32 lines
1.1 KiB
Python
32 lines
1.1 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 = [8000, 11025, 16000, 22050]
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audio_sample_rates = [11025]
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def __init__(self, input_dir):
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self.input_files = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.endswith('.wav')]
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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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# 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 = torchaudio.transforms.Resample(original_sample_rate, mangled_sample_rate)
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low_quality_audio = resample_transform(high_quality_audio)
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return (AudioUtils.stereo_tensor_to_mono(high_quality_audio), original_sample_rate), (AudioUtils.stereo_tensor_to_mono(low_quality_audio), mangled_sample_rate)
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