README.md

([简体中文](./README_cn.md)|English)
# Speech Verification)

## Introduction

Speaker Verification, refers to the problem of getting a speaker embedding from an audio. 

This demo is an implementation to extract speaker embedding from a specific audio file. It can be done by a single command or a few lines in python using `PaddleSpeech`. 

## Usage
### 1. Installation
see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).

You can choose one way from easy, meduim and hard to install paddlespeech.

### 2. Prepare Input File
The input of this demo should be a WAV file(`.wav`), and the sample rate must be the same as the model.

Here are sample files for this demo that can be downloaded:
```bash
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
```

### 3. Usage
- Command Line(Recommended)
  ```bash
  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

  paddlespeech vector --task score --input "./85236145389.wav ./123456789.wav"
  
  echo -e "demo4 85236145389.wav 85236145389.wav \n demo5 85236145389.wav 123456789.wav" > vec.job
  paddlespeech vector --task score --input vec.job
  ```
  
  Usage:
  ```bash
  paddlespeech vector --help
  ```
  Arguments:
  - `input`(required): Audio file to recognize.
  - `task` (required): Specify `vector` task. Default `spk`。
  - `model`: Model type of vector task. Default: `ecapatdnn_voxceleb12`.
  - `sample_rate`: Sample rate of the model. Default: `16000`.
  - `config`: Config of vector task. Use pretrained model when it is None. Default: `None`.
  - `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`.
  - `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.

  Output:

  ```bash
    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
  ```python
  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))

  test_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='./123456789.wav',
      device=paddle.get_device())
  print('Test embedding Result: \n{}'.format(test_emb))
  score = vector_executor.get_embeddings_score(audio_emb, test_emb)
  print(f"Eembeddings Score: {score}")
  ```

  Output：

  ```bash
  # 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  ]
    # get the test embedding
    Test embedding Result:
    [ -1.902964     2.0690894   -8.034194     3.5472693    0.18089125
      6.9085927    1.4097427   -1.9487704  -10.021278    -0.20755845
      -8.04332      4.344489     2.3200977  -14.306299     5.184692
    -11.55602     -3.8497238    0.6444722    1.2833948    2.6766639
      0.5878921    0.7946299    1.7207596    2.5791872   14.998469
      -1.3385371   15.031221    -0.8006958    1.99287     -9.52007
      2.435466     4.003221    -4.33817     -4.898601    -5.304714
    -18.033886    10.790787   -12.784645    -5.641755     2.9761686
    -10.566622     1.4839455    6.152458    -5.7195854    2.8603241
      6.112133     8.489869     5.5958056    1.2836679   -1.2293907
      0.89927405   7.0288725   -2.854029    -0.9782962    5.8255906
      14.905906    -5.025907     0.7866458   -4.2444224  -16.354029
      10.521315     0.9604709   -3.3257897    7.144871   -13.592733
      -8.568869    -1.7953678    0.26313916  10.916714    -6.9374123
      1.857403    -6.2746415    2.8154466   -7.2338667   -2.293357
      -0.05452765   5.4287076    5.0849075   -6.690375    -1.6183422
      3.654291     0.94352573  -9.200294    -5.4749465   -3.5235846
      1.3420814    4.240421    -2.772944    -2.8451524   16.311104
      4.2969875   -1.762936   -12.5758915    8.595198    -0.8835239
      -1.5708797    1.568961     1.1413603    3.5032008   -0.45251232
      -6.786333    16.89443      5.3366146   -8.789056     0.6355629
      3.2579517   -3.328322     7.5969577    0.66025066  -6.550468
      -9.148656     2.020372    -0.4615173    1.1965656   -3.8764873
      11.6562195   -6.0750933   12.182899     3.2218833    0.81969476
      5.570001    -3.8459578   -7.205299     7.9262037   -7.6611166
      -5.249467    -2.2671914    7.2658715  -13.298164     4.821147
      -2.7263982   11.691089    -3.8918593   -2.838112    -1.0336838
      -3.8034165    2.8536487   -5.60398     -1.1972581    1.3455094
      -3.4903061    2.2408795    5.5010734   -3.970756    11.99696
      -7.8858757    0.43160373  -5.5059714    4.3426995   16.322706
      11.635366     0.72157705  -9.245714    -3.91465     -4.449838
      -1.5716927    7.713747    -2.2430465   -6.198303   -13.481864
      2.8156567   -5.7812386    5.1456156    2.7289324  -14.505571
      13.270688     3.448231    -7.0659585    4.5886116   -4.466099
      -0.296428   -11.463529    -2.6076477   14.110243    -6.9725137
      -1.9962958    2.7119343   19.391657     0.01961198  14.607133
      -1.6695905   -4.391516     1.3131028   -6.670972    -5.888604
      12.0612335    5.9285784    3.3715196    1.492534    10.723728
      -0.95514804 -12.085431  ]
    # get the score between enroll and test
    Eembeddings Score: 0.4292638301849365
  ```

### 4.Pretrained Models

Here is a list of pretrained models released by PaddleSpeech that can be used by command and python API:

| Model | Sample Rate
| :--- | :---: |
| ecapatdnn_voxceleb12 | 16k