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(简体中文|[English](./README.md))
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# 声纹识别
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## 介绍
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声纹识别是一项用计算机程序自动提取说话人特征的技术。
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这个 demo 是一个从给定音频文件提取说话人特征，它可以通过使用 `PaddleSpeech` 的单个命令或 python 中的几行代码来实现。
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## 使用方法
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### 1. 安装
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请看[安装文档](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install_cn.md)。
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你可以从 easy，medium，hard 三中方式中选择一种方式安装。
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### 2. 准备输入
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这个 demo 的输入应该是一个 WAV 文件（`.wav`），并且采样率必须与模型的采样率相同。
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可以下载此 demo 的示例音频：
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```bash
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# 该音频的内容是数字串 85236145389
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wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
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```
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### 3. 使用方法
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- 命令行 (推荐使用)
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```bash
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paddlespeech vector --task spk --input 85236145389.wav
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echo -e "demo1 85236145389.wav" > vec.job
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paddlespeech vector --task spk --input vec.job
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echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk
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```
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使用方法：
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```bash
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paddlespeech vector --help
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```
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参数：
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- `input`(必须输入)：用于识别的音频文件。
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- `model`：声纹任务的模型，默认值：`ecapatdnn_voxceleb12`。
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- `sample_rate`：音频采样率，默认值：`16000`。
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- `config`：声纹任务的参数文件，若不设置则使用预训练模型中的默认配置，默认值：`None`。
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- `ckpt_path`：模型参数文件，若不设置则下载预训练模型使用，默认值：`None`。
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- `device`：执行预测的设备，默认值：当前系统下 paddlepaddle 的默认 device。
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输出：
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```bash
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demo {'dim': 192, 'embedding': array([ -5.749211 , 9.505463 , -8.200284 , -5.2075014 ,
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5.3940268 , -3.04878 , 1.611095 , 10.127234 ,
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-10.534177 , -15.821609 , 1.2032688 , -0.35080156,
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1.2629458 , -12.643498 , -2.5758228 , -11.343508 ,
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2.3385992 , -8.719341 , 14.213509 , 15.404744 ,
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-0.39327756, 6.338786 , 2.688887 , 8.7104025 ,
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17.469526 , -8.77959 , 7.0576906 , 4.648855 ,
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-1.3089896 , -23.294737 , 8.013747 , 13.891729 ,
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-9.926753 , 5.655307 , -5.9422326 , -22.842539 ,
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0.6293588 , -18.46266 , -10.811862 , 9.8192625 ,
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3.0070958 , 3.8072643 , -2.3861165 , 3.0821571 ,
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-14.739942 , 1.7594414 , -0.6485091 , 4.485623 ,
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2.0207152 , 7.264915 , -6.40137 , 23.63524 ,
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2.9711294 , -22.708025 , 9.93719 , 20.354511 ,
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-10.324688 , -0.700492 , -8.783211 , -5.27593 ,
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15.999649 , 3.3004563 , 12.747926 , 15.429879 ,
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4.7849145 , 5.6699696 , -2.3826702 , 10.605882 ,
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3.9112158 , 3.1500628 , 15.859915 , -2.1832209 ,
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-23.908653 , -6.4799504 , -4.5365124 , -9.224193 ,
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14.568347 , -10.568833 , 4.982321 , -4.342062 ,
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0.0914714 , 12.645902 , -5.74285 , -3.2141201 ,
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-2.7173362 , -6.680575 , 0.4757669 , -5.035051 ,
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-6.7964664 , 16.865469 , -11.54324 , 7.681869 ,
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0.44475392, 9.708182 , -8.932846 , 0.4123232 ,
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-4.361452 , 1.3948607 , 9.511665 , 0.11667654,
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2.9079323 , 6.049952 , 9.275183 , -18.078873 ,
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6.2983274 , -0.7500531 , -2.725033 , -7.6027865 ,
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3.3404543 , 2.990815 , 4.010979 , 11.000591 ,
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-2.8873312 , 7.1352735 , -16.79663 , 18.495346 ,
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-14.293832 , 7.89578 , 2.2714825 , 22.976387 ,
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-4.875734 , -3.0836344 , -2.9999814 , 13.751918 ,
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6.448228 , -11.924197 , 2.171869 , 2.0423572 ,
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-6.173772 , 10.778437 , 25.77281 , -4.9495463 ,
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14.57806 , 0.3044315 , 2.6132357 , -7.591999 ,
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-2.076944 , 9.025118 , 1.7834753 , -3.1799617 ,
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-4.9401326 , 23.465864 , 5.1685796 , -9.018578 ,
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9.037825 , -4.4150195 , 6.859591 , -12.274467 ,
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-0.88911164, 5.186309 , -3.9988663 , -13.638606 ,
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-9.925445 , -0.06329413, -3.6709652 , -12.397416 ,
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-12.719869 , -1.395601 , 2.1150916 , 5.7381287 ,
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-4.4691963 , -3.82819 , -0.84233856, -1.1604277 ,
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-13.490127 , 8.731719 , -20.778936 , -11.495662 ,
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5.8033476 , -4.752041 , 10.833007 , -6.717991 ,
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4.504732 , 13.4244375 , 1.1306485 , 7.3435574 ,
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1.400918 , 14.704036 , -9.501399 , 7.2315617 ,
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-6.417456 , 1.3333273 , 11.872697 , -0.30664724,
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8.8845 , 6.5569253 , 4.7948146 , 0.03662816,
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-8.704245 , 6.224871 , -3.2701402 , -11.508579 ],
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dtype=float32)}
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```
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- Python API
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```python
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import paddle
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from paddlespeech.cli import VectorExecutor
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vector_executor = VectorExecutor()
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audio_emb = vector_executor(
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model='ecapatdnn_voxceleb12',
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sample_rate=16000,
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config=None, # Set `config` and `ckpt_path` to None to use pretrained model.
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ckpt_path=None,
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audio_file='./85236145389.wav',
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force_yes=False,
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device=paddle.get_device())
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print('Audio embedding Result: \n{}'.format(audio_emb))
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```
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输出：
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```bash
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# Vector Result:
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{'dim': 192, 'embedding': array([ -5.749211 , 9.505463 , -8.200284 , -5.2075014 ,
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5.3940268 , -3.04878 , 1.611095 , 10.127234 ,
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-10.534177 , -15.821609 , 1.2032688 , -0.35080156,
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1.2629458 , -12.643498 , -2.5758228 , -11.343508 ,
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2.3385992 , -8.719341 , 14.213509 , 15.404744 ,
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-0.39327756, 6.338786 , 2.688887 , 8.7104025 ,
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17.469526 , -8.77959 , 7.0576906 , 4.648855 ,
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-1.3089896 , -23.294737 , 8.013747 , 13.891729 ,
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-9.926753 , 5.655307 , -5.9422326 , -22.842539 ,
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0.6293588 , -18.46266 , -10.811862 , 9.8192625 ,
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3.0070958 , 3.8072643 , -2.3861165 , 3.0821571 ,
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-14.739942 , 1.7594414 , -0.6485091 , 4.485623 ,
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2.0207152 , 7.264915 , -6.40137 , 23.63524 ,
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2.9711294 , -22.708025 , 9.93719 , 20.354511 ,
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-10.324688 , -0.700492 , -8.783211 , -5.27593 ,
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15.999649 , 3.3004563 , 12.747926 , 15.429879 ,
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4.7849145 , 5.6699696 , -2.3826702 , 10.605882 ,
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3.9112158 , 3.1500628 , 15.859915 , -2.1832209 ,
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-23.908653 , -6.4799504 , -4.5365124 , -9.224193 ,
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14.568347 , -10.568833 , 4.982321 , -4.342062 ,
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0.0914714 , 12.645902 , -5.74285 , -3.2141201 ,
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-2.7173362 , -6.680575 , 0.4757669 , -5.035051 ,
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-6.7964664 , 16.865469 , -11.54324 , 7.681869 ,
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0.44475392, 9.708182 , -8.932846 , 0.4123232 ,
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-4.361452 , 1.3948607 , 9.511665 , 0.11667654,
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2.9079323 , 6.049952 , 9.275183 , -18.078873 ,
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6.2983274 , -0.7500531 , -2.725033 , -7.6027865 ,
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3.3404543 , 2.990815 , 4.010979 , 11.000591 ,
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-2.8873312 , 7.1352735 , -16.79663 , 18.495346 ,
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-14.293832 , 7.89578 , 2.2714825 , 22.976387 ,
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-4.875734 , -3.0836344 , -2.9999814 , 13.751918 ,
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6.448228 , -11.924197 , 2.171869 , 2.0423572 ,
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-6.173772 , 10.778437 , 25.77281 , -4.9495463 ,
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14.57806 , 0.3044315 , 2.6132357 , -7.591999 ,
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-2.076944 , 9.025118 , 1.7834753 , -3.1799617 ,
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-4.9401326 , 23.465864 , 5.1685796 , -9.018578 ,
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9.037825 , -4.4150195 , 6.859591 , -12.274467 ,
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-0.88911164, 5.186309 , -3.9988663 , -13.638606 ,
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-9.925445 , -0.06329413, -3.6709652 , -12.397416 ,
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-12.719869 , -1.395601 , 2.1150916 , 5.7381287 ,
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-4.4691963 , -3.82819 , -0.84233856, -1.1604277 ,
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-13.490127 , 8.731719 , -20.778936 , -11.495662 ,
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5.8033476 , -4.752041 , 10.833007 , -6.717991 ,
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4.504732 , 13.4244375 , 1.1306485 , 7.3435574 ,
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1.400918 , 14.704036 , -9.501399 , 7.2315617 ,
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-6.417456 , 1.3333273 , 11.872697 , -0.30664724,
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8.8845 , 6.5569253 , 4.7948146 , 0.03662816,
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-8.704245 , 6.224871 , -3.2701402 , -11.508579 ],
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dtype=float32)}
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```
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### 4.预训练模型
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以下是 PaddleSpeech 提供的可以被命令行和 python API 使用的预训练模型列表：
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| 模型 | 采样率
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| :--- | :---: |
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| ecapatdnn_voxceleb12 | 16k
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