cleanup
This commit is contained in:
Mikkeli Matlock
+65
-246
@@ -1,246 +1,65 @@
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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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from font_manager import safe_title, initialize_fonts
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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: {safe_title(os.path.basename(file_path))}")
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print(f"{safe_title(audio['artist'][0])} - {safe_title(audio['title'][0])}")
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else:
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print(f"File name: {safe_title(os.path.basename(file_path))}")
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class AudioFile:
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def __init__(self, file_path):
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self.file_path = file_path
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# file name / song name
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if (audio := try_mp3_tags(self.file_path)) is not None:
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self.song_name = safe_title(f"{audio['artist'][0]} - {audio['title'][0]}")
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else:
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self.song_name = safe_title(os.path.basename(self.file_path))
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self.y, self.sr = librosa.load(file_path)
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# load automatically normalises everything to [-1.0, 1.0]
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# and that's alright
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self.y_mono = librosa.to_mono(self.y)
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self.max_amplitude = np.max(np.abs(self.y_mono))
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self.avg_amplitude = np.mean(np.abs(self.y_mono))
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self.bpm, _ = librosa.beat.beat_track(y=self.y_mono, sr=self.sr)
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def display_song_name(self):
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print(self.song_name)
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def get_amplitudes(self):
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return self.max_amplitude, self.avg_amplitude
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def get_bpm(self):
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# librosa.beat.beat_track returns numpy array - extract scalar value
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if isinstance(self.bpm, np.ndarray):
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return float(self.bpm[0]) if len(self.bpm) > 0 else 0.0
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return float(self.bpm)
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def get_energy_levels_over_time(self, window = 10, hop = 2):
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"""_summary_
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Args:
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window (int, optional): Length of rolling RMS window in seconds. Defaults to 10.
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hop (int, optional): Length of window hop in seconds. Defaults to 2.
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"""
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# check if the window and hop are the same as before
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if (not hasattr(self, 'window')) or ((self.window != window) or (self.hop != hop)):
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self.window, self.hop = window, hop
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# only calculate if not already calculated
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if not hasattr(self, 'rms_array'):
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# window and hop are in seconds
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window_samples = window * self.sr
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hop_samples = hop * self.sr
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# Calculate RMS over the rolling windows
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self.rms_array = librosa.feature.rms(y=self.y, frame_length=window_samples, hop_length=hop_samples)
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def _get_times(self):
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"""Get time array for RMS data. Internal method for GUI integration."""
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if not hasattr(self, 'rms_array'):
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self.get_energy_levels_over_time()
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return librosa.frames_to_time(np.arange(self.rms_array.shape[1]), sr=self.sr, hop_length=self.hop*self.sr)
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def plot_energy_levels_over_time(self, display='window'):
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"""_summary_
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Args:
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display (str, optional): Option for where to display the plot. Defaults to 'window'.
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'window' - display in a pyplot window
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'gui' - for directing to the GUI (TBD)
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"""
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if not hasattr(self, 'rms_array'):
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self.get_energy_levels_over_time()
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# Convert frame indices to time
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times = librosa.frames_to_time(np.arange(self.rms_array.shape[1]), sr=self.sr, hop_length=self.hop*self.sr)
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# Normalize RMS for color mapping
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# check maximum power to determine mastering headspace:
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# a -6 dBFS headroom should yield a max power of around 0.25
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# otherwise could go anywhere, but we take 0.6
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local_max_power = np.max(self.rms_array)
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if local_max_power > 0.3:
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norm = mcolors.Normalize(vmin=0, vmax=0.6)
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maxpower = 0.6
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else:
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norm = mcolors.Normalize(vmin=0, vmax=0.3)
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maxpower = 0.3
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# colour map
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cmap = cm.autumn
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# Plot
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if display == 'window':
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fig, ax = plt.subplots(figsize=(10, 4))
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ax.set_ylim(0., maxpower)
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for i in range(len(times)-1):
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ax.fill_between(times[i:i+2], 0, self.rms_array[0][i], color=cmap(norm(self.rms_array[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(safe_title(os.path.basename(self.file_path)))
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plt.show(block=False)
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plt.pause(0.001)
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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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if __name__ == '__main__':
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# Legacy batch processing mode - runs when master_core.py is executed directly
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# For GUI usage, run main.py instead
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print("Running legacy batch analysis mode...")
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print("For the new GUI interface, please run: python main.py")
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print()
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# Initialize fonts for matplotlib
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initialize_fonts()
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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] != '#' and 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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currentsong = AudioFile(file)
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currentsong.display_song_name()
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print(f"BPM: {currentsong.get_bpm()}")
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currentsong.plot_energy_levels_over_time()
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# plot_macro_time_power_graph(file)
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plt.show()
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import os
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import librosa
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import numpy as np
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from mutagen.mp3 import MP3
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from mutagen.easyid3 import EasyID3
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from font_manager import safe_title
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def _try_mp3_tags(file_path):
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try:
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return MP3(file_path, ID3=EasyID3)
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except Exception:
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return None
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class AudioFile:
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def __init__(self, file_path):
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self.file_path = file_path
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audio = _try_mp3_tags(self.file_path)
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artist = audio.get('artist', [None])[0] if audio is not None else None
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title = audio.get('title', [None])[0] if audio is not None else None
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if artist and title:
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self.song_name = safe_title(f"{artist} - {title}")
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else:
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self.song_name = safe_title(os.path.basename(self.file_path))
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# librosa.load normalises to [-1.0, 1.0]
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self.y, self.sr = librosa.load(file_path)
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self.y_mono = librosa.to_mono(self.y)
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self.max_amplitude = np.max(np.abs(self.y_mono))
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self.avg_amplitude = np.mean(np.abs(self.y_mono))
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self.bpm, _ = librosa.beat.beat_track(y=self.y_mono, sr=self.sr)
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def get_bpm(self):
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# librosa.beat.beat_track returns numpy array - extract scalar value
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if isinstance(self.bpm, np.ndarray):
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return float(self.bpm[0]) if len(self.bpm) > 0 else 0.0
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return float(self.bpm)
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def get_energy_levels_over_time(self, window=10, hop=2):
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"""Compute rolling RMS power.
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Args:
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window: Rolling window length in seconds.
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hop: Hop length in seconds.
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"""
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if (not hasattr(self, 'window')) or (self.window != window) or (self.hop != hop):
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self.window, self.hop = window, hop
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if not hasattr(self, 'rms_array'):
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window_samples = window * self.sr
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hop_samples = hop * self.sr
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self.rms_array = librosa.feature.rms(
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y=self.y, frame_length=window_samples, hop_length=hop_samples
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)
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def get_times(self):
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"""Time-axis values matching the RMS frames."""
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if not hasattr(self, 'rms_array'):
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self.get_energy_levels_over_time()
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return librosa.frames_to_time(
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np.arange(self.rms_array.shape[1]), sr=self.sr, hop_length=self.hop * self.sr
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)
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