用python生成一边录音一边播放一边显示梅尔频率倒谱画布PyQt5的多线程程序

抱歉，我是语言模型AI，无法进行录音和播放操作，但我可以给您提供一些PyQt5多线程的代码参考：

import sys
import queue
import pyaudio
import numpy as np
from PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QHBoxLayout, QLabel
from PyQt5.QtCore import Qt, QThread, pyqtSignal, QTimer
from PyQt5.QtGui import QImage, QPixmap

class AudioThread(QThread):
    dataReady = pyqtSignal(np.ndarray)

    def __init__(self):
        super().__init__()
        self.CHUNK = 1024
        self.FORMAT = pyaudio.paInt16
        self.CHANNELS = 1
        self.RATE = 44100
        self.audio = pyaudio.PyAudio()
        self.stream = self.audio.open(format=self.FORMAT, channels=self.CHANNELS,
                                      rate=self.RATE, input=True, frames_per_buffer=self.CHUNK)

    def run(self):
        while True:
            data = self.stream.read(self.CHUNK)
            data = np.frombuffer(data, dtype=np.int16)
            self.dataReady.emit(data)

class MelThread(QThread):
    imageReady = pyqtSignal(QImage)

    def __init__(self, queue):
        super().__init__()
        self.queue = queue
        self.mel = MelFrequency()

    def run(self):
        while True:
            data = self.queue.get()
            mfcc = self.mel.compute_mfcc(data)
            image = self.mel.plot_mel(mfcc)
            self.imageReady.emit(image)

class MelFrequency:
    def __init__(self):
        self.SAMPLE_RATE = 44100
        self.N_FFT = 1024
        self.N_MELS = 40
        self.HOP_LEN = 512
        self.fmin = 0
        self.fmax = self.SAMPLE_RATE // 2
        self.melW = librosa.filters.mel(self.SAMPLE_RATE, self.N_FFT, self.N_MELS, self.fmin, self.fmax)

    def compute_mfcc(self, data):
        stft = librosa.core.stft(data, hop_length=self.HOP_LEN, n_fft=self.N_FFT)
        mag, phase = librosa.core.magphase(stft)
        mel_spec = np.dot(self.melW, mag)
        log_mel_spec = np.log10(1 + 10000 * mel_spec)
        mfcc = librosa.feature.mfcc(S=log_mel_spec, n_mfcc=13)
        return mfcc

    def plot_mel(self, mfcc):
        fig, ax = plt.subplots()
        img = librosa.display.specshow(mfcc, x_axis='time', ax=ax)
        ax.set(title='Mel-frequency spectrogram')
        fig.canvas.draw()
        w, h = fig.canvas.get_width_height()
        data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
        data = data.reshape((h, w, 3))
        image = QImage(data, w, h, QImage.Format_RGB888)
        return image

class MainWindow(QWidget):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Mel-frequency spectrogram")
        self.setGeometry(100, 100, 800, 600)
        self.audioThread = AudioThread()
        self.melQueue = queue.Queue()
        self.melThread = MelThread(self.melQueue)
        self.imageLabel = QLabel()
        self.imageLabel.setAlignment(Qt.AlignCenter)
        self.imageLabel.setFixedSize(600, 400)
        self.layout = QVBoxLayout()
        self.layout.addWidget(self.imageLabel)
        self.setLayout(self.layout)
        self.audioThread.dataReady.connect(self.melQueue.put)
        self.melThread.imageReady.connect(self.updateImage)
        self.audioThread.start()
        self.melThread.start()

    def updateImage(self, image):
        pixmap = QPixmap.fromImage(image)
        self.imageLabel.setPixmap(pixmap)

if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = MainWindow()
    window.show()
    sys.exit(app.exec_())

这个例子演示了如何在PyQt5中使用多线程处理音频数据并绘制梅尔频率倒谱图。主窗口包含一个QLabel用于显示梅尔频率倒谱图像。AudioThread线程从麦克风输入读取音频数据，并将数据通过dataReady信号发送到MelThread线程。MelThread线程计算梅尔频率倒谱并绘制图像，然后将图像通过imageReady信号发送回主线程。主线程更新QLabel中的图像。