47 lines
2.0 KiB
Python
47 lines
2.0 KiB
Python
from torch.utils.data import Dataset
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import torch.nn.functional as F
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import torch
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import torchaudio
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import os
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import random
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import torchaudio.transforms as T
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import tqdm
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import AudioUtils
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class AudioDataset(Dataset):
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audio_sample_rates = [11025]
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def __init__(self, input_dir, device):
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self.device = device
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input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav') or f.endswith('.mp3') or f.endswith('.flac')]
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data = []
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for audio_clip in tqdm.tqdm(input_files, desc=f"Processing {len(input_files)} audio file(s)"):
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audio, original_sample_rate = torchaudio.load(audio_clip, normalize=True)
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audio = AudioUtils.stereo_tensor_to_mono(audio)
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# Generate low-quality audio with random downsampling
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mangled_sample_rate = random.choice(self.audio_sample_rates)
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resample_transform_low = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
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resample_transform_high = torchaudio.transforms.Resample(mangled_sample_rate, original_sample_rate)
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low_audio = resample_transform_low(audio)
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low_audio = resample_transform_high(low_audio)
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splitted_high_quality_audio = AudioUtils.split_audio(audio, 128)
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splitted_high_quality_audio[-1] = AudioUtils.pad_tensor(splitted_high_quality_audio[-1], 128)
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splitted_low_quality_audio = AudioUtils.split_audio(low_audio, 128)
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splitted_low_quality_audio[-1] = AudioUtils.pad_tensor(splitted_low_quality_audio[-1], 128)
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for high_quality_sample, low_quality_sample in zip(splitted_high_quality_audio, splitted_low_quality_audio):
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data.append(((high_quality_sample, low_quality_sample), (original_sample_rate, mangled_sample_rate)))
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self.audio_data = data
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def __len__(self):
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return len(self.audio_data)
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def __getitem__(self, idx):
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return self.audio_data[idx]
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