([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
```
Usage:
```bash
paddlespeech vector --help
```
Arguments:
- `input` (required): Audio file to recognize.
- `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 {'dim': 192, 'embedding': array([ -5.749211 , 9.505463 , -8.200284 , -5.2075014 ,
5.3940268 , -3.04878 , 1.611095 , 10.127234 ,
-10.534177 , -15.821609 , 1.2032688 , -0.35080156,
1.2629458 , -12.643498 , -2.5758228 , -11.343508 ,
2.3385992 , -8.719341 , 14.213509 , 15.404744 ,
-0.39327756, 6.338786 , 2.688887 , 8.7104025 ,
17.469526 , -8.77959 , 7.0576906 , 4.648855 ,
-1.3089896 , -23.294737 , 8.013747 , 13.891729 ,
-9.926753 , 5.655307 , -5.9422326 , -22.842539 ,
0.6293588 , -18.46266 , -10.811862 , 9.8192625 ,
3.0070958 , 3.8072643 , -2.3861165 , 3.0821571 ,
-14.739942 , 1.7594414 , -0.6485091 , 4.485623 ,
2.0207152 , 7.264915 , -6.40137 , 23.63524 ,
2.9711294 , -22.708025 , 9.93719 , 20.354511 ,
-10.324688 , -0.700492 , -8.783211 , -5.27593 ,
15.999649 , 3.3004563 , 12.747926 , 15.429879 ,
4.7849145 , 5.6699696 , -2.3826702 , 10.605882 ,
3.9112158 , 3.1500628 , 15.859915 , -2.1832209 ,
-23.908653 , -6.4799504 , -4.5365124 , -9.224193 ,
14.568347 , -10.568833 , 4.982321 , -4.342062 ,
0.0914714 , 12.645902 , -5.74285 , -3.2141201 ,
-2.7173362 , -6.680575 , 0.4757669 , -5.035051 ,
-6.7964664 , 16.865469 , -11.54324 , 7.681869 ,
0.44475392, 9.708182 , -8.932846 , 0.4123232 ,
-4.361452 , 1.3948607 , 9.511665 , 0.11667654,
2.9079323 , 6.049952 , 9.275183 , -18.078873 ,
6.2983274 , -0.7500531 , -2.725033 , -7.6027865 ,
3.3404543 , 2.990815 , 4.010979 , 11.000591 ,
-2.8873312 , 7.1352735 , -16.79663 , 18.495346 ,
-14.293832 , 7.89578 , 2.2714825 , 22.976387 ,
-4.875734 , -3.0836344 , -2.9999814 , 13.751918 ,
6.448228 , -11.924197 , 2.171869 , 2.0423572 ,
-6.173772 , 10.778437 , 25.77281 , -4.9495463 ,
14.57806 , 0.3044315 , 2.6132357 , -7.591999 ,
-2.076944 , 9.025118 , 1.7834753 , -3.1799617 ,
-4.9401326 , 23.465864 , 5.1685796 , -9.018578 ,
9.037825 , -4.4150195 , 6.859591 , -12.274467 ,
-0.88911164, 5.186309 , -3.9988663 , -13.638606 ,
-9.925445 , -0.06329413, -3.6709652 , -12.397416 ,
-12.719869 , -1.395601 , 2.1150916 , 5.7381287 ,
-4.4691963 , -3.82819 , -0.84233856, -1.1604277 ,
-13.490127 , 8.731719 , -20.778936 , -11.495662 ,
5.8033476 , -4.752041 , 10.833007 , -6.717991 ,
4.504732 , 13.4244375 , 1.1306485 , 7.3435574 ,
1.400918 , 14.704036 , -9.501399 , 7.2315617 ,
-6.417456 , 1.3333273 , 11.872697 , -0.30664724,
8.8845 , 6.5569253 , 4.7948146 , 0.03662816,
-8.704245 , 6.224871 , -3.2701402 , -11.508579 ],
dtype=float32)}
```
- 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,
ckpt_path=None,
audio_file='./85236145389.wav',
force_yes=False,
device=paddle.get_device())
print('Audio embedding Result: \n{}'.format(audio_emb))
```
Output:
```bash
# Vector Result:
{'dim': 192, 'embedding': array([ -5.749211 , 9.505463 , -8.200284 , -5.2075014 ,
5.3940268 , -3.04878 , 1.611095 , 10.127234 ,
-10.534177 , -15.821609 , 1.2032688 , -0.35080156,
1.2629458 , -12.643498 , -2.5758228 , -11.343508 ,
2.3385992 , -8.719341 , 14.213509 , 15.404744 ,
-0.39327756, 6.338786 , 2.688887 , 8.7104025 ,
17.469526 , -8.77959 , 7.0576906 , 4.648855 ,
-1.3089896 , -23.294737 , 8.013747 , 13.891729 ,
-9.926753 , 5.655307 , -5.9422326 , -22.842539 ,
0.6293588 , -18.46266 , -10.811862 , 9.8192625 ,
3.0070958 , 3.8072643 , -2.3861165 , 3.0821571 ,
-14.739942 , 1.7594414 , -0.6485091 , 4.485623 ,
2.0207152 , 7.264915 , -6.40137 , 23.63524 ,
2.9711294 , -22.708025 , 9.93719 , 20.354511 ,
-10.324688 , -0.700492 , -8.783211 , -5.27593 ,
15.999649 , 3.3004563 , 12.747926 , 15.429879 ,
4.7849145 , 5.6699696 , -2.3826702 , 10.605882 ,
3.9112158 , 3.1500628 , 15.859915 , -2.1832209 ,
-23.908653 , -6.4799504 , -4.5365124 , -9.224193 ,
14.568347 , -10.568833 , 4.982321 , -4.342062 ,
0.0914714 , 12.645902 , -5.74285 , -3.2141201 ,
-2.7173362 , -6.680575 , 0.4757669 , -5.035051 ,
-6.7964664 , 16.865469 , -11.54324 , 7.681869 ,
0.44475392, 9.708182 , -8.932846 , 0.4123232 ,
-4.361452 , 1.3948607 , 9.511665 , 0.11667654,
2.9079323 , 6.049952 , 9.275183 , -18.078873 ,
6.2983274 , -0.7500531 , -2.725033 , -7.6027865 ,
3.3404543 , 2.990815 , 4.010979 , 11.000591 ,
-2.8873312 , 7.1352735 , -16.79663 , 18.495346 ,
-14.293832 , 7.89578 , 2.2714825 , 22.976387 ,
-4.875734 , -3.0836344 , -2.9999814 , 13.751918 ,
6.448228 , -11.924197 , 2.171869 , 2.0423572 ,
-6.173772 , 10.778437 , 25.77281 , -4.9495463 ,
14.57806 , 0.3044315 , 2.6132357 , -7.591999 ,
-2.076944 , 9.025118 , 1.7834753 , -3.1799617 ,
-4.9401326 , 23.465864 , 5.1685796 , -9.018578 ,
9.037825 , -4.4150195 , 6.859591 , -12.274467 ,
-0.88911164, 5.186309 , -3.9988663 , -13.638606 ,
-9.925445 , -0.06329413, -3.6709652 , -12.397416 ,
-12.719869 , -1.395601 , 2.1150916 , 5.7381287 ,
-4.4691963 , -3.82819 , -0.84233856, -1.1604277 ,
-13.490127 , 8.731719 , -20.778936 , -11.495662 ,
5.8033476 , -4.752041 , 10.833007 , -6.717991 ,
4.504732 , 13.4244375 , 1.1306485 , 7.3435574 ,
1.400918 , 14.704036 , -9.501399 , 7.2315617 ,
-6.417456 , 1.3333273 , 11.872697 , -0.30664724,
8.8845 , 6.5569253 , 4.7948146 , 0.03662816,
-8.704245 , 6.224871 , -3.2701402 , -11.508579 ],
dtype=float32)}
```
### 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
