forked from mrq/DL-Art-School
164 lines
6.2 KiB
Python
164 lines
6.2 KiB
Python
import os
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import pathlib
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import random
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import torch
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import torch.utils.data
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import torch.nn.functional as F
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import torchaudio
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from audio2numpy import open_audio
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from tqdm import tqdm
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from data.audio.wav_aug import WavAugmentor
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from data.util import find_files_of_type, is_wav_file, is_audio_file
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from models.tacotron2.taco_utils import load_wav_to_torch
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from utils.util import opt_get
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def load_audio(audiopath, sampling_rate):
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if audiopath[-4:] == '.wav':
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audio, lsr = load_wav_to_torch(audiopath)
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else:
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audio, lsr = open_audio(audiopath)
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audio = torch.FloatTensor(audio)
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# Remove any channel data.
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if len(audio.shape) > 1:
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if audio.shape[0] < 5:
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audio = audio[0]
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else:
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assert audio.shape[1] < 5
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audio = audio[:, 0]
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if lsr != sampling_rate:
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if lsr < sampling_rate:
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print(f'{audiopath} has a sample rate of {sampling_rate} which is lower than the requested sample rate of {sampling_rate}. This is not a good idea.')
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audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=sampling_rate/lsr, mode='nearest', recompute_scale_factor=False).squeeze()
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# Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
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# '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
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if torch.any(audio > 2) or not torch.any(audio < 0):
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print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
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audio.clip_(-1, 1)
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return audio.unsqueeze(0)
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class UnsupervisedAudioDataset(torch.utils.data.Dataset):
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def __init__(self, opt):
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path = opt['path']
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cache_path = opt['cache_path'] # Will fail when multiple paths specified, must be specified in this case.
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if not isinstance(path, list):
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path = [path]
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if os.path.exists(cache_path):
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self.audiopaths = torch.load(cache_path)
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else:
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print("Building cache..")
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self.audiopaths = []
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for p in path:
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self.audiopaths.extend(find_files_of_type('img', p, qualifier=is_audio_file)[0])
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torch.save(self.audiopaths, cache_path)
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# Parse options
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self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
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self.pad_to = opt_get(opt, ['pad_to_seconds'], None)
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if self.pad_to is not None:
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self.pad_to *= self.sampling_rate
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self.pad_to = opt_get(opt, ['pad_to_samples'], self.pad_to)
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self.extra_samples = opt_get(opt, ['extra_samples'], 0)
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self.extra_sample_len = opt_get(opt, ['extra_sample_length'], 2)
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self.extra_sample_len *= self.sampling_rate
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def get_audio_for_index(self, index):
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audiopath = self.audiopaths[index]
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audio = load_audio(audiopath, self.sampling_rate)
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return audio, audiopath
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def get_related_audio_for_index(self, index):
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if self.extra_samples <= 0:
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return None, 0
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audiopath = self.audiopaths[index]
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related_files = find_files_of_type('img', os.path.dirname(audiopath), qualifier=is_audio_file)[0]
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assert audiopath in related_files
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assert len(related_files) < 50000 # Sanity check to ensure we aren't loading "related files" that aren't actually related.
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if len(related_files) == 0:
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print(f"No related files for {audiopath}")
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related_files.remove(audiopath)
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related_clips = []
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random.shuffle(related_clips)
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i = 0
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for related_file in related_files:
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rel_clip = load_audio(related_file, self.sampling_rate)
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gap = rel_clip.shape[-1] - self.extra_sample_len
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if gap < 0:
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rel_clip = F.pad(rel_clip, pad=(0, abs(gap)))
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elif gap > 0:
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rand_start = random.randint(0, gap)
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rel_clip = rel_clip[:, rand_start:rand_start+self.extra_sample_len]
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related_clips.append(rel_clip)
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i += 1
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if i >= self.extra_samples:
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break
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actual_extra_samples = i
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while i < self.extra_samples:
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related_clips.append(torch.zeros(1, self.extra_sample_len))
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i += 1
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return torch.stack(related_clips, dim=0), actual_extra_samples
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def __getitem__(self, index):
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try:
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# Split audio_norm into two tensors of equal size.
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audio_norm, filename = self.get_audio_for_index(index)
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alt_files, actual_samples = self.get_related_audio_for_index(index)
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except:
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print(f"Error loading audio for file {filename} or {alt_files}")
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return self[index+1]
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# This is required when training to make sure all clips align.
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if self.pad_to is not None:
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if audio_norm.shape[-1] <= self.pad_to:
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audio_norm = torch.nn.functional.pad(audio_norm, (0, self.pad_to - audio_norm.shape[-1]))
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else:
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gap = audio_norm.shape[-1] - self.pad_to
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start = random.randint(0, gap-1)
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audio_norm = audio_norm[:, start:start+self.pad_to]
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output = {
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'clip': audio_norm,
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'path': filename,
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}
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if self.extra_samples > 0:
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output['alt_clips'] = alt_files
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output['num_alt_clips'] = actual_samples
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return output
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def __len__(self):
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return len(self.audiopaths)
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if __name__ == '__main__':
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params = {
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'mode': 'unsupervised_audio',
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'path': ['Z:\\split\\cleaned\\books0', 'Z:\\split\\cleaned\\books2'],
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'cache_path': 'E:\\audio\\remote-cache.pth',
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'sampling_rate': 22050,
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'pad_to_seconds': 5,
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'phase': 'train',
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'n_workers': 4,
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'batch_size': 16,
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'extra_samples': 4,
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}
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from data import create_dataset, create_dataloader, util
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ds = create_dataset(params)
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dl = create_dataloader(ds, params)
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i = 0
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for b in tqdm(dl):
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for b_ in range(16):
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pass
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#torchaudio.save(f'{i}_clip1_{b_}.wav', b['clip1'][b_], ds.sampling_rate)
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#torchaudio.save(f'{i}_clip2_{b_}.wav', b['clip2'][b_], ds.sampling_rate)
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#i += 1
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