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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 是从一个给定音频文件中提取说话人特征，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 三种方式中选择一种方式安装。
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### 2. 准备输入
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声纹cli 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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wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.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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paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav"
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echo -e "demo4 85236145389.wav 85236145389.wav \n demo5 85236145389.wav 123456789.wav" > vec.job
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paddlespeech vector --task score --input vec.job
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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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- `task` (必须输入): 用于指定 `vector` 处理的具体任务，默认是 `spk`。
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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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[ -1.3251206 7.8606825 -4.620626 0.3000721 2.2648535
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-1.1931441 3.0647137 7.673595 -6.0044727 -12.02426
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-1.9496069 3.1269536 1.618838 -7.6383104 -1.2299773
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-12.338331 2.1373026 -5.3957124 9.717328 5.6752305
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3.7805123 3.0597172 3.429692 8.97601 13.174125
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-0.53132284 8.9424715 4.46511 -4.4262476 -9.726503
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8.399328 7.2239175 -7.435854 2.9441683 -4.3430395
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-13.886965 -1.6346735 -10.9027405 -5.311245 3.8007221
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3.8976038 -2.1230774 -2.3521194 4.151031 -7.4048667
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0.13911647 2.4626107 4.9664545 0.9897574 5.4839754
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-3.3574002 10.1340065 -0.6120171 -10.403095 4.6007543
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16.00935 -7.7836914 -4.1945305 -6.9368606 1.1789556
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11.490801 4.2380238 9.550931 8.375046 7.5089145
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-0.65707296 -0.30051577 2.8406055 3.0828028 0.730817
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6.148354 0.13766119 -13.424735 -7.7461405 -2.3227983
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-8.305252 2.9879124 -10.995229 0.15211068 -2.3820348
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-1.7984174 8.495629 -5.8522367 -3.755498 0.6989711
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-5.2702994 -2.6188622 -1.8828466 -4.64665 14.078544
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-0.5495333 10.579158 -3.2160501 9.349004 -4.381078
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-11.675817 -2.8630207 4.5721755 2.246612 -4.574342
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1.8610188 2.3767874 5.6257877 -9.784078 0.64967257
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-1.4579505 0.4263264 -4.9211264 -2.454784 3.4869802
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-0.42654222 8.341269 1.356552 7.0966883 -13.102829
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8.016734 -7.1159344 1.8699781 0.208721 14.699384
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-1.025278 -2.6107233 -2.5082312 8.427193 6.9138527
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-6.2912464 0.6157366 2.489688 -3.4668267 9.921763
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11.200815 -0.1966403 7.4916005 -0.62312716 -0.25848144
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-9.947997 -0.9611041 1.1649219 -2.1907122 -1.5028487
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-0.51926106 15.165954 2.4649463 -0.9980445 7.4416637
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-2.0768049 3.5896823 -7.3055434 -7.5620847 4.323335
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0.0804418 -6.56401 -2.3148053 -1.7642345 -2.4708817
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-7.675618 -9.548878 -1.0177554 0.16986446 2.5877135
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-1.8752296 -0.36614323 -6.0493784 -2.3965611 -5.9453387
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0.9424033 -13.155974 -7.457801 0.14658108 -3.742797
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5.8414927 -1.2872906 5.5694313 12.57059 1.0939219
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2.2142086 1.9181576 6.9914207 -5.888139 3.1409824
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-2.003628 2.4434285 9.973139 5.03668 2.0051203
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2.8615603 5.860224 2.9176188 -1.6311141 2.0292206
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-4.070415 -6.831437 ]
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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.vector 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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device=paddle.get_device())
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print('Audio embedding Result: \n{}'.format(audio_emb))
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test_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='./123456789.wav',
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device=paddle.get_device())
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print('Test embedding Result: \n{}'.format(test_emb))
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# score range [0, 1]
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score = vector_executor.get_embeddings_score(audio_emb, test_emb)
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print(f"Eembeddings Score: {score}")
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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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Audio embedding Result:
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[ -1.3251206 7.8606825 -4.620626 0.3000721 2.2648535
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-1.1931441 3.0647137 7.673595 -6.0044727 -12.02426
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-1.9496069 3.1269536 1.618838 -7.6383104 -1.2299773
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-12.338331 2.1373026 -5.3957124 9.717328 5.6752305
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3.7805123 3.0597172 3.429692 8.97601 13.174125
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-0.53132284 8.9424715 4.46511 -4.4262476 -9.726503
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8.399328 7.2239175 -7.435854 2.9441683 -4.3430395
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-13.886965 -1.6346735 -10.9027405 -5.311245 3.8007221
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3.8976038 -2.1230774 -2.3521194 4.151031 -7.4048667
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0.13911647 2.4626107 4.9664545 0.9897574 5.4839754
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-3.3574002 10.1340065 -0.6120171 -10.403095 4.6007543
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16.00935 -7.7836914 -4.1945305 -6.9368606 1.1789556
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11.490801 4.2380238 9.550931 8.375046 7.5089145
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-0.65707296 -0.30051577 2.8406055 3.0828028 0.730817
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6.148354 0.13766119 -13.424735 -7.7461405 -2.3227983
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-8.305252 2.9879124 -10.995229 0.15211068 -2.3820348
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-1.7984174 8.495629 -5.8522367 -3.755498 0.6989711
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-5.2702994 -2.6188622 -1.8828466 -4.64665 14.078544
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-0.5495333 10.579158 -3.2160501 9.349004 -4.381078
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-11.675817 -2.8630207 4.5721755 2.246612 -4.574342
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1.8610188 2.3767874 5.6257877 -9.784078 0.64967257
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-1.4579505 0.4263264 -4.9211264 -2.454784 3.4869802
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-0.42654222 8.341269 1.356552 7.0966883 -13.102829
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8.016734 -7.1159344 1.8699781 0.208721 14.699384
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-1.025278 -2.6107233 -2.5082312 8.427193 6.9138527
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-6.2912464 0.6157366 2.489688 -3.4668267 9.921763
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11.200815 -0.1966403 7.4916005 -0.62312716 -0.25848144
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-9.947997 -0.9611041 1.1649219 -2.1907122 -1.5028487
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-0.51926106 15.165954 2.4649463 -0.9980445 7.4416637
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-2.0768049 3.5896823 -7.3055434 -7.5620847 4.323335
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0.0804418 -6.56401 -2.3148053 -1.7642345 -2.4708817
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-7.675618 -9.548878 -1.0177554 0.16986446 2.5877135
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-1.8752296 -0.36614323 -6.0493784 -2.3965611 -5.9453387
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0.9424033 -13.155974 -7.457801 0.14658108 -3.742797
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5.8414927 -1.2872906 5.5694313 12.57059 1.0939219
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2.2142086 1.9181576 6.9914207 -5.888139 3.1409824
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-2.003628 2.4434285 9.973139 5.03668 2.0051203
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2.8615603 5.860224 2.9176188 -1.6311141 2.0292206
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-4.070415 -6.831437 ]
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# get the test embedding
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Test embedding Result:
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[ 2.5247195 5.119042 -4.335273 4.4583654 5.047907
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3.5059214 1.6159848 0.49364898 -11.6899185 -3.1014526
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-5.6589785 -0.42684984 2.674276 -11.937654 6.2248464
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-10.776924 -5.694543 1.112041 1.5709964 1.0961034
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1.3976512 2.324352 1.339981 5.279319 13.734659
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-2.5753925 13.651442 -2.2357535 5.1575427 -3.251567
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1.4023279 6.1191974 -6.0845175 -1.3646189 -2.6789894
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-15.220778 9.779349 -9.411551 -6.388947 6.8313975
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-9.245996 0.31196198 2.5509644 -4.413065 6.1649427
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6.793837 2.6328635 8.620976 3.4832475 0.52491665
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2.9115407 5.8392377 0.6702376 -3.2726715 2.6694255
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16.91701 -5.5811176 0.23362345 -4.5573606 -11.801059
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14.728292 -0.5198082 -3.999922 7.0927105 -7.0459595
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-5.4389 -0.46420583 -5.1085467 10.376568 -8.889225
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-0.37705845 -1.659806 2.6731026 -7.1909504 1.4608804
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-2.163136 -0.17949677 4.0241547 0.11319201 0.601279
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2.039692 3.1910992 -11.649526 -8.121584 -4.8707457
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0.3851982 1.4231744 -2.3321972 0.99332285 14.121717
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5.899413 0.7384519 -17.760096 10.555021 4.1366534
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-0.3391071 -0.20792882 3.208204 0.8847948 -8.721497
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-6.432868 13.006379 4.8956 -9.155822 -1.9441519
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5.7815638 -2.066733 10.425042 -0.8802383 -2.4314315
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-9.869258 0.35095334 -5.3549943 2.1076174 -8.290468
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8.4433365 -4.689333 9.334139 -2.172678 -3.0250976
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8.394216 -3.2110903 -7.93868 2.3960824 -2.3213403
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-1.4963245 -3.476059 4.132903 -10.893354 4.362673
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-0.45456508 10.258634 -1.1655927 -6.7799754 0.22885278
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-4.399287 2.333433 -4.84745 -4.2752337 -1.3577863
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-1.0685898 9.505196 7.3062205 0.08708266 12.927811
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-9.57974 1.3936648 -1.9444873 5.776769 15.251903
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10.6118355 -1.4903594 -9.535318 -3.6553776 -1.6699586
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-0.5933151 7.600357 -4.8815503 -8.698617 -15.855757
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0.25632986 -7.2235737 0.9506656 0.7128582 -9.051738
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8.74869 -1.6426028 -6.5762258 2.506905 -6.7431564
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5.129912 -12.189555 -3.6435068 12.068113 -6.0059533
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-2.3535995 2.9014351 22.3082 -1.5563312 13.193291
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2.7583609 -7.468798 1.3407065 -4.599617 -6.2345777
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10.7689295 7.137627 5.099476 0.3473359 9.647881
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-2.0484571 -5.8549366 ]
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# get the score between enroll and test
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Eembeddings Score: 0.45332613587379456
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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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