# VoiceprintRecognition-PaddlePaddle **Repository Path**: advancer-debug/VoiceprintRecognition-PaddlePaddle ## Basic Information - **Project Name**: VoiceprintRecognition-PaddlePaddle - **Description**: 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。 - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: develop - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 3 - **Created**: 2023-09-27 - **Last Updated**: 2023-09-27 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # 基于PaddlePaddle实现的声纹识别系统 ![python version](https://img.shields.io/badge/python-3.8+-orange.svg) ![GitHub forks](https://img.shields.io/github/forks/yeyupiaoling/VoiceprintRecognition-PaddlePaddle) ![GitHub Repo stars](https://img.shields.io/github/stars/yeyupiaoling/VoiceprintRecognition-PaddlePaddle) ![GitHub](https://img.shields.io/github/license/yeyupiaoling/VoiceprintRecognition-PaddlePaddle) ![支持系统](https://img.shields.io/badge/支持系统-Win/Linux/MAC-9cf) 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](https://github.com/yeyupiaoling/VoiceprintRecognition-PaddlePaddle/tree/release/0.x)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。**

使用环境： - Anaconda 3 - Python 3.8 - PaddlePaddle 2.4.1 - 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、AMLoss、ARMLoss、CELoss 4. 支持预处理方法：MelSpectrogram、Spectrogram、MFCC、Fbank **模型论文：** - EcapaTdnn：[ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://arxiv.org/abs/2005.07143v3) - TDNN：[Prediction of speech intelligibility with DNN-based performance measures](https://arxiv.org/abs/2203.09148) - Res2Net：[Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169) - ResNetSE：[Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507) - CAMPPlus：[CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://arxiv.org/abs/2303.00332v3) - ERes2Net：[An Enhanced Res2Net with Local and Global Feature Fusion for Speaker Verification](https://arxiv.org/abs/2305.12838v1) # 模型下载 | 模型 | Params(M) | 预处理方法 | 数据集 | train speakers | threshold | EER | MinDCF | 模型下载 | |:---------:|:---------:|:-----:|:----------------------------------:|:--------------:|:---------:|:-------:|:-------:|:-------------------------------------------------------------------------------:| | CAM++ | 7.5 | Fbank | [CN-Celeb](http://openslr.org/82/) | 2796 | 0.25 | 0.09485 | 0.56214 | 加入知识星球获取/[CSDN下载](https://download.csdn.net/download/qq_33200967/88265940)(不建议) | | ERes2Net | 8.2 | Fbank | [CN-Celeb](http://openslr.org/82/) | 2796 | 0.22 | 0.09637 | 0.52627 | 加入知识星球获取 | | ResNetSE | 10.7 | Fbank | [CN-Celeb](http://openslr.org/82/) | 2796 | 0.19 | 0.10222 | 0.57981 | 加入知识星球获取 | | EcapaTdnn | 6.7 | Fbank | [CN-Celeb](http://openslr.org/82/) | 2796 | 0.25 | 0.10465 | 0.58521 | 加入知识星球获取 | | TDNN | 3.2 | Fbank | [CN-Celeb](http://openslr.org/82/) | 2796 | 0.23 | 0.11804 | 0.61070 | 加入知识星球获取 | | Res2Net | 7.2 | Fbank | [CN-Celeb](http://openslr.org/82/) | 2796 | 0.18 | 0.14126 | 0.68511 | 加入知识星球获取 | | CAM++ | 7.5 | Fbank | 更大数据集 | 2W+ | 0.34 | 0.07884 | 0.52738 | 加入知识星球获取 | | ERes2Net | 8.2 | Fbank | 其他数据集 | 20W | 0.36 | 0.02939 | 0.18355 | 加入知识星球获取 | | CAM++ | 7.5 | Flank | 其他数据集 | 20W | 0.29 | 0.04768 | 0.31429 | 加入知识星球获取 | 说明： 1. 评估的测试集为[CN-Celeb的测试集](https://aistudio.baidu.com/aistudio/datasetdetail/233361)，包含196个说话人。 ## 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。 ```shell 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： ```shell 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。 ```shell git clone https://github.com/yeyupiaoling/VoiceprintRecognition-PaddlePaddle.git cd VoiceprintRecognition-PaddlePaddle/ pip install . ``` # 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。 ```yaml # 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` ```shell # 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 ``` VisualDL页面： ![VisualDL页面](./docs/images/log.jpg) # 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。 ```shell 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 ``` # 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。 ```shell 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 ``` # 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。 ```shell 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 ``` # 其他版本 - Tensorflow：[VoiceprintRecognition-Tensorflow](https://github.com/yeyupiaoling/VoiceprintRecognition-Tensorflow) - Pytorch：[VoiceprintRecognition-Pytorch](https://github.com/yeyupiaoling/VoiceprintRecognition-Pytorch) - Keras：[VoiceprintRecognition-Keras](https://github.com/yeyupiaoling/VoiceprintRecognition-Keras) ## 打赏作者

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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