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https://github.com/liuxp0827/govpr
gmm-ubm voiceprint recognition by golang
https://github.com/liuxp0827/govpr
gmm-ubm golang voiceprint
Last synced: 3 months ago
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gmm-ubm voiceprint recognition by golang
- Host: GitHub
- URL: https://github.com/liuxp0827/govpr
- Owner: liuxp0827
- License: apache-2.0
- Created: 2016-09-02T01:56:35.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2020-03-31T11:48:36.000Z (over 4 years ago)
- Last Synced: 2024-07-16T13:09:16.785Z (4 months ago)
- Topics: gmm-ubm, golang, voiceprint
- Language: Go
- Homepage:
- Size: 1020 KB
- Stars: 303
- Watchers: 26
- Forks: 81
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 声纹识别
来自于阿里聚安全对声纹识别的介绍:[探秘身份认证利器——声纹识别](https://jaq.alibaba.com/community/art/show?spm=a313e.7916648.0.0.WwucQ3&articleid=661)
## 简介
govpr是golang 实现的基于 GMM-UBM 说话人识别引擎(声纹识别),可用于语音验证,身份识别的场景.
目前暂时仅支持汉语数字的语音,语音格式为wav格式(比特率16000,16bits,单声道)
## 安装
go get -v -u github.com/liuxp0827/govpr
cd $GOPATH/src/github.com/liuxp0827/govpr/example
go run main.go
## 示例
如下是一个简单的示例. 可跳转至 [example](https://github.com/liuxp0827/govpr/blob/master/example)
查看详细的例子,示例中的语音为纯数字8位数字.语音验证后得到一个得分,可设置阈值来判断验证语音是否为注册训练者本人.
示例中,预设阈值1.0,语音验证得分>=1.0,可认定为是本人语音,语音验证得分<1.0则非本人语音.
(注:阈值设为1.0并非最优值,仅是给出一个示例.另女性声纹得分相对较低,理论上应对不同性别给出不同阈值等级,govpr暂未实现通过声音分辨性别,后续会开发该功能)
## 注意
示例中,使用了五组完全不同的语音内容进行训练和验证,但实际上 govpr 更适合于文本相关的说话人识别,采用五组训练语音和验证语音内容相同的语音数据,可得到更好的识别效果.
```go
package main
import (
"github.com/liuxp0827/govpr"
"github.com/liuxp0827/govpr/log"
"github.com/liuxp0827/govpr/waveIO"
"io/ioutil"
)
type engine struct {
vprEngine *govpr.VPREngine
}
func NewEngine(sampleRate, delSilRange int, ubmFile, userModelFile string) (*engine, error) {
vprEngine, err := govpr.NewVPREngine(sampleRate, delSilRange, false, ubmFile, userModelFile)
if err != nil {
return nil, err
}
return &engine{vprEngine: vprEngine}, nil
}
func (this *engine) DestroyEngine() {
this.vprEngine = nil
}
func (this *engine) TrainSpeech(buffers [][]byte) error {
var err error
count := len(buffers)
for i := 0; i < count; i++ {
err = this.vprEngine.AddTrainBuffer(buffers[i])
if err != nil {
log.Error(err)
return err
}
}
defer this.vprEngine.ClearTrainBuffer()
defer this.vprEngine.ClearAllBuffer()
err = this.vprEngine.TrainModel()
if err != nil {
log.Error(err)
return err
}
return nil
}
func (this *engine) RecSpeech(buffer []byte) (float64, error) {
err := this.vprEngine.AddVerifyBuffer(buffer)
defer this.vprEngine.ClearVerifyBuffer()
if err != nil {
log.Error(err)
return -1.0, err
}
err = this.vprEngine.VerifyModel()
if err != nil {
log.Error(err)
return -1.0, err
}
return this.vprEngine.GetScore(), nil
}
func main() {
log.SetLevel(log.LevelDebug)
vprEngine, err := NewEngine(16000, 50, "../ubm/ubm", "model/test.dat")
if err != nil {
log.Fatal(err)
}
trainlist := []string{
"wav/train/01_32468975.wav",
"wav/train/02_58769423.wav",
"wav/train/03_59682734.wav",
"wav/train/04_64958273.wav",
"wav/train/05_65432978.wav",
}
trainBuffer := make([][]byte, 0)
for _, file := range trainlist {
buf, err := loadWaveData(file)
if err != nil {
log.Error(err)
return
}
trainBuffer = append(trainBuffer, buf)
}
err = vprEngine.TrainSpeech(trainBuffer)
if err != nil {
log.Fatal(err)
}
var threshold float64 = 1.0
selfverifyBuffer, err := waveIO.WaveLoad("wav/verify/self_34986527.wav")
if err != nil {
log.Fatal(err)
}
self_score, err := vprEngine.RecSpeech(selfverifyBuffer)
if err != nil {
log.Fatal(err)
}
log.Infof("self score %f, pass? %v", self_score, self_score >= threshold)
otherverifyBuffer, err := waveIO.WaveLoad("wav/verify/other_38974652.wav")
if err != nil {
log.Fatal(err)
}
other_score, err := vprEngine.RecSpeech(otherverifyBuffer)
if err != nil {
log.Fatal(err)
}
log.Infof("other score %f, pass? %v", other_score, other_score >= threshold)
}
func loadWaveData(file string) ([]byte, error) {
data, err := ioutil.ReadFile(file)
if err != nil {
return nil, err
}
// remove .wav header info 44 bits
data = data[44:]
return data, nil
}
```