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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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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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demo [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055
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1.756596 5.167894 10.80636 -3.8226728 -5.6141334
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2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897
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-9.723131 0.6619743 -6.976803 10.213478 7.494748
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2.9105635 3.8949256 3.7999806 7.1061673 16.905321
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-7.1493764 8.733103 3.4230042 -4.831653 -11.403367
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11.232214 7.1274667 -4.2828417 2.452362 -5.130748
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-18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683
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0.7618269 1.1253023 -2.083836 4.725744 -8.782597
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-3.539873 3.814236 5.1420674 2.162061 4.096431
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-6.4162116 12.747448 1.9429878 -15.152943 6.417416
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16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939
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11.567354 3.69788 11.258265 7.442363 9.183411
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4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783
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7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073
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-3.7776625 6.917234 -9.848748 -2.0944717 -5.135116
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0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578
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-7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651
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-4.473626 7.6624317 -0.55381083 9.631587 -6.4704556
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-8.548508 4.3716145 -0.79702514 4.478997 -2.9758704
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3.272176 2.8382776 5.134597 -9.190781 -0.5657382
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-4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576
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-0.31784213 9.493548 2.1144536 4.358092 -12.089823
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8.451689 -7.925461 4.6242585 4.4289427 18.692003
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-2.6204622 -5.149185 -0.35821092 8.488551 4.981496
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-9.32683 -2.2544234 6.6417594 1.2119585 10.977129
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16.555033 3.3238444 9.551863 -1.6676947 -0.79539716
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-8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796
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0.66607 15.443222 4.740594 -3.4725387 11.592567
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-2.054497 1.7361217 -8.265324 -9.30447 5.4068313
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-1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733
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-8.649895 -9.998958 -2.564841 -0.53999114 2.601808
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-0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085
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1.483641 -15.365992 -8.288208 3.8847756 -3.4876456
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7.3629923 0.4657332 3.132599 12.438889 -1.8337058
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4.532936 2.7264361 10.145339 -6.521951 2.897153
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-3.3925855 5.079156 7.759716 4.677565 5.8457737
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2.402413 7.7071047 3.9711342 -6.390043 6.1268735
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-3.7760346 -11.118123 ]
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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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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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[ 1.4217498 5.626253 -5.342073 1.1773866 3.308055
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1.756596 5.167894 10.80636 -3.8226728 -5.6141334
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2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897
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-9.723131 0.6619743 -6.976803 10.213478 7.494748
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2.9105635 3.8949256 3.7999806 7.1061673 16.905321
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-7.1493764 8.733103 3.4230042 -4.831653 -11.403367
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11.232214 7.1274667 -4.2828417 2.452362 -5.130748
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-18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683
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0.7618269 1.1253023 -2.083836 4.725744 -8.782597
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-3.539873 3.814236 5.1420674 2.162061 4.096431
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-6.4162116 12.747448 1.9429878 -15.152943 6.417416
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16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939
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11.567354 3.69788 11.258265 7.442363 9.183411
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4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783
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7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073
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-3.7776625 6.917234 -9.848748 -2.0944717 -5.135116
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0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578
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-7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651
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-4.473626 7.6624317 -0.55381083 9.631587 -6.4704556
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-8.548508 4.3716145 -0.79702514 4.478997 -2.9758704
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3.272176 2.8382776 5.134597 -9.190781 -0.5657382
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-4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576
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-0.31784213 9.493548 2.1144536 4.358092 -12.089823
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8.451689 -7.925461 4.6242585 4.4289427 18.692003
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-2.6204622 -5.149185 -0.35821092 8.488551 4.981496
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-9.32683 -2.2544234 6.6417594 1.2119585 10.977129
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16.555033 3.3238444 9.551863 -1.6676947 -0.79539716
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-8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796
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0.66607 15.443222 4.740594 -3.4725387 11.592567
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-2.054497 1.7361217 -8.265324 -9.30447 5.4068313
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-1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733
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-8.649895 -9.998958 -2.564841 -0.53999114 2.601808
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-0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085
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1.483641 -15.365992 -8.288208 3.8847756 -3.4876456
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7.3629923 0.4657332 3.132599 12.438889 -1.8337058
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4.532936 2.7264361 10.145339 -6.521951 2.897153
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-3.3925855 5.079156 7.759716 4.677565 5.8457737
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2.402413 7.7071047 3.9711342 -6.390043 6.1268735
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-3.7760346 -11.118123 ]
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# get the test embedding
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Test embedding Result:
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[ -1.902964 2.0690894 -8.034194 3.5472693 0.18089125
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6.9085927 1.4097427 -1.9487704 -10.021278 -0.20755845
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-8.04332 4.344489 2.3200977 -14.306299 5.184692
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-11.55602 -3.8497238 0.6444722 1.2833948 2.6766639
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0.5878921 0.7946299 1.7207596 2.5791872 14.998469
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-1.3385371 15.031221 -0.8006958 1.99287 -9.52007
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2.435466 4.003221 -4.33817 -4.898601 -5.304714
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-18.033886 10.790787 -12.784645 -5.641755 2.9761686
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-10.566622 1.4839455 6.152458 -5.7195854 2.8603241
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6.112133 8.489869 5.5958056 1.2836679 -1.2293907
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0.89927405 7.0288725 -2.854029 -0.9782962 5.8255906
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14.905906 -5.025907 0.7866458 -4.2444224 -16.354029
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10.521315 0.9604709 -3.3257897 7.144871 -13.592733
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-8.568869 -1.7953678 0.26313916 10.916714 -6.9374123
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1.857403 -6.2746415 2.8154466 -7.2338667 -2.293357
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-0.05452765 5.4287076 5.0849075 -6.690375 -1.6183422
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3.654291 0.94352573 -9.200294 -5.4749465 -3.5235846
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1.3420814 4.240421 -2.772944 -2.8451524 16.311104
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4.2969875 -1.762936 -12.5758915 8.595198 -0.8835239
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-1.5708797 1.568961 1.1413603 3.5032008 -0.45251232
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-6.786333 16.89443 5.3366146 -8.789056 0.6355629
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3.2579517 -3.328322 7.5969577 0.66025066 -6.550468
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-9.148656 2.020372 -0.4615173 1.1965656 -3.8764873
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11.6562195 -6.0750933 12.182899 3.2218833 0.81969476
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5.570001 -3.8459578 -7.205299 7.9262037 -7.6611166
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-5.249467 -2.2671914 7.2658715 -13.298164 4.821147
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-2.7263982 11.691089 -3.8918593 -2.838112 -1.0336838
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-3.8034165 2.8536487 -5.60398 -1.1972581 1.3455094
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-3.4903061 2.2408795 5.5010734 -3.970756 11.99696
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-7.8858757 0.43160373 -5.5059714 4.3426995 16.322706
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11.635366 0.72157705 -9.245714 -3.91465 -4.449838
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-1.5716927 7.713747 -2.2430465 -6.198303 -13.481864
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2.8156567 -5.7812386 5.1456156 2.7289324 -14.505571
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13.270688 3.448231 -7.0659585 4.5886116 -4.466099
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-0.296428 -11.463529 -2.6076477 14.110243 -6.9725137
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-1.9962958 2.7119343 19.391657 0.01961198 14.607133
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-1.6695905 -4.391516 1.3131028 -6.670972 -5.888604
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12.0612335 5.9285784 3.3715196 1.492534 10.723728
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-0.95514804 -12.085431 ]
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# get the score between enroll and test
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Eembeddings Score: 0.4292638301849365
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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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