用python生成一边录音一边播放一边显示MFCC画布PyQt5的多线程程序

请确认您所提供的信息是否完整，如果您需要生成实现该功能的代码，那么我需要更多的信息，例如：

1. 您想要录制的音频是从哪里获取的？
2. 您想要播放的音频是从哪里获取的？
3. 您想要显示的MFCC画布是如何生成的？
4. 您需要的多线程程序的具体功能是什么？

请提供更多信息以便我更好地为您解答问题。

相关问题

用python生成一边录音一边显示MFCC画布PyQt5的多线程程序

以下是一个用Python生成一边录音一边显示MFCC画布的PyQt5多线程程序的示例：

import sys
import numpy as np
import pyaudio
import wave
import librosa
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure

class AudioRecorder(QObject):
    finished = pyqtSignal()
    update_mfcc = pyqtSignal(np.ndarray)

    def __init__(self):
        super().__init__()
        self.chunk = 1024
        self.format = pyaudio.paInt16
        self.channels = 1
        self.rate = 44100
        self.frames = []
        self.p = pyaudio.PyAudio()
        self.stream = None
        self.is_recording = False

    def start_recording(self):
        self.is_recording = True
        self.stream = self.p.open(format=self.format, channels=self.channels,
                                  rate=self.rate, input=True,
                                  frames_per_buffer=self.chunk)

        while self.is_recording:
            data = self.stream.read(self.chunk)
            self.frames.append(data)
            samples = np.frombuffer(data, dtype=np.int16)
            mfcc = librosa.feature.mfcc(y=samples, sr=self.rate)
            self.update_mfcc.emit(mfcc)

        self.stream.stop_stream()
        self.stream.close()
        self.p.terminate()

        wf = wave.open("recording.wav", 'wb')
        wf.setnchannels(self.channels)
        wf.setsampwidth(self.p.get_sample_size(self.format))
        wf.setframerate(self.rate)
        wf.writeframes(b''.join(self.frames))
        wf.close()

        self.finished.emit()

    def stop_recording(self):
        self.is_recording = False

class MFCCCanvas(FigureCanvas):
    def __init__(self, parent=None, width=5, height=4, dpi=100):
        self.fig = Figure(figsize=(width, height), dpi=dpi)
        self.axes = self.fig.add_subplot(111)
        self.axes.set_title("MFCC")
        self.axes.set_xlabel("Time")
        self.axes.set_ylabel("MFCC Coefficients")
        super().__init__(self.fig)

    def update_figure(self, mfcc):
        self.axes.clear()
        librosa.display.specshow(mfcc, x_axis='time', ax=self.axes)
        self.draw()

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.setWindowTitle("Audio Recorder")
        self.setFixedSize(500, 500)

        self.record_button = QPushButton("Record", self)
        self.record_button.setGeometry(QRect(200, 400, 100, 50))
        self.record_button.clicked.connect(self.record_button_clicked)

        self.mfcc_canvas = MFCCCanvas(self)
        self.mfcc_canvas.setGeometry(QRect(50, 50, 400, 300))

        self.audio_recorder = AudioRecorder()
        self.audio_recorder.update_mfcc.connect(self.mfcc_canvas.update_figure)
        self.audio_recorder.finished.connect(self.record_finished)

        self.threadpool = QThreadPool()

    def record_button_clicked(self):
        if self.audio_recorder.is_recording:
            self.audio_recorder.stop_recording()
            self.record_button.setText("Record")
        else:
            self.record_button.setText("Stop")
            self.threadpool.start(self.audio_recorder.start_recording)

    def record_finished(self):
        QMessageBox.information(self, "Finished", "Recording has finished.")

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

这个程序使用PyAudio库来录制音频，每次录制一小段音频，然后计算其MFCC特征并将其显示在画布上。程序还包含一个按钮，用于开始和停止录音。当录音完成时，程序会将录制的音频保存到名为“recording.wav”的WAV文件中。程序还使用PyQt5的QThreadPool类来管理多个线程，以便在录制音频时不会阻塞用户界面。

用python生成一边录音一边显示MFCC画布的多线程程序

以下是用Python编写的一边录音一边显示MFCC画布的多线程程序：

import pyaudio
import numpy as np
import matplotlib.pyplot as plt
import librosa
import threading

# 设置录音参数
CHUNK = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100

# 创建PyAudio对象
p = pyaudio.PyAudio()

# 创建MFCC画布
fig, ax = plt.subplots()
im = ax.imshow(np.zeros((13, 50)), cmap='hot', interpolation='nearest', origin='lower')
fig.canvas.draw()

# 定义线程类
class RecordThread(threading.Thread):
    def __init__(self):
        threading.Thread.__init__(self)
        self.frames = []

    def run(self):
        stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK)
        while True:
            data = stream.read(CHUNK)
            self.frames.append(data)

    def get_frames(self):
        return self.frames

# 定义线程类
class MFCCThread(threading.Thread):
    def __init__(self, record_thread):
        threading.Thread.__init__(self)
        self.record_thread = record_thread

    def run(self):
        while True:
            frames = self.record_thread.get_frames()
            y = np.frombuffer(b''.join(frames), dtype=np.int16)
            mfcc = librosa.feature.mfcc(y=y, sr=RATE)
            im.set_data(mfcc)
            fig.canvas.draw()

# 创建录音线程和MFCC线程
record_thread = RecordThread()
mfcc_thread = MFCCThread(record_thread)

# 启动线程
record_thread.start()
mfcc_thread.start()

# 显示MFCC画布
plt.show()

# 停止录音和PyAudio对象
record_thread.join()
p.terminate()

该程序使用PyAudio库进行录音，并使用LibROSA库生成MFCC特征。程序创建两个线程，一个用于录音并获取音频数据，另一个用于生成MFCC特征并在Matplotlib图形上显示。程序使用Matplotlib库创建MFCC图形，并在循环中更新图形数据。最后，程序启动两个线程并显示MFCC图形。