声波识别如何创建

import pyaudio
import wave
import librosa
import numpy as np
from sklearn.svm import SVC

# 录制音频
def record_audio(filename, duration=5):
    chunk = 1024
    format = pyaudio.paInt16
    channels = 1
    rate = 44100
    record_seconds = duration
    output_filename = filename

    audio = pyaudio.PyAudio()
    stream = audio.open(format=format, channels=channels,
                        rate=rate, input=True,
                        frames_per_buffer=chunk)
    print("Recording...")
    frames = []
    for i in range(0, int(rate / chunk * record_seconds)):
        data = stream.read(chunk)
        frames.append(data)
    print("Recording finished.")
    stream.stop_stream()
    stream.close()
    audio.terminate()

    wf = wave.open(output_filename, 'wb')
    wf.setnchannels(channels)
    wf.setsampwidth(audio.get_sample_size(format))
    wf.setframerate(rate)
    wf.writeframes(b''.join(frames))
    wf.close()

# 提取特征
def extract_features(filename):
    y, sr = librosa.load(filename)
    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
    return np.mean(mfccs.T, axis=0)

# 训练模型
def train_model(features, labels):
    model = SVC(kernel='linear')
    model.fit(features, labels)
    return model

# 主程序
if __name__ == "__main__":
    # 假设我们有一些预先录制的音频文件和对应的标签
    audio_files = ['user1.wav', 'user2.wav']
    labels = ['user1', 'user2']

    features = []
    for file in audio_files:
        feature = extract_features(file)
        features.append(feature)

    model = train_model(features, labels)

    # 实时验证
    test_file = 'test_user1.wav'
    test_feature = extract_features(test_file)
    prediction = model.predict([test_feature])
    print(f"Predicted user: {prediction[0]}")