使用python写一个说话人识别的代码

以下是一个基本的说话人识别代码，使用Python和Librosa库来提取声音特征和分类：

import librosa
import numpy as np
import os
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier

# 获取音频文件的MFCC特征
def extract_features(file_name):
    try:
        audio_data, sample_rate = librosa.load(file_name, res_type='kaiser_fast')
        mfccs = librosa.feature.mfcc(y=audio_data, sr=sample_rate, n_mfcc=20)
        mfccs_scaled = np.mean(mfccs.T, axis=0)
    except Exception as e:
        print("Error encountered while parsing file: ", file_name)
        return None
    return mfccs_scaled

# 加载数据集和标签，返回训练集和测试集
def load_data(test_size=0.2):
    x, y = [], []
    for file in os.listdir("audio_files"):
        if file.endswith(".wav"):
            file_path = os.path.join("audio_files", file)
            feature = extract_features(file_path)
            if feature is not None:
                x.append(feature)
                y.append(file[0:2])
    le = preprocessing.LabelEncoder()
    y = le.fit_transform(y)
    return train_test_split(np.array(x), y, test_size=test_size, random_state=42)

# 训练MLP分类器
def train_model(x_train, y_train):
    clf = MLPClassifier(hidden_layer_sizes=(500,), max_iter=1000, alpha=0.0001, solver='adam', verbose=10,  random_state=21,tol=0.000000001)
    clf.fit(x_train, y_train)
    return clf

# 测试分类器
def test_model(clf, x_test, y_test):
    score = clf.score(x_test, y_test)
    print("Accuracy:", score)

# 加载数据集
x_train, x_test, y_train, y_test = load_data()

# 训练模型
clf = train_model(x_train, y_train)

# 测试模型
test_model(clf, x_test, y_test)

这个代码需要将所有的音频文件都放在同一个文件夹（“audio_files”）中，并以说话人的名字（例如，“Alice.wav”）命名。最后，这个代码会输出分类器在测试集上的准确率。