([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 asr --help
```
Arguments:
- `input` (required): Audio file to recognize.
- `model` : Model type of asr task. Default: `conformer_wenetspeech` .
- `sample_rate` : Sample rate of the model. Default: `16000` .
- `config` : Config of asr 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 [ -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 ]
```
- 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:
[ -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 ]
```
### 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
