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74 lines
2.2 KiB
74 lines
2.2 KiB
import torch
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import torchaudio
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import matplotlib as plt
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import musdb
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import os
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import numpy as np
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import glob
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import librosa
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import soundfile
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def load(path, sr=22050, mono=True, mode="numpy", offset=0.0, duration=None):
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y, curr_sr = librosa.load(path, sr=sr, mono=mono, res_type='kaiser_fast', offset=offset, duration=duration)
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if len(y.shape) == 1:
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# Expand channel dimension
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y = y[np.newaxis, :]
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if mode == "pytorch":
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y = torch.tensor(y)
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return y, curr_sr
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def write_wav(path, audio, sr):
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soundfile.write(path, audio.T, sr, "PCM_16")
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def get_musdbhq(database_path):
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'''
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Retrieve audio file paths for MUSDB HQ dataset
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:param database_path: MUSDB HQ root directory
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:return: dictionary with train and test keys, each containing list of samples, each sample containing all audio paths
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'''
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subsets = list()
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for subset in ["train", "test"]:
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print("Loading " + subset + " set...")
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tracks = glob.glob(os.path.join(database_path, subset, "*"))
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samples = list()
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# Go through tracks
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for track_folder in sorted(tracks):
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# Skip track if mixture is already written, assuming this track is done already
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example = dict()
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for stem in ["mix", "bass", "drums", "other", "vocals"]:
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filename = stem if stem != "mix" else "mixture"
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audio_path = os.path.join(track_folder, filename + ".wav")
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example[stem] = audio_path
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# Add other instruments to form accompaniment
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acc_path = os.path.join(track_folder, "accompaniment.wav")
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if not os.path.exists(acc_path):
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print("Writing accompaniment to " + track_folder)
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stem_audio = []
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for stem in ["bass", "drums", "other"]:
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audio, sr = load(example[stem], sr=None, mono=False)
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stem_audio.append(audio)
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acc_audio = np.clip(sum(stem_audio), -1.0, 1.0)
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write_wav(acc_path, acc_audio, sr)
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example["accompaniment"] = acc_path
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samples.append(example)
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subsets.append(samples)
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return subsets
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path = "C:/Users/IAN/Desktop/Wave-U-Net/musdb18-hq/"
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res = get_musdbhq(path)
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print(res)
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