74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
import os
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import random
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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 = [11025]
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def __init__(self, input_dir, clip_length=16384):
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input_files = [
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os.path.join(root, f)
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for root, _, files in os.walk(input_dir)
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for f in files
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if f.endswith(".wav") or f.endswith(".mp3") or f.endswith(".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
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original_sample_rate = decoded_samples.sample_rate
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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(
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original_sample_rate, mangled_sample_rate
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)
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resample_transform_high = torchaudio.transforms.Resample(
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mangled_sample_rate, original_sample_rate
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)
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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, clip_length)
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splitted_high_quality_audio[-1] = AudioUtils.pad_tensor(
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splitted_high_quality_audio[-1], clip_length
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)
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splitted_low_quality_audio = AudioUtils.split_audio(low_audio, clip_length)
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splitted_low_quality_audio[-1] = AudioUtils.pad_tensor(
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splitted_low_quality_audio[-1], clip_length
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)
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for high_quality_sample, low_quality_sample in zip(
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splitted_high_quality_audio, splitted_low_quality_audio
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):
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data.append(
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(
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(high_quality_sample, low_quality_sample),
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(original_sample_rate, mangled_sample_rate),
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)
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)
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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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