76 lines
1.8 KiB
Python
76 lines
1.8 KiB
Python
import librosa
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import numpy as np
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from pathlib import Path
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from multiprocessing import Pool, cpu_count
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import mlflow
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SAMPLE_RATE = 22050
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def spec_to_audio(spec):
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"""
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Convert a normalized mel-spectrogram back to audio.
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"""
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spec = (spec * 80) - 80
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spec = librosa.db_to_amplitude(spec)*80
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audio = librosa.feature.inverse.mel_to_audio(spec,sr=SAMPLE_RATE)
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return audio
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def process_file(file_path):
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"""
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Load 10 second chunks single song.
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"""
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y, sr = librosa.load(file_path, mono=True, sr=SAMPLE_RATE)
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if(not sr == SAMPLE_RATE):
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return []
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size = int(SAMPLE_RATE * 10)
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sample_len = len(y)
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file_chunks = []
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for start_pos in range(0, sample_len, size):
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end = start_pos + size
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if end <= sample_len:
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chunk = y[start_pos:end]
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file_chunks.append(chunk)
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return file_chunks
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def load():
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"""
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Load 10 second chunks of songs.
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"""
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audio = []
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files = list(Path("./data/").glob("*.mp3"))
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#files = files[:12]
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with Pool(cpu_count()) as pool:
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chunk_list = pool.map(process_file, files)
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for l in chunk_list:
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audio.extend(l)
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return audio
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def audio_split(audio):
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"""
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Split 10 seconds of audio to 2 5 second clips
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"""
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size = int(SAMPLE_RATE*5)
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x = audio[:size]
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y = audio[size:size*2]
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x = librosa.feature.melspectrogram(y=x, sr=SAMPLE_RATE)
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y = librosa.feature.melspectrogram(y=y, sr=SAMPLE_RATE)
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x = ((librosa.amplitude_to_db(x,ref=np.max)+80)/80)
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y = ((librosa.amplitude_to_db(y,ref=np.max)+80)/80)
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return x,y
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def dataset(chunks):
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"""
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convert 10 second chunks to dataset
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"""
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x,y=[],[]
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with Pool(cpu_count()) as pool:
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audio_list = pool.map(audio_split,chunks)
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for (ax,ay) in audio_list:
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x.append(ax)
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y.append(ay)
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return x,y
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