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 cli 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
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.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 [ -1.3251206    7.8606825   -4.620626     0.3000721    2.2648535
    -1.1931441    3.0647137    7.673595    -6.0044727  -12.02426
    -1.9496069    3.1269536    1.618838    -7.6383104   -1.2299773
  -12.338331     2.1373026   -5.3957124    9.717328     5.6752305
    3.7805123    3.0597172    3.429692     8.97601     13.174125
    -0.53132284   8.9424715    4.46511     -4.4262476   -9.726503
    8.399328     7.2239175   -7.435854     2.9441683   -4.3430395
  -13.886965    -1.6346735  -10.9027405   -5.311245     3.8007221
    3.8976038   -2.1230774   -2.3521194    4.151031    -7.4048667
    0.13911647   2.4626107    4.9664545    0.9897574    5.4839754
    -3.3574002   10.1340065   -0.6120171  -10.403095     4.6007543
    16.00935     -7.7836914   -4.1945305   -6.9368606    1.1789556
    11.490801     4.2380238    9.550931     8.375046     7.5089145
    -0.65707296  -0.30051577   2.8406055    3.0828028    0.730817
    6.148354     0.13766119 -13.424735    -7.7461405   -2.3227983
    -8.305252     2.9879124  -10.995229     0.15211068  -2.3820348
    -1.7984174    8.495629    -5.8522367   -3.755498     0.6989711
    -5.2702994   -2.6188622   -1.8828466   -4.64665     14.078544
    -0.5495333   10.579158    -3.2160501    9.349004    -4.381078
  -11.675817    -2.8630207    4.5721755    2.246612    -4.574342
    1.8610188    2.3767874    5.6257877   -9.784078     0.64967257
    -1.4579505    0.4263264   -4.9211264   -2.454784     3.4869802
    -0.42654222   8.341269     1.356552     7.0966883  -13.102829
    8.016734    -7.1159344    1.8699781    0.208721    14.699384
    -1.025278    -2.6107233   -2.5082312    8.427193     6.9138527
    -6.2912464    0.6157366    2.489688    -3.4668267    9.921763
    11.200815    -0.1966403    7.4916005   -0.62312716  -0.25848144
    -9.947997    -0.9611041    1.1649219   -2.1907122   -1.5028487
    -0.51926106  15.165954     2.4649463   -0.9980445    7.4416637
    -2.0768049    3.5896823   -7.3055434   -7.5620847    4.323335
    0.0804418   -6.56401     -2.3148053   -1.7642345   -2.4708817
    -7.675618    -9.548878    -1.0177554    0.16986446   2.5877135
    -1.8752296   -0.36614323  -6.0493784   -2.3965611   -5.9453387
    0.9424033  -13.155974    -7.457801     0.14658108  -3.742797
    5.8414927   -1.2872906    5.5694313   12.57059      1.0939219
    2.2142086    1.9181576    6.9914207   -5.888139     3.1409824
    -2.003628     2.4434285    9.973139     5.03668      2.0051203
    2.8615603    5.860224     2.9176188   -1.6311141    2.0292206
    -4.070415    -6.831437  ]
  ```

- Python API
  ```python
  from paddlespeech.cli.vector 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 range [0, 1]
  score = vector_executor.get_embeddings_score(audio_emb, test_emb)
  print(f"Eembeddings Score: {score}")
  ```

  Output：

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

### 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