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PaddleSpeech/demos/speaker_verification/README_cn.md

216 lines
11 KiB

(简体中文|[English](./README.md))
# 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](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install_cn.md)。
你可以从 easymediumhard 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:
```bash
# 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)
```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
```
使用方法:
```bash
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:
```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, # 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}")
```
输出:
```bash
# Vector Result:
Audio embedding 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 ]
# get the test embedding
Test embedding Result:
[ -1.9617152 4.2184057 -5.4289927 3.8006616 7.400566
12.844175 1.4330423 0.4860911 -15.927942 -13.081303
-4.585545 2.378477 5.5894523 -13.060747 18.578707
-9.107497 -9.904055 0.7032993 0.7945765 -1.4118854
-6.4434266 -2.7688267 5.4320455 2.9636188 23.857662
-4.797293 22.821133 -1.6718386 0.80379957 -10.28131
-1.0586771 5.840774 -11.794188 0.9715659 -10.794272
-9.9839325 11.916608 -19.614918 -7.38727 12.361765
-15.568076 3.796782 1.4648503 -9.617965 1.8912128
5.5519567 4.1027875 9.565811 1.6652825 -0.06557167
7.3765106 6.91407 -3.4179301 4.676896 2.4507313
21.415924 -1.5271066 0.7630236 -15.634208 -24.682417
12.035311 1.9669697 -13.733474 11.616938 -16.630692
-16.287516 -7.4265285 -6.4809394 5.4794173 -8.481719
2.0745668 -7.50969 1.8279544 -15.189501 -4.000386
-1.5209727 6.975059 4.518711 3.0962887 -6.8465433
1.3825562 7.6983547 -9.399815 -7.3269534 -2.6540608
1.3231711 5.0338726 -5.9562182 -10.437971 19.123528
12.213971 -2.8820174 -20.65914 15.071251 8.114322
-4.045127 7.5128584 -3.3306584 6.822803 -0.05004288
-4.4368496 18.926466 14.04377 -5.9657135 4.714744
10.24277 -3.848245 14.494125 5.3582125 -6.30404
-14.122616 2.1969411 -5.90989 9.3047 -8.431231
10.438023 -11.987487 20.954517 -4.279951 -0.3756797
13.041809 -6.051407 -10.529183 3.7894943 -1.6330183
6.743382 -0.19549051 7.315633 -19.438568 0.6115422
4.5697403 2.1208212 0.52282465 -6.9142766 -5.8893275
0.5135903 0.92921656 -3.0571883 -7.4849505 2.2382743
-3.0478394 0.08785366 6.810543 -5.1137877 15.182398
-6.9418297 -8.922732 -2.4528694 7.324874 19.77244
13.997188 -5.08692 -14.329076 -6.1807523 -1.8777156
-3.6879017 6.3892293 -3.78877 -13.009837 -16.838747
-4.1660237 -7.4346085 0.5579437 -2.8482168 -13.509024
9.329142 8.1292095 -8.064337 -4.002228 -18.78694
7.7969575 -13.585645 -5.8225474 15.266658 -8.57028
-7.449079 2.2094946 28.004955 -3.0901644 11.932054
-1.5897936 -4.826059 6.9232755 -11.169697 -5.235409
11.251503 2.105524 4.0860977 -0.5384147 19.023642
1.6203141 -10.608387 ]
# get the score between enroll and test
Eembeddings Score: 0.3965281546115875
```
### 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