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夜雨飘零 / VoiceprintRecognition-PaddlePaddle

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README
Apache-2.0

基于PaddlePaddle实现的声纹识别系统

python version GitHub forks GitHub Repo stars GitHub 支持系统

本分支为1.0版本，如果要使用之前的0.3版本请在0.x分支使用。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 Loss，ArcFace loss：Additive 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。

欢迎大家扫码入知识星球或者QQ群讨论，知识星球里面提供项目的模型文件和博主其他相关项目的模型文件，也包括其他一些资源。

知识星球 QQ群

使用环境：

  • Anaconda 3
  • Python 3.11
  • PaddlePaddle 2.5.1
  • Windows 10 or Ubuntu 18.04

项目特性

  1. 支持模型：EcapaTdnn、TDNN、Res2Net、ResNetSE、ERes2Net、CAM++
  2. 支持池化层：AttentiveStatsPool(ASP)、SelfAttentivePooling(SAP)、TemporalStatisticsPooling(TSP)、TemporalAveragePooling(TAP)、TemporalStatsPool(TSTP)
  3. 支持损失函数：AAMLoss、SphereFace2、AMLoss、ARMLoss、CELoss
  4. 支持预处理方法：MelSpectrogram、Spectrogram、MFCC、Fbank

模型论文：

模型下载

训练CN-Celeb数据，共有2796个说话人。

模型 Params(M) 预处理方法 数据集 train speakers threshold EER MinDCF 模型下载
CAM++ 6.8 Fbank CN-Celeb 2796 0.25 0.09485 0.56214 加入知识星球获取
ERes2Net 6.6 Fbank CN-Celeb 2796 0.22 0.09637 0.52627 加入知识星球获取
ResNetSE 7.8 Fbank CN-Celeb 2796 0.19 0.10222 0.57981 加入知识星球获取
EcapaTdnn 6.1 Fbank CN-Celeb 2796 0.25 0.10465 0.58521 加入知识星球获取
TDNN 2.6 Fbank CN-Celeb 2796 0.23 0.11804 0.61070 加入知识星球获取
Res2Net 5.0 Fbank CN-Celeb 2796 0.18 0.14126 0.68511 加入知识星球获取
CAM++ 6.8 Fbank 更大数据集 2W+ 0.34 0.07884 0.52738 加入知识星球获取
CAM++ 6.8 Flank 其他数据集 20W 0.29 0.04768 0.31429 加入知识星球获取
ERes2Net 55.1 Fbank 其他数据集 20W 0.36 0.02939 0.18355 加入知识星球获取

说明：

  1. 评估的测试集为CN-Celeb的测试集，包含196个说话人。
  2. 使用语速增强分类大小翻三倍speed_perturb_3_class: True。
  3. 参数数量不包含了分类器的参数数量。

训练VoxCeleb1&2数据，共有7205个说话人。

模型 Params(M) 预处理方法 数据集 train speakers threshold EER MinDCF 模型下载
CAM++ 6.8 Fbank VoxCeleb1&2 7205 0.27 0.03350 0.25726 加入知识星球获取
ERes2Net 6.6 Fbank VoxCeleb1&2 7205 0.21 0.03997 0.30614 加入知识星球获取
ResNetSE 7.8 Fbank VoxCeleb1&2 7205 0.21 0.03758 0.27625 加入知识星球获取
EcapaTdnn 6.1 Fbank VoxCeleb1&2 7205 0.27 0.02852 0.19432 加入知识星球获取
TDNN 2.6 Fbank VoxCeleb1&2 7205 0.25 0.03541 0.28130 加入知识星球获取
Res2Net 5.0 Fbank VoxCeleb1&2 7205 0.21 0.04749 0.44950 加入知识星球获取
CAM++ 6.8 Fbank 更大数据集 2W+ 0.28 0.03192 0.22032 加入知识星球获取
CAM++ 6.8 Fbank 其他数据集 20W+ 0.49 0.10331 0.71206 加入知识星球获取
ERes2Net 55.1 Fbank 其他数据集 20W+ 0.53 0.08903 0.62131 加入知识星球获取

说明：

  1. 评估的测试集为VoxCeleb1&2的测试集，包含158个说话人。
  2. 使用语速增强分类大小翻三倍speed_perturb_3_class: True。
  3. 参数数量不包含了分类器的参数数量。

安装环境

  • 首先安装的是PaddlePaddle的GPU版本，如果已经安装过了，请跳过。
conda install paddlepaddle-gpu==2.4.1 cudatoolkit=10.2 --channel https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/
  • 安装ppvector库。

使用pip安装，命令如下：

python -m pip install ppvector -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-PaddlePaddle.git
cd VoiceprintRecognition-PaddlePaddle/
pip 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：LogMelSpectrogram、MelSpectrogram、Spectrogram、MFCC、Fbank
  feature_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:
    sr: 16000
    n_mels: 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。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 --logdir=log --host 0.0.0.0

# 单卡训练
CUDA_VISIBLE_DEVICES=0 python train.py
# 多卡训练
python -m paddle.distributed.launch --gpus '0,1' 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)             Input Shape          Output Shape         Param #    
========================================================================================
          Conv1D-2              [[1, 64, 102]]        [1, 512, 98]         164,352    
          Conv1d-1              [[1, 64, 98]]         [1, 512, 98]            0       
           ReLU-1               [[1, 512, 98]]        [1, 512, 98]            0       
       BatchNorm1D-2            [[1, 512, 98]]        [1, 512, 98]          2,048     
       BatchNorm1d-1            [[1, 512, 98]]        [1, 512, 98]            0       
        TDNNBlock-1             [[1, 64, 98]]         [1, 512, 98]            0       
          Conv1D-4              [[1, 512, 98]]        [1, 512, 98]         262,656    
          Conv1d-3              [[1, 512, 98]]        [1, 512, 98]            0       
           ReLU-2               [[1, 512, 98]]        [1, 512, 98]            0       
       BatchNorm1D-4            [[1, 512, 98]]        [1, 512, 98]          2,048     
       BatchNorm1d-3            [[1, 512, 98]]        [1, 512, 98]            0       
        TDNNBlock-2             [[1, 512, 98]]        [1, 512, 98]            0       
··········································
         SEBlock-3           [[1, 512, 98], None]     [1, 512, 98]            0       
      SERes2NetBlock-3          [[1, 512, 98]]        [1, 512, 98]            0       
         Conv1D-70             [[1, 1536, 98]]       [1, 1536, 98]        2,360,832   
         Conv1d-69             [[1, 1536, 98]]       [1, 1536, 98]            0       
          ReLU-32              [[1, 1536, 98]]       [1, 1536, 98]            0       
       BatchNorm1D-58          [[1, 1536, 98]]       [1, 1536, 98]          6,144     
       BatchNorm1d-57          [[1, 1536, 98]]       [1, 1536, 98]            0       
        TDNNBlock-29           [[1, 1536, 98]]       [1, 1536, 98]            0       
         Conv1D-72             [[1, 4608, 98]]        [1, 128, 98]         589,952    
         Conv1d-71             [[1, 4608, 98]]        [1, 128, 98]            0       
          ReLU-33               [[1, 128, 98]]        [1, 128, 98]            0       
       BatchNorm1D-60           [[1, 128, 98]]        [1, 128, 98]           512      
       BatchNorm1d-59           [[1, 128, 98]]        [1, 128, 98]            0       
        TDNNBlock-30           [[1, 4608, 98]]        [1, 128, 98]            0       
           Tanh-1               [[1, 128, 98]]        [1, 128, 98]            0       
         Conv1D-74              [[1, 128, 98]]       [1, 1536, 98]         198,144    
         Conv1d-73              [[1, 128, 98]]       [1, 1536, 98]            0       
AttentiveStatisticsPooling-1   [[1, 1536, 98]]        [1, 3072, 1]            0       
       BatchNorm1D-62           [[1, 3072, 1]]        [1, 3072, 1]         12,288     
       BatchNorm1d-61           [[1, 3072, 1]]        [1, 3072, 1]            0       
         Conv1D-76              [[1, 3072, 1]]        [1, 192, 1]          590,016    
         Conv1d-75              [[1, 3072, 1]]        [1, 192, 1]             0       
        EcapaTdnn-1             [[1, 98, 64]]           [1, 192]              0       
  SpeakerIdentification-1         [[1, 192]]           [1, 9726]          1,867,392   
========================================================================================
Total params: 8,039,808
Trainable params: 8,020,480
Non-trainable params: 19,328
----------------------------------------------------------------------------------------
Input size (MB): 0.02
Forward/backward pass size (MB): 35.60
Params size (MB): 30.67
Estimated Total Size (MB): 66.30
----------------------------------------------------------------------------------------
[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: 15:41:08

启动VisualDL：visualdl --logdir=log --host 0.0.0.0，VisualDL页面如下：

VisualDL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 [00:28<00:00,  2.95it/s]
开始两两对比音频特征...
100%|███████████████████████████| 5332/5332 [00:05<00:00, 1027.83it/s]
评估消耗时间：65s，threshold：0.26，EER: 0.14739, MinDCF: 0.41999

推理接口

下面给出了几个常用的接口，更多接口请参考mvector/predict.py，也可以往下看声纹对比和声纹识别的例子。

from ppvector.predict import PPVectorPredictor

predictor = PPVectorPredictor(configs='configs/cam++.yml',
                              model_path='models/CAMPPlus_Fbank/best_model/')
# 获取音频特征
embedding = predictor.predict(audio_data='dataset/a_1.wav')
# 获取两个音频的相似度
similarity = predictor.contrast(audio_data1='dataset/a_1.wav', audio_data2='dataset/a_2.wav')

# 注册用户音频
predictor.register(user_name='夜雨飘零', audio_data='dataset/test.wav')
# 识别用户音频
name, score = predictor.recognition(audio_data='dataset/test1.wav')
# 获取所有用户
users_name = predictor.get_users()
# 删除用户音频
predictor.remove_user(user_name='夜雨飘零')

声纹对比

下面开始实现声纹对比，创建infer_contrast.py程序，首先介绍几个重要的函数，predict()函数是可以获取声纹特征，predict_batch()函数是可以获取一批的声纹特征，contrast()函数可以对比两条音频的相似度，register()函数注册一条音频到声纹库里面，recognition()函输入一条音频并且从声纹库里面对比识别，remove_user()函数移除你好。声纹库里面的注册人。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_path1=audio/a_1.wav --audio_path2=audio/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/ecapa_tdnn/model.pdparams
audio/a_1.wav 和 audio/b_2.wav 不是同一个人，相似度为：-0.09565544128417969

同时还提供了有GUI界面的声纹对比程序，执行infer_contrast_gui.py启动程序，界面如下，分别选择两个音频，点击开始判断，就可以判断它们是否是同一个人。

声纹对比界面

声纹识别

在新闻识别里面主要使用到register()函数和recognition()函数，首先使用register()函数函数来注册音频到声纹库里面，也可以直接把文件添加到audio_db文件夹里面，使用的时候通过recognition()函数来发起识别，输入一条音频，就可以从声纹库里面识别到所需要的说话人。

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 - ------------------------------------------------
······································································
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.pdparams
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

同时还提供了有GUI界面的声纹识别程序，执行infer_recognition_gui.py启动，点击注册音频到声纹库按钮，理解开始说话，录制3秒钟，然后输入注册人的名称，之后可以执行声纹识别按钮，然后立即说话，录制3秒钟后，等待识别结果。删除用户按钮可以删除用户。实时识别按钮可以实时识别，可以一直录音，一直识别。

声纹识别界面

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参考资料

  1. https://github.com/PaddlePaddle/PaddleSpeech
  2. https://github.com/yeyupiaoling/PaddlePaddle-MobileFaceNets
  3. https://github.com/yeyupiaoling/PPASR
  4. https://github.com/alibaba-damo-academy/3D-Speaker
  5. https://github.com/wenet-e2e/wespeaker
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简介

本项目使用了EcapaTdnn、ResNetSE、ERes2Net、CAM++等多种先进的声纹识别模型，同时本项目也支持了MelSpectrogram、Spectrogram、MFCC、Fbank等多种数据预处理方法。 展开 收起
声纹识别
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