GUI frame
This commit is contained in:
Mikkeli Matlock
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f:/ncmr/vocaloid/randomcovers/zorra/zorra_release1.mp3
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f:/ncmr/vocaloid/randomcovers/zorra/zorra_release2.mp3
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f:/ncmr/vocaloid/randomcovers/zorra/zorra_release4.mp3
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f:/ncmr/vocaloid/randomcovers/silti/silti_release1.mp3
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f:/ncmr/vocaloid/northwichcase/transparency/rover_release12.mp3
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I:/musiikki/[160518] THE IDOLM@STER CINDERELLA GIRLS STARLIGHT MASTER 02 Tulip [320K]/01. Tulip (M@STER VERSION).mp3
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I:/musiikki/556t - MELTING POT/01. ココロ.mp3
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@@ -1,128 +1,40 @@
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import librosa
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import numpy as np
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import os
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import sys
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from PyQt5.QtWidgets import QApplication, QWidget, QVBoxLayout, QLabel
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from PyQt5.QtCore import Qt
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import matplotlib.pyplot as plt
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import matplotlib.colors as mcolors
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import matplotlib.cm as cm
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from mutagen.mp3 import MP3
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from mutagen.easyid3 import EasyID3
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def try_mp3_tags(file_path):
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try:
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# if there is metadata
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audio = MP3(file_path, ID3=EasyID3)
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return audio
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except Exception as e:
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print(f"Error reading ID3 tags: {e}")
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return None
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def read_mp3_tags(file_path):
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if (audio := try_mp3_tags(file_path)) is not None:
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print(f"File name: {os.path.basename(file_path)}")
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print(f"{audio['artist'][0]} - {audio['title'][0]}")
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else:
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print(f"File name: {os.path.basename(file_path)}")
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def analyze_track_librosa(file_path):
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# Load the audio file
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# y is the audio time series and sr is the sampling rate
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y, sr = librosa.load(file_path)
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# Calculate the maximum amplitude
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# Librosa's load function normalizes the audio to [-1, 1], so we scale it back
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max_amplitude = np.max(np.abs(y))
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# Average amplitude
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avg_amplitude = np.mean(np.abs(y))
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# Convert max amplitude to dBFS
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max_amplitude_dBFS = librosa.amplitude_to_db([max_amplitude], ref=1.0)
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avg_amplitude_dBFS = librosa.amplitude_to_db([avg_amplitude], ref=1.0)
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# Calculate RMS in dB
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S, phase = librosa.magphase(librosa.stft(y))
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rms_stft = librosa.feature.rms(S=S)
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rms = librosa.feature.rms(y=y)
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avg_power_dBFS_stft = 20 * np.log10(np.mean(rms_stft))
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avg_power_dBFS = 20 * np.log10(np.mean(rms))
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return max_amplitude_dBFS[0], avg_amplitude_dBFS[0], avg_power_dBFS, avg_power_dBFS_stft
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def plot_macro_time_power_graph(file_path):
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# Load the audio file
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y, sr = librosa.load(file_path, mono=True)
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# Define the window and hop length
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# 10 seconds window and 1 second hop
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window_length = int(sr * 10) # 10 seconds in samples
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hop_length = int(sr * 1) # 1 second in samples
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# Calculate RMS over the rolling windows
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rms = librosa.feature.rms(y=y, frame_length=window_length, hop_length=hop_length)
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# Convert frame indices to time
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times = librosa.frames_to_time(np.arange(rms.shape[1]), sr=sr, hop_length=hop_length)
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# Normalize RMS for color mapping
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norm = mcolors.Normalize(vmin=0, vmax=0.4)
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# Choose a colormap
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cmap = cm.autumn
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# Plot
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fig, ax = plt.subplots(figsize=(10, 4))
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ax.set_ylim(0., 0.4)
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for i in range(len(times)-1):
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ax.fill_between(times[i:i+2], 0, rms[0][i], color=cmap(norm(rms[0][i])), edgecolor='none')
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class AudioDragDropWidget(QWidget):
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def __init__(self):
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super().__init__()
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self.initUI()
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# Adding a colorbar to indicate the scale of RMS values
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sm = cm.ScalarMappable(cmap=cmap, norm=norm)
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sm.set_array([])
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cbar = plt.colorbar(sm, ax=ax, label='RMS Power')
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# cbar.ax.set_yticklabels([f"{x-60.0:.0f} dBFS" for x in cbar.get_ticks()]) # Adjust labels to show true dBFS values
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def initUI(self):
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self.setWindowTitle('Drag and Drop Audio Analysis')
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self.setGeometry(100, 100, 400, 200) # x, y, width, height
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self.setAcceptDrops(True)
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# Layout and label for displaying messages
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layout = QVBoxLayout()
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self.label = QLabel('Drag and drop an audio file here', self)
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self.label.setAlignment(Qt.AlignCenter)
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layout.addWidget(self.label)
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self.setLayout(layout)
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ax.set_ylabel('Power')
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ax.set_xlabel('Time')
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ax.set_title(f'{os.path.basename(file_path)}')
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# plt.ylabel('Power')
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# plt.xlabel('Time (s)')
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# plt.title(f'{os.path.basename(file_path)}')
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plt.show(block=False)
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plt.pause(0.001)
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def dragEnterEvent(self, event):
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if event.mimeData().hasUrls():
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event.accept()
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else:
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event.ignore()
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def dropEvent(self, event):
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files = [u.toLocalFile() for u in event.mimeData().urls()]
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for file_path in files:
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self.label.setText(f'File dropped: {file_path}')
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# Here, you would call your plot function with the dropped file path
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# For example: plot_macro_time_power_graph_colormap(file_path)
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break # This example only processes the first dropped file
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def find_mp3_files(directory):
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mp3_files = []
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# Walk through the directory
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for root, dirs, files in os.walk(directory):
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# Filter and append .mp3 files
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for file in files:
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if file.endswith(".mp3"):
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mp3_files.append(os.path.join(root, file))
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return mp3_files
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# Replace 'path/to/your/audiofile.mp3' with the path to your audio file
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file_path = []
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# file_path.append('f:/ncmr/vocaloid/randomcovers/zorra/zorra_release1.mp3')
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# file_path.append('f:/ncmr/vocaloid/randomcovers/zorra/zorra_release2.mp3')
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file_path.append('f:/ncmr/vocaloid/randomcovers/zorra/zorra_release4.mp3')
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# file_path.append('f:/ncmr/vocaloid/randomcovers/silti/silti_release1.mp3')
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# file_path.append('f:/ncmr/vocaloid/northwichcase/transparency/rover_release12.mp3')
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file_path.append('I:/musiikki/[160518] THE IDOLM@STER CINDERELLA GIRLS STARLIGHT MASTER 02 Tulip [320K]/01. Tulip (M@STER VERSION).mp3')
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file_path.append('I:/musiikki/556t - MELTING POT/01. ココロ.mp3')
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for file in file_path:
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max_amplitude, avg_amplitude, avg_power, avg_power_stft = analyze_track_librosa(file)
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read_mp3_tags(file)
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print(f"Maximum Amplitude: {max_amplitude:.2f} dBFS")
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print(f"Average Amplitude: {avg_amplitude:.2f} dBFS")
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print(f"Average Power: {avg_power:.2f} dBFS")
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print(f"Average Power (STFT): {avg_power_stft:.2f} dBFS")
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plot_macro_time_power_graph(file)
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plt.show()
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if __name__ == '__main__':
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app = QApplication(sys.argv)
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ex = AudioDragDropWidget()
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ex.show()
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sys.exit(app.exec_())
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+123
@@ -0,0 +1,123 @@
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import librosa
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import numpy as np
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import os
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import matplotlib.pyplot as plt
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import matplotlib.colors as mcolors
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import matplotlib.cm as cm
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from mutagen.mp3 import MP3
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from mutagen.easyid3 import EasyID3
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def try_mp3_tags(file_path):
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try:
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# if there is metadata
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audio = MP3(file_path, ID3=EasyID3)
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return audio
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except Exception as e:
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print(f"Error reading ID3 tags: {e}")
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return None
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def read_mp3_tags(file_path):
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if (audio := try_mp3_tags(file_path)) is not None:
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print(f"File name: {os.path.basename(file_path)}")
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print(f"{audio['artist'][0]} - {audio['title'][0]}")
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else:
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print(f"File name: {os.path.basename(file_path)}")
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def analyze_track_librosa(file_path):
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# Load the audio file
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# y is the audio time series and sr is the sampling rate
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y, sr = librosa.load(file_path)
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# Calculate the maximum amplitude
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# Librosa's load function normalizes the audio to [-1, 1], so we scale it back
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max_amplitude = np.max(np.abs(y))
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# Average amplitude
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avg_amplitude = np.mean(np.abs(y))
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# Convert max amplitude to dBFS
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max_amplitude_dBFS = librosa.amplitude_to_db([max_amplitude], ref=1.0)
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avg_amplitude_dBFS = librosa.amplitude_to_db([avg_amplitude], ref=1.0)
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# Calculate RMS in dB
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S, phase = librosa.magphase(librosa.stft(y))
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rms_stft = librosa.feature.rms(S=S)
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rms = librosa.feature.rms(y=y)
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avg_power_dBFS_stft = 20 * np.log10(np.mean(rms_stft))
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avg_power_dBFS = 20 * np.log10(np.mean(rms))
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return max_amplitude_dBFS[0], avg_amplitude_dBFS[0], avg_power_dBFS, avg_power_dBFS_stft
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def plot_macro_time_power_graph(file_path):
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# Load the audio file
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y, sr = librosa.load(file_path, mono=True)
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# Define the window and hop length
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# 10 seconds window and 1 second hop
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window_length = int(sr * 10) # 10 seconds in samples
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hop_length = int(sr * 1) # 1 second in samples
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# Calculate RMS over the rolling windows
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rms = librosa.feature.rms(y=y, frame_length=window_length, hop_length=hop_length)
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# Convert frame indices to time
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times = librosa.frames_to_time(np.arange(rms.shape[1]), sr=sr, hop_length=hop_length)
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# Normalize RMS for color mapping
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norm = mcolors.Normalize(vmin=0, vmax=0.4)
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# Choose a colormap
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cmap = cm.autumn
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# Plot
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fig, ax = plt.subplots(figsize=(10, 4))
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ax.set_ylim(0., 0.4)
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for i in range(len(times)-1):
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ax.fill_between(times[i:i+2], 0, rms[0][i], color=cmap(norm(rms[0][i])), edgecolor='none')
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# Adding a colorbar to indicate the scale of RMS values
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sm = cm.ScalarMappable(cmap=cmap, norm=norm)
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sm.set_array([])
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cbar = plt.colorbar(sm, ax=ax, label='RMS Power')
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# cbar.ax.set_yticklabels([f"{x-60.0:.0f} dBFS" for x in cbar.get_ticks()]) # Adjust labels to show true dBFS values
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ax.set_ylabel('Power')
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ax.set_xlabel('Time')
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ax.set_title(f'{os.path.basename(file_path)}')
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# plt.ylabel('Power')
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# plt.xlabel('Time (s)')
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# plt.title(f'{os.path.basename(file_path)}')
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plt.show(block=False)
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plt.pause(0.001)
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def find_mp3_files(directory):
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mp3_files = []
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# Walk through the directory
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for root, dirs, files in os.walk(directory):
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# Filter and append .mp3 files
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for file in files:
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if file.endswith(".mp3"):
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mp3_files.append(os.path.join(root, file))
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return mp3_files
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# Replace 'path/to/your/audiofile.mp3' with the path to your audio file
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file_path = []
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with open('./files.txt', 'r') as f:
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for line in f:
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if line[0] != '#':
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file_path.append(line.strip())
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for file in file_path:
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max_amplitude, avg_amplitude, avg_power, avg_power_stft = analyze_track_librosa(file)
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read_mp3_tags(file)
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print(f"Maximum Amplitude: {max_amplitude:.2f} dBFS")
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print(f"Average Amplitude: {avg_amplitude:.2f} dBFS")
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print(f"Average Power: {avg_power:.2f} dBFS")
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print(f"Average Power (STFT): {avg_power_stft:.2f} dBFS")
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plot_macro_time_power_graph(file)
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plt.show()
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