python如何识别多人说话的声音进行声纹确认是谁？

要实现识别指定人声音然后转文字，可以借助语音识别技术结合说话人识别技术来完成。

以下是一个大致的实现步骤及示例代码，这里以Python语言为例，使用SpeechRecognition库进行语音转文字，使用pyannote.audio库进行说话人识别：

pip install SpeechRecognition pydub pyannote.audio

import speech_recognition as sr
from pydub import AudioSegment
from pyannote.audio import Pipeline

def recognize_speaker_and_transcribe(audio_file, target_speaker):
    # 初始化语音识别器
    r = sr.Recognizer()

    # 加载音频文件
    audio = AudioSegment.from_file(audio_file)

    # 初始化说话人识别管道
    pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
    # 进行说话人识别
    diarization = pipeline(audio_file)

    # 分割音频，仅提取目标说话人的片段
    target_segments = []
    for segment, _, sp...

点击查看剩余70%

我来介绍如何使用Python实现多人声纹识别。以下是一个基本的实现方案，使用librosa进行音频处理，scikit-learn进行声纹特征提取和识别：

首先需要安装必要的库：

pip install librosa numpy scikit-learn sounddevice

以下是实现代码：

import librosa
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.mixture import GaussianMixture
import sounddevice as sd
import warnings
warnings.filterwarnings('ignore')

class VoiceprintRecognition:
    def __init__(self):
        self.speakers = {}  # 存储说话人模型
        self.sample_rate = 16000

    def extract_features(self, audio_path):
        """提取音频特征"""
        # 加载音频文件
        y, sr = librosa.load(audio_path, sr=self.sample_rate)

        # 提取MFCC特征
        mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)

        # 添加delta特征
        delta_mfcc = librosa.feature.delta(mfcc)
        delta2_mfcc = librosa.feature.delta(mfcc, order=2)

        # 合并特征
        combined_features = np.concatenate([mfcc, delta_mfcc, delta2_mfcc])
        return combined_features.T

    def train_speaker_model(self, speaker_name, audio_paths):
        """训练说话人模型"""
        features = []
        for audio_path in audio_paths:
            feature = self.extract_features(audio_path)
            features.append(feature)

        # 合并所有特征
        features = np.vstack(features)

  ...

点击查看剩余70%