46 lines
2.0 KiB
Python
46 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 AudioUtils
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class AudioDataset(Dataset):
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audio_sample_rates = [11025]
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MAX_LENGTH = 44100 # Define your desired maximum length here
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def __init__(self, input_dir, device):
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self.input_files = [os.path.join(root, f) for root, _, files in os.walk(input_dir) for f in files if f.endswith('.wav')]
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self.device = device
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def __len__(self):
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return len(self.input_files)
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def __getitem__(self, idx):
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# Load high-quality audio
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high_quality_audio, original_sample_rate = torchaudio.load(self.input_files[idx], normalize=True)
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# Change to mono
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high_quality_audio = AudioUtils.stereo_tensor_to_mono(high_quality_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_quality_audio = resample_transform_low(high_quality_audio)
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low_quality_audio = resample_transform_high(low_quality_audio)
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splitted_high_quality_audio = AudioUtils.split_audio(high_quality_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_high_quality_audio = [tensor.to(self.device) for tensor in splitted_high_quality_audio]
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splitted_low_quality_audio = AudioUtils.split_audio(low_quality_audio, 128)
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splitted_low_quality_audio[-1] = AudioUtils.pad_tensor(splitted_low_quality_audio[-1], 128)
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splitted_low_quality_audio = [tensor.to(self.device) for tensor in splitted_low_quality_audio]
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return (splitted_high_quality_audio, original_sample_rate), (splitted_low_quality_audio, mangled_sample_rate)
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