98 lines
2.1 KiB
Markdown
98 lines
2.1 KiB
Markdown
# 前言
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使用环境：
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- Anaconda 3
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- Python 3.8
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- Pytorch 1.13.1
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- Windows 10 or Ubuntu 18.04
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# 项目特性
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1. 支持模型：EcapaTdnn、TDNN、Res2Net、ResNetSE
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2. 支持池化层：AttentiveStatsPool(ASP)、SelfAttentivePooling(SAP)、TemporalStatisticsPooling(TSP)、TemporalAveragePooling(TAP)
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3. 支持损失函数：AAMLoss、AMLoss、ARMLoss、CELoss
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4. 支持预处理方法：MelSpectrogram、Spectrogram、MFCC
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## 安装环境
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- 首先安装的是Pytorch的GPU版本，如果已经安装过了，请跳过。
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```shell
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conda install pytorch==11.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia
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```
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- 安装ppvector库。
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使用pip安装，命令如下：
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```shell
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python -m pip install mvector -U -i https://pypi.tuna.tsinghua.edu.cn/simple
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```
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# 使用指南
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## 1. 环境准备
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### 1.1 安装依赖
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```shell
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# 使用conda创建环境（可选）
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conda create -n voiceprint python=3.8
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conda activate voiceprint
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# 安装项目依赖
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pip install -r requirements.txt
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```
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### 1.2 准备音频数据
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- 在`audio_db/`目录存放注册语音（建议16kHz单通道wav格式）
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- 测试音频建议存放至`test_audio/`目录
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## 2. 核心功能使用
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### 2.1 训练声纹模型
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```shell
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python train.py \
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--config_path configs/ecapa_tdnn.yml \
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--augmentation_config configs/augmentation.json \
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--save_dir models/
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```
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### 2.2 声纹注册入库
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```python
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from mvector import MVector
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mvector = MVector()
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mvector.register_user(name="user1", audio_path="audio_db/user1.wav")
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```
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### 2.3 实时声纹识别
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```shell
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python infer_recognition.py \
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--model_path models/ecapa_tdnn.pth \
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--audio_path test_audio/unknown.wav
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```
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### 2.4 声纹对比验证
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```shell
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python infer_contrast.py \
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--audio1 audio_db/user1.wav \
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--audio2 test_audio/sample.wav \
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--threshold 0.7
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```
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## 3. 降噪预处理
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```python
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from Reduction_Noise import NoiseReducer
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reducer = NoiseReducer("Reduction_Noise/pytorch_model.bin")
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clean_audio = reducer.process("noisy_audio.wav")
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```
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## 4. 模型评估
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```shell
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python eval.py \
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--model_path models/ecapa_tdnn.pth \
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--test_csv eval_samples.csv \
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--batch_size 32
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```
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