# 基于PaddlePaddle实现的语音情感识别系统
本项目是一个语音情感识别项目，目前效果一般，供大家学习使用。后面会持续优化，提高准确率，如果同学们有好的建议，也欢迎来探讨。
**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。**
<div align="center">
<img src="https://yeyupiaoling.cn/zsxq.png" alt="知识星球" width="400">
<img src="https://yeyupiaoling.cn/qq.png" alt="QQ群" width="400">
</div>
# 使用准备
- Anaconda 3
- Python 3.8
- PaddlePaddle 2.4.0
- Windows 10 or Ubuntu 18.04
# 模型测试表
| 模型 | Params(M) | 预处理方法 | 数据集 | 类别数量 | 准确率 | 获取模型 |
|:-----------------:|:---------:|:-----:|:-------:|:----:|:-------:|:--------:|
| BidirectionalLSTM | 1.8 | Flank | RAVDESS | 8 | 0.95193 | 加入知识星球获取 |
说明：
1. RAVDESS数据集只使用`Audio_Speech_Actors_01-24.zip`
## 安装环境
- 首先安装的是PaddlePaddle的2.6.1以上的版本，如果已经安装过了，请跳过。
```shell
conda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.7 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge
```
- 安装ppser库。
使用pip安装，命令如下：
```shell
python -m pip install ppser -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/SpeechEmotionRecognition-PaddlePaddle.git
cd SpeechEmotionRecognition-PaddlePaddle/
pip install .
```
## 准备数据
生成数据列表，用于下一步的读取需要，项目默认提供一个数据集[RAVDESS](https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip?download=1)，下载这个数据集并解压到`dataset`目录下。
生成数据列表，用于下一步的读取需要，项目默认提供一个数据集[RAVDESS](https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip?download=1)，这个数据集的[介绍页面](https://zenodo.org/record/1188976#.XsAXemgzaUk)，这个数据包含中性、平静、快乐、悲伤、愤怒、恐惧、厌恶、惊讶八种情感，本项目只使用里面的`Audio_Speech_Actors_01-24.zip`，数据集，说话的语句只有`Kids are talking by the door`和`Dogs are sitting by the door`，可以说这个训练集是非常简单的。下载这个数据集并解压到`dataset`目录下。
```shell
python create_data.py
```
如果自定义数据集，可以按照下面格式，`audio_path`为音频文件路径，用户需要提前把音频数据集存放在`dataset/audio`目录下，每个文件夹存放一个类别的音频数据，每条音频数据长度在3秒左右，如 `dataset/audio/angry/······`。`audio`是数据列表存放的位置，生成的数据类别的格式为 `音频路径\t音频对应的类别标签`，音频路径和标签用制表符 `\t`分开。读者也可以根据自己存放数据的方式修改以下函数。
执行`create_data.py`里面的`get_data_list('dataset/audios', 'dataset')`函数即可生成数据列表，同时也生成归一化文件，具体看代码。
```shell
python create_data.py
```
生成的列表是长这样的，前面是音频的路径，后面是该音频对应的标签，从0开始，路径和标签之间用`\t`隔开。
```shell
dataset/Audio_Speech_Actors_01-24/Actor_13/03-01-01-01-02-01-13.wav 0
dataset/Audio_Speech_Actors_01-24/Actor_01/03-01-02-01-01-01-01.wav 1
dataset/Audio_Speech_Actors_01-24/Actor_01/03-01-03-02-01-01-01.wav 2
```
**注意：** `create_data.py`里面的`create_standard('configs/bi_lstm.yml')`函数必须要执行的，这个是生成归一化的文件。
# 提取特征（可选）
在训练过程中，首先是要读取音频数据，然后提取特征，最后再进行训练。其中读取音频数据、提取特征也是比较消耗时间的，所以我们可以选择提前提取好取特征，训练模型的是就可以直接加载提取好的特征，这样训练速度会更快。这个提取特征是可选择，如果没有提取好的特征，训练模型的时候就会从读取音频数据，然后提取特征开始。提取特征步骤如下：
1. 执行`extract_features.py`，提取特征，特征会保存在`dataset/features`目录下，并生成新的数据列表`train_list_features.txt`和`test_list_features.txt`。
```shell
python extract_features.py --configs=configs/bi_lstm.yml --save_dir=dataset/features
```
2. 修改配置文件，将`dataset_conf.train_list`和`dataset_conf.test_list`修改为`train_list_features.txt`和`test_list_features.txt`。
## 训练
接着就可以开始训练模型了，创建 `train.py`。配置文件里面的参数一般不需要修改，但是这几个是需要根据自己实际的数据集进行调整的，首先最重要的就是分类大小`dataset_conf.num_class`，这个每个数据集的分类大小可能不一样，根据自己的实际情况设定。然后是`dataset_conf.batch_size`，如果是显存不够的话，可以减小这个参数。
```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：
```[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:14 - ----------- 额外配置参数 -----------
[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - configs: configs/bi_lstm.yml
[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - local_rank: 0
[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - pretrained_model: None
[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - resume_model: None
[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - save_model_path: models/
[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - use_gpu: True
[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:17 - ------------------------------------------------
[2023-08-18 18:48:49.680176 INFO ] utils:print_arguments:19 - ----------- 配置文件参数 -----------
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:22 - dataset_conf:
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:25 - aug_conf:
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - noise_aug_prob: 0.2
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - noise_dir: dataset/noise
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - speed_perturb: True
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - volume_aug_prob: 0.2
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - volume_perturb: False
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:25 - dataLoader:
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - batch_size: 32
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - num_workers: 4
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - do_vad: False
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:25 - eval_conf:
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - batch_size: 1
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - max_duration: 3
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - label_list_path: dataset/label_list.txt
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - max_duration: 3
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - min_duration: 0.5
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - sample_rate: 16000
[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - scaler_path: dataset/standard.m
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - target_dB: -20
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - test_list: dataset/test_list.txt
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - train_list: dataset/train_list.txt
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - use_dB_normalization: True
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:22 - model_conf:
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - num_class: None
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:22 - optimizer_conf:
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - learning_rate: 0.001
[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - optimizer: Adam
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - scheduler: WarmupCosineSchedulerLR
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:25 - scheduler_args:
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:27 - max_lr: 0.001
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:27 - min_lr: 1e-05
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:27 - warmup_epoch: 5
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - weight_decay: 1e-06
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:22 - preprocess_conf:
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - feature_method: CustomFeatures
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:22 - train_conf:
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - enable_amp: False
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - log_interval: 10
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - max_epoch: 60
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:31 - use_model: BidirectionalLSTM
[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:32 - ------------------------------------------------
[2023-08-18 18:48:49.683184 WARNING] trainer:__init__:66 - Windows系统不支持多线程读取数据，已自动关闭！
------------------------------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
================================================================================================
Linear-1 [[1, 312]] [1, 512] 160,256
LSTM-1 [[1, 1, 512]] [[1, 1, 512], [[2, 1, 256], [2, 1, 256]]] 1,576,960
Tanh-1 [[1, 512]] [1, 512] 0
Dropout-1 [[1, 512]] [1, 512] 0
Linear-2 [[1, 512]] [1, 256] 131,328
ReLU-1 [[1, 256]] [1, 256] 0
Linear-3 [[1, 256]] [1, 6] 1,542
================================================================================================
Total params: 1,870,086
Trainable params: 1,870,086
Non-trainable params: 0
------------------------------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.03
Params size (MB): 7.13
Estimated Total Size (MB): 7.16
------------------------------------------------------------------------------------------------
[2023-08-18 18:48:51.425936 INFO ] trainer:train:378 - 训练数据：4407
[2023-08-18 18:48:53.526136 INFO ] trainer:__train_epoch:331 - Train epoch: [1/60], batch: [0/138], loss: 1.80256, accuracy: 0.15625, learning rate: 0.00001000, speed: 15.24 data/sec, eta: 4:49:49
····················
```
# 评估
执行下面命令执行评估。
```shell
python eval.py --configs=configs/bi_lstm.yml
```
评估输出如下：
```shell
[2024-02-03 15:13:25.469242 INFO ] trainer:evaluate:461 - 成功加载模型：models/BiLSTM_Emotion2Vec/best_model/model.pth
100%|██████████████████████████████| 150/150 [00:00<00:00, 1281.96it/s]
评估消耗时间：1s，loss：0.61840，accuracy：0.87333
```
评估会出来输出准确率，还保存了混淆矩阵图片，保存路径`output/images/`，如下。
<br/>
<div align="center">
<img src="docs/images/image1.png" alt="混淆矩阵" width="600">
</div>
注意：如果类别标签是中文的，需要设置安装字体才能正常显示，一般情况下Windows无需安装，Ubuntu需要安装。如果Windows确实是确实字体，只需要[字体文件](https://github.com/tracyone/program_font)这里下载`.ttf`格式的文件，复制到`C:\Windows\Fonts`即可。Ubuntu系统操作如下。
1. 安装字体
```shell
git clone https://github.com/tracyone/program_font && cd program_font && ./install.sh
```
2. 执行下面Python代码
```python
import matplotlib
import shutil
import os
path = matplotlib.matplotlib_fname()
path = path.replace('matplotlibrc', 'fonts/ttf/')
print(path)
shutil.copy('/usr/share/fonts/MyFonts/simhei.ttf', path)
user_dir = os.path.expanduser('~')
shutil.rmtree(f'{user_dir}/.cache/matplotlib', ignore_errors=True)
```
# 预测
在训练结束之后，我们得到了一个模型参数文件，我们使用这个模型预测音频。
```shell
python infer.py --audio_path=dataset/test.wav
```
## 打赏作者
<br/>
<div align="center">
<p>打赏一块钱支持一下作者</p>
<img src="https://yeyupiaoling.cn/reward.png" alt="打赏作者" width="400">
</div>
# 参考资料
1. https://github.com/yeyupiaoling/AudioClassification-PaddlePaddle
Raw data
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"author": "yeyupiaoling",
"author_email": null,
"download_url": "https://github.com/yeyupiaoling/SpeechEmotionRecognition-PaddlePaddle.git",
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"description": "# \u57fa\u4e8ePaddlePaddle\u5b9e\u73b0\u7684\u8bed\u97f3\u60c5\u611f\u8bc6\u522b\u7cfb\u7edf\r\n\r\n\u672c\u9879\u76ee\u662f\u4e00\u4e2a\u8bed\u97f3\u60c5\u611f\u8bc6\u522b\u9879\u76ee\uff0c\u76ee\u524d\u6548\u679c\u4e00\u822c\uff0c\u4f9b\u5927\u5bb6\u5b66\u4e60\u4f7f\u7528\u3002\u540e\u9762\u4f1a\u6301\u7eed\u4f18\u5316\uff0c\u63d0\u9ad8\u51c6\u786e\u7387\uff0c\u5982\u679c\u540c\u5b66\u4eec\u6709\u597d\u7684\u5efa\u8bae\uff0c\u4e5f\u6b22\u8fce\u6765\u63a2\u8ba8\u3002\r\n\r\n**\u6b22\u8fce\u5927\u5bb6\u626b\u7801\u5165\u77e5\u8bc6\u661f\u7403\u6216\u8005QQ\u7fa4\u8ba8\u8bba\uff0c\u77e5\u8bc6\u661f\u7403\u91cc\u9762\u63d0\u4f9b\u9879\u76ee\u7684\u6a21\u578b\u6587\u4ef6\u548c\u535a\u4e3b\u5176\u4ed6\u76f8\u5173\u9879\u76ee\u7684\u6a21\u578b\u6587\u4ef6\uff0c\u4e5f\u5305\u62ec\u5176\u4ed6\u4e00\u4e9b\u8d44\u6e90\u3002**\r\n\r\n<div align=\"center\">\r\n <img src=\"https://yeyupiaoling.cn/zsxq.png\" alt=\"\u77e5\u8bc6\u661f\u7403\" width=\"400\">\r\n <img src=\"https://yeyupiaoling.cn/qq.png\" alt=\"QQ\u7fa4\" width=\"400\">\r\n</div>\r\n\r\n\r\n# \u4f7f\u7528\u51c6\u5907\r\n\r\n - Anaconda 3\r\n - Python 3.8\r\n - PaddlePaddle 2.4.0\r\n - Windows 10 or Ubuntu 18.04\r\n\r\n\r\n# \u6a21\u578b\u6d4b\u8bd5\u8868\r\n\r\n| \u6a21\u578b | Params(M) | \u9884\u5904\u7406\u65b9\u6cd5 | \u6570\u636e\u96c6 | \u7c7b\u522b\u6570\u91cf | \u51c6\u786e\u7387 | \u83b7\u53d6\u6a21\u578b |\r\n|:-----------------:|:---------:|:-----:|:-------:|:----:|:-------:|:--------:|\r\n| BidirectionalLSTM | 1.8 | Flank | RAVDESS | 8 | 0.95193 | \u52a0\u5165\u77e5\u8bc6\u661f\u7403\u83b7\u53d6 |\r\n\r\n\u8bf4\u660e\uff1a\r\n1. RAVDESS\u6570\u636e\u96c6\u53ea\u4f7f\u7528`Audio_Speech_Actors_01-24.zip`\r\n\r\n## \u5b89\u88c5\u73af\u5883\r\n\r\n - \u9996\u5148\u5b89\u88c5\u7684\u662fPaddlePaddle\u76842.6.1\u4ee5\u4e0a\u7684\u7248\u672c\uff0c\u5982\u679c\u5df2\u7ecf\u5b89\u88c5\u8fc7\u4e86\uff0c\u8bf7\u8df3\u8fc7\u3002\r\n```shell\r\nconda install paddlepaddle-gpu==2.6.1 cudatoolkit=11.7 -c https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/Paddle/ -c conda-forge\r\n```\r\n\r\n - \u5b89\u88c5ppser\u5e93\u3002\r\n \r\n\u4f7f\u7528pip\u5b89\u88c5\uff0c\u547d\u4ee4\u5982\u4e0b\uff1a\r\n```shell\r\npython -m pip install ppser -U -i https://pypi.tuna.tsinghua.edu.cn/simple\r\n```\r\n\r\n**\u5efa\u8bae\u6e90\u7801\u5b89\u88c5**\uff0c\u6e90\u7801\u5b89\u88c5\u80fd\u4fdd\u8bc1\u4f7f\u7528\u6700\u65b0\u4ee3\u7801\u3002\r\n```shell\r\ngit clone https://github.com/yeyupiaoling/SpeechEmotionRecognition-PaddlePaddle.git\r\ncd SpeechEmotionRecognition-PaddlePaddle/\r\npip install .\r\n```\r\n\r\n## \u51c6\u5907\u6570\u636e\r\n\r\n\u751f\u6210\u6570\u636e\u5217\u8868\uff0c\u7528\u4e8e\u4e0b\u4e00\u6b65\u7684\u8bfb\u53d6\u9700\u8981\uff0c\u9879\u76ee\u9ed8\u8ba4\u63d0\u4f9b\u4e00\u4e2a\u6570\u636e\u96c6[RAVDESS](https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip?download=1)\uff0c\u4e0b\u8f7d\u8fd9\u4e2a\u6570\u636e\u96c6\u5e76\u89e3\u538b\u5230`dataset`\u76ee\u5f55\u4e0b\u3002\r\n\r\n\u751f\u6210\u6570\u636e\u5217\u8868\uff0c\u7528\u4e8e\u4e0b\u4e00\u6b65\u7684\u8bfb\u53d6\u9700\u8981\uff0c\u9879\u76ee\u9ed8\u8ba4\u63d0\u4f9b\u4e00\u4e2a\u6570\u636e\u96c6[RAVDESS](https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip?download=1)\uff0c\u8fd9\u4e2a\u6570\u636e\u96c6\u7684[\u4ecb\u7ecd\u9875\u9762](https://zenodo.org/record/1188976#.XsAXemgzaUk)\uff0c\u8fd9\u4e2a\u6570\u636e\u5305\u542b\u4e2d\u6027\u3001\u5e73\u9759\u3001\u5feb\u4e50\u3001\u60b2\u4f24\u3001\u6124\u6012\u3001\u6050\u60e7\u3001\u538c\u6076\u3001\u60ca\u8bb6\u516b\u79cd\u60c5\u611f\uff0c\u672c\u9879\u76ee\u53ea\u4f7f\u7528\u91cc\u9762\u7684`Audio_Speech_Actors_01-24.zip`\uff0c\u6570\u636e\u96c6\uff0c\u8bf4\u8bdd\u7684\u8bed\u53e5\u53ea\u6709`Kids are talking by the door`\u548c`Dogs are sitting by the door`\uff0c\u53ef\u4ee5\u8bf4\u8fd9\u4e2a\u8bad\u7ec3\u96c6\u662f\u975e\u5e38\u7b80\u5355\u7684\u3002\u4e0b\u8f7d\u8fd9\u4e2a\u6570\u636e\u96c6\u5e76\u89e3\u538b\u5230`dataset`\u76ee\u5f55\u4e0b\u3002\r\n\r\n```shell\r\npython create_data.py\r\n```\r\n\r\n\u5982\u679c\u81ea\u5b9a\u4e49\u6570\u636e\u96c6\uff0c\u53ef\u4ee5\u6309\u7167\u4e0b\u9762\u683c\u5f0f\uff0c`audio_path`\u4e3a\u97f3\u9891\u6587\u4ef6\u8def\u5f84\uff0c\u7528\u6237\u9700\u8981\u63d0\u524d\u628a\u97f3\u9891\u6570\u636e\u96c6\u5b58\u653e\u5728`dataset/audio`\u76ee\u5f55\u4e0b\uff0c\u6bcf\u4e2a\u6587\u4ef6\u5939\u5b58\u653e\u4e00\u4e2a\u7c7b\u522b\u7684\u97f3\u9891\u6570\u636e\uff0c\u6bcf\u6761\u97f3\u9891\u6570\u636e\u957f\u5ea6\u57283\u79d2\u5de6\u53f3\uff0c\u5982 `dataset/audio/angry/\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7`\u3002`audio`\u662f\u6570\u636e\u5217\u8868\u5b58\u653e\u7684\u4f4d\u7f6e\uff0c\u751f\u6210\u7684\u6570\u636e\u7c7b\u522b\u7684\u683c\u5f0f\u4e3a `\u97f3\u9891\u8def\u5f84\\t\u97f3\u9891\u5bf9\u5e94\u7684\u7c7b\u522b\u6807\u7b7e`\uff0c\u97f3\u9891\u8def\u5f84\u548c\u6807\u7b7e\u7528\u5236\u8868\u7b26 `\\t`\u5206\u5f00\u3002\u8bfb\u8005\u4e5f\u53ef\u4ee5\u6839\u636e\u81ea\u5df1\u5b58\u653e\u6570\u636e\u7684\u65b9\u5f0f\u4fee\u6539\u4ee5\u4e0b\u51fd\u6570\u3002\r\n\r\n\u6267\u884c`create_data.py`\u91cc\u9762\u7684`get_data_list('dataset/audios', 'dataset')`\u51fd\u6570\u5373\u53ef\u751f\u6210\u6570\u636e\u5217\u8868\uff0c\u540c\u65f6\u4e5f\u751f\u6210\u5f52\u4e00\u5316\u6587\u4ef6\uff0c\u5177\u4f53\u770b\u4ee3\u7801\u3002\r\n\r\n```shell\r\npython create_data.py\r\n```\r\n\r\n\u751f\u6210\u7684\u5217\u8868\u662f\u957f\u8fd9\u6837\u7684\uff0c\u524d\u9762\u662f\u97f3\u9891\u7684\u8def\u5f84\uff0c\u540e\u9762\u662f\u8be5\u97f3\u9891\u5bf9\u5e94\u7684\u6807\u7b7e\uff0c\u4ece0\u5f00\u59cb\uff0c\u8def\u5f84\u548c\u6807\u7b7e\u4e4b\u95f4\u7528`\\t`\u9694\u5f00\u3002\r\n\r\n```shell\r\ndataset/Audio_Speech_Actors_01-24/Actor_13/03-01-01-01-02-01-13.wav\t0\r\ndataset/Audio_Speech_Actors_01-24/Actor_01/03-01-02-01-01-01-01.wav\t1\r\ndataset/Audio_Speech_Actors_01-24/Actor_01/03-01-03-02-01-01-01.wav\t2\r\n```\r\n\r\n**\u6ce8\u610f\uff1a** `create_data.py`\u91cc\u9762\u7684`create_standard('configs/bi_lstm.yml')`\u51fd\u6570\u5fc5\u987b\u8981\u6267\u884c\u7684\uff0c\u8fd9\u4e2a\u662f\u751f\u6210\u5f52\u4e00\u5316\u7684\u6587\u4ef6\u3002\r\n\r\n\r\n# \u63d0\u53d6\u7279\u5f81\uff08\u53ef\u9009\uff09\r\n\r\n\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\uff0c\u9996\u5148\u662f\u8981\u8bfb\u53d6\u97f3\u9891\u6570\u636e\uff0c\u7136\u540e\u63d0\u53d6\u7279\u5f81\uff0c\u6700\u540e\u518d\u8fdb\u884c\u8bad\u7ec3\u3002\u5176\u4e2d\u8bfb\u53d6\u97f3\u9891\u6570\u636e\u3001\u63d0\u53d6\u7279\u5f81\u4e5f\u662f\u6bd4\u8f83\u6d88\u8017\u65f6\u95f4\u7684\uff0c\u6240\u4ee5\u6211\u4eec\u53ef\u4ee5\u9009\u62e9\u63d0\u524d\u63d0\u53d6\u597d\u53d6\u7279\u5f81\uff0c\u8bad\u7ec3\u6a21\u578b\u7684\u662f\u5c31\u53ef\u4ee5\u76f4\u63a5\u52a0\u8f7d\u63d0\u53d6\u597d\u7684\u7279\u5f81\uff0c\u8fd9\u6837\u8bad\u7ec3\u901f\u5ea6\u4f1a\u66f4\u5feb\u3002\u8fd9\u4e2a\u63d0\u53d6\u7279\u5f81\u662f\u53ef\u9009\u62e9\uff0c\u5982\u679c\u6ca1\u6709\u63d0\u53d6\u597d\u7684\u7279\u5f81\uff0c\u8bad\u7ec3\u6a21\u578b\u7684\u65f6\u5019\u5c31\u4f1a\u4ece\u8bfb\u53d6\u97f3\u9891\u6570\u636e\uff0c\u7136\u540e\u63d0\u53d6\u7279\u5f81\u5f00\u59cb\u3002\u63d0\u53d6\u7279\u5f81\u6b65\u9aa4\u5982\u4e0b\uff1a\r\n\r\n1. \u6267\u884c`extract_features.py`\uff0c\u63d0\u53d6\u7279\u5f81\uff0c\u7279\u5f81\u4f1a\u4fdd\u5b58\u5728`dataset/features`\u76ee\u5f55\u4e0b\uff0c\u5e76\u751f\u6210\u65b0\u7684\u6570\u636e\u5217\u8868`train_list_features.txt`\u548c`test_list_features.txt`\u3002\r\n\r\n```shell\r\npython extract_features.py --configs=configs/bi_lstm.yml --save_dir=dataset/features\r\n```\r\n\r\n2. \u4fee\u6539\u914d\u7f6e\u6587\u4ef6\uff0c\u5c06`dataset_conf.train_list`\u548c`dataset_conf.test_list`\u4fee\u6539\u4e3a`train_list_features.txt`\u548c`test_list_features.txt`\u3002\r\n\r\n\r\n## \u8bad\u7ec3\r\n\r\n\u63a5\u7740\u5c31\u53ef\u4ee5\u5f00\u59cb\u8bad\u7ec3\u6a21\u578b\u4e86\uff0c\u521b\u5efa `train.py`\u3002\u914d\u7f6e\u6587\u4ef6\u91cc\u9762\u7684\u53c2\u6570\u4e00\u822c\u4e0d\u9700\u8981\u4fee\u6539\uff0c\u4f46\u662f\u8fd9\u51e0\u4e2a\u662f\u9700\u8981\u6839\u636e\u81ea\u5df1\u5b9e\u9645\u7684\u6570\u636e\u96c6\u8fdb\u884c\u8c03\u6574\u7684\uff0c\u9996\u5148\u6700\u91cd\u8981\u7684\u5c31\u662f\u5206\u7c7b\u5927\u5c0f`dataset_conf.num_class`\uff0c\u8fd9\u4e2a\u6bcf\u4e2a\u6570\u636e\u96c6\u7684\u5206\u7c7b\u5927\u5c0f\u53ef\u80fd\u4e0d\u4e00\u6837\uff0c\u6839\u636e\u81ea\u5df1\u7684\u5b9e\u9645\u60c5\u51b5\u8bbe\u5b9a\u3002\u7136\u540e\u662f`dataset_conf.batch_size`\uff0c\u5982\u679c\u662f\u663e\u5b58\u4e0d\u591f\u7684\u8bdd\uff0c\u53ef\u4ee5\u51cf\u5c0f\u8fd9\u4e2a\u53c2\u6570\u3002\r\n\r\n```shell\r\n# \u5355\u5361\u8bad\u7ec3\r\nCUDA_VISIBLE_DEVICES=0 python train.py\r\n# \u591a\u5361\u8bad\u7ec3\r\npython -m paddle.distributed.launch --gpus '0,1' train.py\r\n```\r\n\r\n\r\n\u8bad\u7ec3\u8f93\u51fa\u65e5\u5fd7\uff1a\r\n```[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:14 - ----------- \u989d\u5916\u914d\u7f6e\u53c2\u6570 -----------\r\n[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - configs: configs/bi_lstm.yml\r\n[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - local_rank: 0\r\n[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - pretrained_model: None\r\n[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - resume_model: None\r\n[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - save_model_path: models/\r\n[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:16 - use_gpu: True\r\n[2023-08-18 18:48:49.662963 INFO ] utils:print_arguments:17 - ------------------------------------------------\r\n[2023-08-18 18:48:49.680176 INFO ] utils:print_arguments:19 - ----------- \u914d\u7f6e\u6587\u4ef6\u53c2\u6570 -----------\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:22 - dataset_conf:\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:25 - \taug_conf:\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tnoise_aug_prob: 0.2\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tnoise_dir: dataset/noise\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tspeed_perturb: True\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tvolume_aug_prob: 0.2\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tvolume_perturb: False\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:25 - \tdataLoader:\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tbatch_size: 32\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tnum_workers: 4\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - \tdo_vad: False\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:25 - \teval_conf:\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tbatch_size: 1\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:27 - \t\tmax_duration: 3\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - \tlabel_list_path: dataset/label_list.txt\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - \tmax_duration: 3\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - \tmin_duration: 0.5\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - \tsample_rate: 16000\r\n[2023-08-18 18:48:49.681177 INFO ] utils:print_arguments:29 - \tscaler_path: dataset/standard.m\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - \ttarget_dB: -20\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - \ttest_list: dataset/test_list.txt\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - \ttrain_list: dataset/train_list.txt\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - \tuse_dB_normalization: True\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:22 - model_conf:\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - \tnum_class: None\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:22 - optimizer_conf:\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - \tlearning_rate: 0.001\r\n[2023-08-18 18:48:49.682177 INFO ] utils:print_arguments:29 - \toptimizer: Adam\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - \tscheduler: WarmupCosineSchedulerLR\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:25 - \tscheduler_args:\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:27 - \t\tmax_lr: 0.001\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:27 - \t\tmin_lr: 1e-05\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:27 - \t\twarmup_epoch: 5\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - \tweight_decay: 1e-06\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:22 - preprocess_conf:\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - \tfeature_method: CustomFeatures\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:22 - train_conf:\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - \tenable_amp: False\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - \tlog_interval: 10\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:29 - \tmax_epoch: 60\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:31 - use_model: BidirectionalLSTM\r\n[2023-08-18 18:48:49.683184 INFO ] utils:print_arguments:32 - ------------------------------------------------\r\n[2023-08-18 18:48:49.683184 WARNING] trainer:__init__:66 - Windows\u7cfb\u7edf\u4e0d\u652f\u6301\u591a\u7ebf\u7a0b\u8bfb\u53d6\u6570\u636e\uff0c\u5df2\u81ea\u52a8\u5173\u95ed\uff01\r\n------------------------------------------------------------------------------------------------\r\n Layer (type) Input Shape Output Shape Param # \r\n================================================================================================\r\n Linear-1 [[1, 312]] [1, 512] 160,256 \r\n LSTM-1 [[1, 1, 512]] [[1, 1, 512], [[2, 1, 256], [2, 1, 256]]] 1,576,960 \r\n Tanh-1 [[1, 512]] [1, 512] 0 \r\n Dropout-1 [[1, 512]] [1, 512] 0 \r\n Linear-2 [[1, 512]] [1, 256] 131,328 \r\n ReLU-1 [[1, 256]] [1, 256] 0 \r\n Linear-3 [[1, 256]] [1, 6] 1,542 \r\n================================================================================================\r\nTotal params: 1,870,086\r\nTrainable params: 1,870,086\r\nNon-trainable params: 0\r\n------------------------------------------------------------------------------------------------\r\nInput size (MB): 0.00\r\nForward/backward pass size (MB): 0.03\r\nParams size (MB): 7.13\r\nEstimated Total Size (MB): 7.16\r\n------------------------------------------------------------------------------------------------\r\n[2023-08-18 18:48:51.425936 INFO ] trainer:train:378 - \u8bad\u7ec3\u6570\u636e\uff1a4407\r\n[2023-08-18 18:48:53.526136 INFO ] trainer:__train_epoch:331 - Train epoch: [1/60], batch: [0/138], loss: 1.80256, accuracy: 0.15625, learning rate: 0.00001000, speed: 15.24 data/sec, eta: 4:49:49\r\n\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\u00b7\r\n```\r\n\r\n# \u8bc4\u4f30\r\n\r\n\u6267\u884c\u4e0b\u9762\u547d\u4ee4\u6267\u884c\u8bc4\u4f30\u3002\r\n\r\n```shell\r\npython eval.py --configs=configs/bi_lstm.yml\r\n```\r\n\r\n\u8bc4\u4f30\u8f93\u51fa\u5982\u4e0b\uff1a\r\n```shell\r\n[2024-02-03 15:13:25.469242 INFO ] trainer:evaluate:461 - \u6210\u529f\u52a0\u8f7d\u6a21\u578b\uff1amodels/BiLSTM_Emotion2Vec/best_model/model.pth\r\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 150/150 [00:00<00:00, 1281.96it/s]\r\n\u8bc4\u4f30\u6d88\u8017\u65f6\u95f4\uff1a1s\uff0closs\uff1a0.61840\uff0caccuracy\uff1a0.87333\r\n```\r\n\r\n\u8bc4\u4f30\u4f1a\u51fa\u6765\u8f93\u51fa\u51c6\u786e\u7387\uff0c\u8fd8\u4fdd\u5b58\u4e86\u6df7\u6dc6\u77e9\u9635\u56fe\u7247\uff0c\u4fdd\u5b58\u8def\u5f84`output/images/`\uff0c\u5982\u4e0b\u3002\r\n<br/>\r\n<div align=\"center\">\r\n<img src=\"docs/images/image1.png\" alt=\"\u6df7\u6dc6\u77e9\u9635\" width=\"600\">\r\n</div>\r\n\r\n\u6ce8\u610f\uff1a\u5982\u679c\u7c7b\u522b\u6807\u7b7e\u662f\u4e2d\u6587\u7684\uff0c\u9700\u8981\u8bbe\u7f6e\u5b89\u88c5\u5b57\u4f53\u624d\u80fd\u6b63\u5e38\u663e\u793a\uff0c\u4e00\u822c\u60c5\u51b5\u4e0bWindows\u65e0\u9700\u5b89\u88c5\uff0cUbuntu\u9700\u8981\u5b89\u88c5\u3002\u5982\u679cWindows\u786e\u5b9e\u662f\u786e\u5b9e\u5b57\u4f53\uff0c\u53ea\u9700\u8981[\u5b57\u4f53\u6587\u4ef6](https://github.com/tracyone/program_font)\u8fd9\u91cc\u4e0b\u8f7d`.ttf`\u683c\u5f0f\u7684\u6587\u4ef6\uff0c\u590d\u5236\u5230`C:\\Windows\\Fonts`\u5373\u53ef\u3002Ubuntu\u7cfb\u7edf\u64cd\u4f5c\u5982\u4e0b\u3002\r\n\r\n1. \u5b89\u88c5\u5b57\u4f53\r\n```shell\r\ngit clone https://github.com/tracyone/program_font && cd program_font && ./install.sh\r\n```\r\n\r\n2. \u6267\u884c\u4e0b\u9762Python\u4ee3\u7801\r\n```python\r\nimport matplotlib\r\nimport shutil\r\nimport os\r\n\r\npath = matplotlib.matplotlib_fname()\r\npath = path.replace('matplotlibrc', 'fonts/ttf/')\r\nprint(path)\r\nshutil.copy('/usr/share/fonts/MyFonts/simhei.ttf', path)\r\nuser_dir = os.path.expanduser('~')\r\nshutil.rmtree(f'{user_dir}/.cache/matplotlib', ignore_errors=True)\r\n```\r\n\r\n\r\n# \u9884\u6d4b\r\n\r\n\u5728\u8bad\u7ec3\u7ed3\u675f\u4e4b\u540e\uff0c\u6211\u4eec\u5f97\u5230\u4e86\u4e00\u4e2a\u6a21\u578b\u53c2\u6570\u6587\u4ef6\uff0c\u6211\u4eec\u4f7f\u7528\u8fd9\u4e2a\u6a21\u578b\u9884\u6d4b\u97f3\u9891\u3002\r\n\r\n```shell\r\npython infer.py --audio_path=dataset/test.wav\r\n```\r\n\r\n## \u6253\u8d4f\u4f5c\u8005\r\n<br/>\r\n<div align=\"center\">\r\n<p>\u6253\u8d4f\u4e00\u5757\u94b1\u652f\u6301\u4e00\u4e0b\u4f5c\u8005</p>\r\n<img src=\"https://yeyupiaoling.cn/reward.png\" alt=\"\u6253\u8d4f\u4f5c\u8005\" width=\"400\">\r\n</div>\r\n\r\n# \u53c2\u8003\u8d44\u6599\r\n\r\n1. https://github.com/yeyupiaoling/AudioClassification-PaddlePaddle\r\n",
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