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PaddleSpeech/demos/speaker_verification/README_cn.md

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(简体中文|English)

声纹识别

介绍

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。

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。

使用方法

1. 安装

请看安装文档。

你可以从 easy，medium，hard 三中方式中选择一种方式安装。

2. 准备输入

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。

可以下载此 demo 的示例音频：

# 该音频的内容是数字串 85236145389
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav

3. 使用方法

  • 命令行 (推荐使用)

    paddlespeech vector --task spk --input 85236145389.wav

echo -e "demo1 85236145389.wav" > vec.job
paddlespeech vector --task spk --input vec.job

echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk

    使用方法：

    paddlespeech vector --help

    参数：

    • input(必须输入)：用于识别的音频文件。
    • task (必须输入): 用于指定 vector 处理的具体任务，默认是 spk。
    • model：声纹任务的模型，默认值：ecapatdnn_voxceleb12。
    • sample_rate：音频采样率，默认值：16000。
    • 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。
    • 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。
    • device：执行预测的设备，默认值：当前系统下 paddlepaddle 的默认 device。

    输出：

    demo  [  1.4217498    5.626253    -5.342073     1.1773866    3.308055
  1.756596     5.167894    10.80636     -3.8226728   -5.6141334
  2.623845    -0.8072968    1.9635103   -7.3128724    0.01103897
  -9.723131     0.6619743   -6.976803    10.213478     7.494748
  2.9105635    3.8949256    3.7999806    7.1061673   16.905321
  -7.1493764    8.733103     3.4230042   -4.831653   -11.403367
  11.232214     7.1274667   -4.2828417    2.452362    -5.130748
  -18.177666    -2.6116815  -11.000337    -6.7314315    1.6564683
  0.7618269    1.1253023   -2.083836     4.725744    -8.782597
  -3.539873     3.814236     5.1420674    2.162061     4.096431
  -6.4162116   12.747448     1.9429878  -15.152943     6.417416
  16.097002    -9.716668    -1.9920526   -3.3649497   -1.871939
  11.567354     3.69788     11.258265     7.442363     9.183411
  4.5281515   -1.2417862    4.3959084    6.6727695    5.8898783
  7.627124    -0.66919386 -11.889693    -9.208865    -7.4274073
  -3.7776625    6.917234    -9.848748    -2.0944717   -5.135116
  0.49563864   9.317534    -5.9141874   -1.8098574   -0.11738578
  -7.169265    -1.0578263   -5.7216787   -5.1173844   16.137651
  -4.473626     7.6624317   -0.55381083   9.631587    -6.4704556
  -8.548508     4.3716145   -0.79702514   4.478997    -2.9758704
  3.272176     2.8382776    5.134597    -9.190781    -0.5657382
  -4.8745747    2.3165567   -5.984303    -2.1798875    0.35541576
  -0.31784213   9.493548     2.1144536    4.358092   -12.089823
  8.451689    -7.925461     4.6242585    4.4289427   18.692003
  -2.6204622   -5.149185    -0.35821092   8.488551     4.981496
  -9.32683     -2.2544234    6.6417594    1.2119585   10.977129
  16.555033     3.3238444    9.551863    -1.6676947   -0.79539716
  -8.605674    -0.47356385   2.6741948   -5.359179    -2.6673796
  0.66607     15.443222     4.740594    -3.4725387   11.592567
  -2.054497     1.7361217   -8.265324    -9.30447      5.4068313
  -1.5180256   -7.746615    -6.089606     0.07112726  -0.34904733
  -8.649895    -9.998958    -2.564841    -0.53999114   2.601808
  -0.31927416  -1.8815292   -2.07215     -3.4105783   -8.2998085
  1.483641   -15.365992    -8.288208     3.8847756   -3.4876456
  7.3629923    0.4657332    3.132599    12.438889    -1.8337058
  4.532936     2.7264361   10.145339    -6.521951     2.897153
  -3.3925855    5.079156     7.759716     4.677565     5.8457737
  2.402413     7.7071047    3.9711342   -6.390043     6.1268735
  -3.7760346  -11.118123  ]

  • Python API

    import paddle
from paddlespeech.cli import VectorExecutor

vector_executor = VectorExecutor()
audio_emb = vector_executor(
    model='ecapatdnn_voxceleb12',
    sample_rate=16000,
    config=None,  # Set `config` and `ckpt_path` to None to use pretrained model.
    ckpt_path=None,
    audio_file='./85236145389.wav',
    device=paddle.get_device())
print('Audio embedding Result: \n{}'.format(audio_emb))

    输出：

    # Vector Result:
 Audio embedding Result:
  [  1.4217498    5.626253    -5.342073     1.1773866    3.308055
  1.756596     5.167894    10.80636     -3.8226728   -5.6141334
  2.623845    -0.8072968    1.9635103   -7.3128724    0.01103897
  -9.723131     0.6619743   -6.976803    10.213478     7.494748
  2.9105635    3.8949256    3.7999806    7.1061673   16.905321
  -7.1493764    8.733103     3.4230042   -4.831653   -11.403367
  11.232214     7.1274667   -4.2828417    2.452362    -5.130748
  -18.177666    -2.6116815  -11.000337    -6.7314315    1.6564683
  0.7618269    1.1253023   -2.083836     4.725744    -8.782597
  -3.539873     3.814236     5.1420674    2.162061     4.096431
  -6.4162116   12.747448     1.9429878  -15.152943     6.417416
  16.097002    -9.716668    -1.9920526   -3.3649497   -1.871939
  11.567354     3.69788     11.258265     7.442363     9.183411
  4.5281515   -1.2417862    4.3959084    6.6727695    5.8898783
  7.627124    -0.66919386 -11.889693    -9.208865    -7.4274073
  -3.7776625    6.917234    -9.848748    -2.0944717   -5.135116
  0.49563864   9.317534    -5.9141874   -1.8098574   -0.11738578
  -7.169265    -1.0578263   -5.7216787   -5.1173844   16.137651
  -4.473626     7.6624317   -0.55381083   9.631587    -6.4704556
  -8.548508     4.3716145   -0.79702514   4.478997    -2.9758704
  3.272176     2.8382776    5.134597    -9.190781    -0.5657382
  -4.8745747    2.3165567   -5.984303    -2.1798875    0.35541576
  -0.31784213   9.493548     2.1144536    4.358092   -12.089823
  8.451689    -7.925461     4.6242585    4.4289427   18.692003
  -2.6204622   -5.149185    -0.35821092   8.488551     4.981496
  -9.32683     -2.2544234    6.6417594    1.2119585   10.977129
  16.555033     3.3238444    9.551863    -1.6676947   -0.79539716
  -8.605674    -0.47356385   2.6741948   -5.359179    -2.6673796
  0.66607     15.443222     4.740594    -3.4725387   11.592567
  -2.054497     1.7361217   -8.265324    -9.30447      5.4068313
  -1.5180256   -7.746615    -6.089606     0.07112726  -0.34904733
  -8.649895    -9.998958    -2.564841    -0.53999114   2.601808
  -0.31927416  -1.8815292   -2.07215     -3.4105783   -8.2998085
  1.483641   -15.365992    -8.288208     3.8847756   -3.4876456
  7.3629923    0.4657332    3.132599    12.438889    -1.8337058
  4.532936     2.7264361   10.145339    -6.521951     2.897153
  -3.3925855    5.079156     7.759716     4.677565     5.8457737
  2.402413     7.7071047    3.9711342   -6.390043     6.1268735
  -3.7760346  -11.118123  ]

4.预训练模型

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：

模型 采样率
ecapatdnn_voxceleb12 16k