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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 [ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268
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-3.04878 1.611095 10.127234 -10.534177 -15.821609
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1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228
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-11.343508 2.3385992 -8.719341 14.213509 15.404744
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-0.39327756 6.338786 2.688887 8.7104025 17.469526
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-8.77959 7.0576906 4.648855 -1.3089896 -23.294737
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8.013747 13.891729 -9.926753 5.655307 -5.9422326
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-22.842539 0.6293588 -18.46266 -10.811862 9.8192625
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3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942
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1.7594414 -0.6485091 4.485623 2.0207152 7.264915
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-6.40137 23.63524 2.9711294 -22.708025 9.93719
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20.354511 -10.324688 -0.700492 -8.783211 -5.27593
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15.999649 3.3004563 12.747926 15.429879 4.7849145
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5.6699696 -2.3826702 10.605882 3.9112158 3.1500628
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15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124
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-9.224193 14.568347 -10.568833 4.982321 -4.342062
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0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362
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-6.680575 0.4757669 -5.035051 -6.7964664 16.865469
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-11.54324 7.681869 0.44475392 9.708182 -8.932846
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0.4123232 -4.361452 1.3948607 9.511665 0.11667654
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2.9079323 6.049952 9.275183 -18.078873 6.2983274
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-0.7500531 -2.725033 -7.6027865 3.3404543 2.990815
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4.010979 11.000591 -2.8873312 7.1352735 -16.79663
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18.495346 -14.293832 7.89578 2.2714825 22.976387
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-4.875734 -3.0836344 -2.9999814 13.751918 6.448228
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-11.924197 2.171869 2.0423572 -6.173772 10.778437
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25.77281 -4.9495463 14.57806 0.3044315 2.6132357
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-7.591999 -2.076944 9.025118 1.7834753 -3.1799617
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-4.9401326 23.465864 5.1685796 -9.018578 9.037825
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-4.4150195 6.859591 -12.274467 -0.88911164 5.186309
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-3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652
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-12.397416 -12.719869 -1.395601 2.1150916 5.7381287
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-4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127
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8.731719 -20.778936 -11.495662 5.8033476 -4.752041
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10.833007 -6.717991 4.504732 13.4244375 1.1306485
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7.3435574 1.400918 14.704036 -9.501399 7.2315617
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-6.417456 1.3333273 11.872697 -0.30664724 8.8845
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6.5569253 4.7948146 0.03662816 -8.704245 6.224871
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-3.2701402 -11.508579 ]
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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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[ -5.749211 9.505463 -8.200284 -5.2075014 5.3940268
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-3.04878 1.611095 10.127234 -10.534177 -15.821609
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1.2032688 -0.35080156 1.2629458 -12.643498 -2.5758228
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-11.343508 2.3385992 -8.719341 14.213509 15.404744
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-0.39327756 6.338786 2.688887 8.7104025 17.469526
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-8.77959 7.0576906 4.648855 -1.3089896 -23.294737
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8.013747 13.891729 -9.926753 5.655307 -5.9422326
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-22.842539 0.6293588 -18.46266 -10.811862 9.8192625
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3.0070958 3.8072643 -2.3861165 3.0821571 -14.739942
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1.7594414 -0.6485091 4.485623 2.0207152 7.264915
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-6.40137 23.63524 2.9711294 -22.708025 9.93719
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20.354511 -10.324688 -0.700492 -8.783211 -5.27593
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15.999649 3.3004563 12.747926 15.429879 4.7849145
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5.6699696 -2.3826702 10.605882 3.9112158 3.1500628
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15.859915 -2.1832209 -23.908653 -6.4799504 -4.5365124
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-9.224193 14.568347 -10.568833 4.982321 -4.342062
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0.0914714 12.645902 -5.74285 -3.2141201 -2.7173362
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-6.680575 0.4757669 -5.035051 -6.7964664 16.865469
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-11.54324 7.681869 0.44475392 9.708182 -8.932846
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0.4123232 -4.361452 1.3948607 9.511665 0.11667654
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2.9079323 6.049952 9.275183 -18.078873 6.2983274
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-0.7500531 -2.725033 -7.6027865 3.3404543 2.990815
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4.010979 11.000591 -2.8873312 7.1352735 -16.79663
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18.495346 -14.293832 7.89578 2.2714825 22.976387
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-4.875734 -3.0836344 -2.9999814 13.751918 6.448228
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-11.924197 2.171869 2.0423572 -6.173772 10.778437
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25.77281 -4.9495463 14.57806 0.3044315 2.6132357
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-7.591999 -2.076944 9.025118 1.7834753 -3.1799617
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-4.9401326 23.465864 5.1685796 -9.018578 9.037825
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-4.4150195 6.859591 -12.274467 -0.88911164 5.186309
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-3.9988663 -13.638606 -9.925445 -0.06329413 -3.6709652
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-12.397416 -12.719869 -1.395601 2.1150916 5.7381287
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-4.4691963 -3.82819 -0.84233856 -1.1604277 -13.490127
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8.731719 -20.778936 -11.495662 5.8033476 -4.752041
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10.833007 -6.717991 4.504732 13.4244375 1.1306485
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7.3435574 1.400918 14.704036 -9.501399 7.2315617
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-6.417456 1.3333273 11.872697 -0.30664724 8.8845
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6.5569253 4.7948146 0.03662816 -8.704245 6.224871
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-3.2701402 -11.508579 ]
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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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