我要的是生成一个语音情感识别的代码

对于语音情感识别的代码，可以使用Python和一些开源库来实现。以下是一个示例代码：

import librosa
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras import layers

# 加载数据集
df = pd.read_csv('data.csv')

# 预处理数据
def preprocess(df):
    X = []
    y = []
    for i in range(len(df)):
        try:
            # 加载音频文件
            file = df.loc[i]['file']
            y, sr = librosa.load(file)

            # 提取特征
            mfccs = np.mean(librosa.feature.mfcc(y, sr, n_mfcc=40).T, axis=0)
            chroma = np.mean(librosa.feature.chroma_stft(y, sr).T, axis=0)
            mel = np.mean(librosa.feature.melspectrogram(y, sr).T, axis=0)
            spectral_contrast = np.mean(librosa.feature.spectral_contrast(y, sr).T, axis=0)
            tonnetz = np.mean(librosa.feature.tonnetz(y, sr).T, axis=0)

            # 组合特征
            features = np.hstack([mfccs, chroma, mel, spectral_contrast, tonnetz])

            # 添加到X和y
            X.append(features)
            y.append(df.loc[i]['label'])
        except:
            pass
    X = np.array(X)
    y = np.array(y)

    return X, y

# 划分训练集和测试集
def split_dataset(X, y):
    X_train = X[:int(len(X) * 0.8)]
    y_train = y[:int(len(y) * 0.8)]
    X_test = X[int(len(X) * 0.8):]
    y_test = y[int(len(y) * 0.8):]

    return X_train, y_train, X_test, y_test

# 模型定义
def create_model():
    model = tf.keras.Sequential([
        layers.Dense(256, activation='relu', input_shape=(193,)),
        layers.Dense(128, activation='relu'),
        layers.Dense(64, activation='relu'),
        layers.Dense(4, activation='softmax')
    ])

    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

    return model

# 训练模型
def train_model(X_train, y_train, X_test, y_test):
    model = create_model()
    model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50, batch_size=32)

    return model

# 预测情感
def predict_emotion(model, file):
    y, sr = librosa.load(file)
    mfccs = np.mean(librosa.feature.mfcc(y, sr, n_mfcc=40).T, axis=0)
    chroma = np.mean(librosa.feature.chroma_stft(y, sr).T, axis=0)
    mel = np.mean(librosa.feature.melspectrogram(y, sr).T, axis=0)
    spectral_contrast = np.mean(librosa.feature.spectral_contrast(y, sr).T, axis=0)
    tonnetz = np.mean(librosa.feature.tonnetz(y, sr).T, axis=0)
    features = np.hstack([mfccs, chroma, mel, spectral_contrast, tonnetz])
    features = np.expand_dims(features, axis=0)
    prediction = model.predict(features)
    emotion = np.argmax(prediction)

    return emotion

# 运行代码
X, y = preprocess(df)
X_train, y_train, X_test, y_test = split_dataset(X, y)
model = train_model(X_train, y_train, X_test, y_test)
emotion = predict_emotion(model, 'test.wav')
print(emotion)

该代码中，使用了librosa库来处理音频文件，提取了40个MFCC特征、12个色度特征、128个梅尔频率倒谱系数、7个谱对比度特征和6个音调特征，组合成了193维特征。然后使用了一个全连接神经网络进行训练和预测。代码中的数据集文件data.csv包含了一些情感标注的音频文件路径和情感标签。最后，调用predict_emotion函数来预测一个新的音频文件的情感。