72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
import os
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import random
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import torch
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import torchaudio
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import torchcodec.decoders as decoders
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import tqdm
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from torch.utils.data import Dataset
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import AudioUtils
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class AudioDataset(Dataset):
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audio_sample_rates = [8000, 11025, 12000, 16000, 22050, 24000, 32000, 44100]
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def __init__(self, input_dir, clip_length: int = 512, normalize: bool = True):
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self.clip_length = clip_length
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self.normalize = normalize
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input_files = [
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os.path.join(input_dir, f)
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for f in os.listdir(input_dir)
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if os.path.isfile(os.path.join(input_dir, f))
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and f.lower().endswith((".wav", ".mp3", ".flac"))
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]
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data = []
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for audio_clip in tqdm.tqdm(
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input_files, desc=f"Processing {len(input_files)} audio file(s)"
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):
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decoder = decoders.AudioDecoder(audio_clip)
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decoded_samples = decoder.get_all_samples()
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audio = decoded_samples.data.float()
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original_sample_rate = decoded_samples.sample_rate
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if normalize:
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audio = AudioUtils.normalize(audio)
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splitted_high_quality_audio = AudioUtils.split_audio(audio, clip_length, True)
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if not splitted_high_quality_audio:
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continue
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for splitted_audio_clip in splitted_high_quality_audio:
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for audio_clip in torch.split(splitted_audio_clip, 1):
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data.append((audio_clip, original_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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audio_clip = self.audio_data[idx]
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mangled_sample_rate = random.choice(self.audio_sample_rates)
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resample_transform_low = torchaudio.transforms.Resample(
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audio_clip[1], mangled_sample_rate
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)
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resample_transform_high = torchaudio.transforms.Resample(
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mangled_sample_rate, audio_clip[1]
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)
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low_audio_clip = resample_transform_high(resample_transform_low(audio_clip[0]))
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if audio_clip[0].shape[1] < low_audio_clip.shape[1]:
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low_audio_clip = low_audio_clip[:, :audio_clip[0].shape[1]]
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elif audio_clip[0].shape[1] > low_audio_clip.shape[1]:
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low_audio_clip = AudioUtils.pad_tensor(low_audio_clip, self.clip_length)
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return ((audio_clip[0], low_audio_clip), (audio_clip[1], mangled_sample_rate))
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