Shape Param #Sequential [1, 9726] --
├─EcapaTdnn: 1-1 [1, 192] --
│ └─Conv1dReluBn: 2-1 [1, 512, 98] --
│ │ └─Conv1d: 3-1 [1, 512, 98] 163,840
│ │ └─BatchNorm1d: 3-2 [1, 512, 98] 1,024
│ └─Sequential: 2-2 [1, 512, 98] --
│ │ └─Conv1dReluBn: 3-3 [1, 512, 98] 263,168
│ │ └─Res2Conv1dReluBn: 3-4 [1, 512, 98] 86,912
│ │ └─Conv1dReluBn: 3-5 [1, 512, 98] 263,168
│ │ └─SE_Connect: 3-6 [1, 512, 98] 262,912
│ └─Sequential: 2-3 [1, 512, 98] --
│ │ └─Conv1dReluBn: 3-7 [1, 512, 98] 263,168
│ │ └─Res2Conv1dReluBn: 3-8 [1, 512, 98] 86,912
│ │ └─Conv1dReluBn: 3-9 [1, 512, 98] 263,168
│ │ └─SE_Connect: 3-10 [1, 512, 98] 262,912
│ └─Sequential: 2-4 [1, 512, 98] --
│ │ └─Conv1dReluBn: 3-11 [1, 512, 98] 263,168
│ │ └─Res2Conv1dReluBn: 3-12 [1, 512, 98] 86,912
│ │ └─Conv1dReluBn: 3-13 [1, 512, 98] 263,168
│ │ └─SE_Connect: 3-14 [1, 512, 98] 262,912
│ └─Conv1d: 2-5 [1, 1536, 98] 2,360,832
│ └─AttentiveStatsPool: 2-6 [1, 3072] --
│ │ └─Conv1d: 3-15 [1, 128, 98] 196,736
│ │ └─Conv1d: 3-16 [1, 1536, 98] 198,144
│ └─BatchNorm1d: 2-7 [1, 3072] 6,144
│ └─Linear: 2-8 [1, 192] 590,016
│ └─BatchNorm1d: 2-9 [1, 192] 384
├─SpeakerIdentification: 1-2 [1, 9726] 1,867,392Total params: 8,012,992
Trainable params: 8,012,992
Non-trainable params: 0
Total mult-adds (M): 468.81Input size (MB): 0.03
Forward/backward pass size (MB): 10.36
Params size (MB): 32.05
Estimated Total Size (MB): 42.44[2023-08-05 09:52:08.084231 INFO ] trainer:train:388 - 训练数据874175
[2023-08-05 09:52:09.186542 INFO ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [0/13659], loss: 11.95824, accuracy: 0.00000, learning rate: 0.00100000, speed: 58.09 data/sec, eta: 5 days, 5:24:08
[2023-08-05 09:52:22.477905 INFO ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [100/13659], loss: 10.35675, accuracy: 0.00278, learning rate: 0.00100000, speed: 481.65 data/sec, eta: 15:07:15
[2023-08-05 09:52:35.948581 INFO ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [200/13659], loss: 10.22089, accuracy: 0.00505, learning rate: 0.00100000, speed: 475.27 data/sec, eta: 15:19:12
[2023-08-05 09:52:49.249098 INFO ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [300/13659], loss: 10.00268, accuracy: 0.00706, learning rate: 0.00100000, speed: 481.45 data/sec, eta: 15:07:11
[2023-08-05 09:53:03.716015 INFO ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [400/13659], loss: 9.76052, accuracy: 0.00830, learning rate: 0.00100000, speed: 442.74 data/sec, eta: 16:26:16
[2023-08-05 09:53:18.258807 INFO ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [500/13659], loss: 9.50189, accuracy: 0.01060, learning rate: 0.00100000, speed: 440.46 data/sec, eta: 16:31:08
[2023-08-05 09:53:31.618354 INFO ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [600/13659], loss: 9.26083, accuracy: 0.01256, learning rate: 0.00100000, speed: 479.50 data/sec, eta: 15:10:12
[2023-08-05 09:53:45.439642 INFO ] trainer:__train_epoch:334 - Train epoch: [1/30], batch: [700/13659], loss: 9.03548, accuracy: 0.01449, learning rate: 0.00099999, speed: 463.63 data/sec, eta: 15:41:08VisualDL页面
评估模型
训练结束之后会保存预测模型我们用预测模型来预测测试集中的音频特征然后使用音频特征进行两两对比计算EER和MinDCF。
python eval.py输出类似如下
······
------------------------------------------------
W0425 08:27:32.057426 17654 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.6, Runtime API Version: 10.2
W0425 08:27:32.065165 17654 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[2023-03-16 20:20:47.195908 INFO ] trainer:evaluate:341 - 成功加载模型models/EcapaTdnn_Fbank/best_model/model.pth
100%|███████████████████████████| 84/84 [00:2800:00, 2.95it/s]
开始两两对比音频特征...
100%|███████████████████████████| 5332/5332 [00:0500:00, 1027.83it/s]
评估消耗时间65sthreshold0.26EER: 0.14739, MinDCF: 0.41999声纹对比
下面开始实现声纹对比创建infer_contrast.py程序编写infer()函数在编写模型的时候模型是有两个输出的第一个是模型的分类输出第二个是音频特征输出。所以在这里要输出的是音频的特征值有了音频的特征值就可以做声纹识别了。我们输入两个语音通过预测函数获取他们的特征数据使用这个特征数据可以求他们的对角余弦值得到的结果可以作为他们相识度。对于这个相识度的阈值threshold读者可以根据自己项目的准确度要求进行修改。
python infer_contrast.py --audio_path1audio/a_1.wav --audio_path2audio/b_2.wav输出类似如下
[2023-04-02 18:30:48.009149 INFO ] utils:print_arguments:13 - ----------- 额外配置参数 -----------
[2023-04-02 18:30:48.009149 INFO ] utils:print_arguments:15 - audio_path1: dataset/a_1.wav
[2023-04-02 18:30:48.009149 INFO ] utils:print_arguments:15 - audio_path2: dataset/b_2.wav
[2023-04-02 18:30:48.009149 INFO ] utils:print_arguments:15 - configs: configs/ecapa_tdnn.yml
[2023-04-02 18:30:48.009149 INFO ] utils:print_arguments:15 - model_path: models/EcapaTdnn_Fbank/best_model/
[2023-04-02 18:30:48.009149 INFO ] utils:print_arguments:15 - threshold: 0.6
[2023-04-02 18:30:48.009149 INFO ] utils:print_arguments:15 - use_gpu: True
[2023-04-02 18:30:48.009149 INFO ] utils:print_arguments:16 - ------------------------------------------------
······································································
W0425 08:29:10.006249 21121 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.6, Runtime API Version: 10.2
W0425 08:29:10.008555 21121 device_context.cc:465] device: 0, cuDNN Version: 7.6.
成功加载模型参数和优化方法参数models/EcapaTdnn_Fbank/best_model/model.pth
audio/a_1.wav 和 audio/b_2.wav 不是同一个人相似度为-0.09565544128417969声纹识别
在上面的声纹对比的基础上我们创建infer_recognition.py实现声纹识别。同样是使用上面声纹对比的infer()预测函数通过这两个同样获取语音的特征数据。 不同的是笔者增加了load_audio_db()和register()以及recognition()第一个函数是加载声纹库中的语音数据这些音频就是相当于已经注册的用户他们注册的语音数据会存放在这里如果有用户需要通过声纹登录就需要拿到用户的语音和语音库中的语音进行声纹对比如果对比成功那就相当于登录成功并且获取用户注册时的信息数据。第二个函数register()其实就是把录音保存在声纹库中同时获取该音频的特征添加到待对比的数据特征中。最后recognition()函数中这个函数就是将输入的语音和语音库中的语音一一对比。 有了上面的声纹识别的函数读者可以根据自己项目的需求完成声纹识别的方式例如笔者下面提供的是通过录音来完成声纹识别。首先必须要加载语音库中的语音语音库文件夹为audio_db然后用户回车后录音3秒钟然后程序会自动录音并使用录音到的音频进行声纹识别去匹配语音库中的语音获取用户的信息。通过这样方式读者也可以修改成通过服务请求的方式完成声纹识别例如提供一个API供APP调用用户在APP上通过声纹登录时把录音到的语音发送到后端完成声纹识别再把结果返回给APP前提是用户已经使用语音注册并成功把语音数据存放在audio_db文件夹中。
python infer_recognition.py输出类似如下
[2023-04-02 18:31:20.521040 INFO ] utils:print_arguments:13 - ----------- 额外配置参数 -----------
[2023-04-02 18:31:20.521040 INFO ] utils:print_arguments:15 - audio_db_path: audio_db/
[2023-04-02 18:31:20.521040 INFO ] utils:print_arguments:15 - configs: configs/ecapa_tdnn.yml
[2023-04-02 18:31:20.521040 INFO ] utils:print_arguments:15 - model_path: models/EcapaTdnn_Fbank/best_model/
[2023-04-02 18:31:20.521040 INFO ] utils:print_arguments:15 - record_seconds: 3
[2023-04-02 18:31:20.521040 INFO ] utils:print_arguments:15 - threshold: 0.6
[2023-04-02 18:31:20.521040 INFO ] utils:print_arguments:15 - use_gpu: True
[2023-04-02 18:31:20.521040 INFO ] utils:print_arguments:16 - ------------------------------------------------
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W0425 08:30:13.257884 23889 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.5, Driver API Version: 11.6, Runtime API Version: 10.2
W0425 08:30:13.260191 23889 device_context.cc:465] device: 0, cuDNN Version: 7.6.
成功加载模型参数和优化方法参数models/ecapa_tdnn/model.pth
Loaded 沙瑞金 audio.
Loaded 李达康 audio.
请选择功能0为注册音频到声纹库1为执行声纹识别0
按下回车键开机录音录音3秒中
开始录音......
录音已结束!
请输入该音频用户的名称夜雨飘零
请选择功能0为注册音频到声纹库1为执行声纹识别1
按下回车键开机录音录音3秒中
开始录音......
录音已结束!
识别说话的为夜雨飘零相似度为0.920434其他版本
TensorflowVoiceprintRecognition-TensorflowPaddlePaddleVoiceprintRecognition-PaddlePaddleKerasVoiceprintRecognition-Keras
参考资料
https://github.com/PaddlePaddle/PaddleSpeechhttps://github.com/yeyupiaoling/PaddlePaddle-MobileFaceNetshttps://github.com/yeyupiaoling/PPASRhttps://github.com/alibaba-damo-academy/3D-Speaker
