forked from mrq/DL-Art-School
202 lines
8.1 KiB
Python
202 lines
8.1 KiB
Python
import os
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import pathlib
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import random
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import sys
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from warnings import warn
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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, load_paths_from_cache
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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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elif audiopath[-4:] == '.mp3':
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# https://github.com/neonbjb/pyfastmp3decoder - Definitely worth it.
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from pyfastmp3decoder.mp3decoder import load_mp3
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audio, lsr = load_mp3(audiopath, sampling_rate)
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audio = torch.FloatTensor(audio)
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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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# warn(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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def load_similar_clips(path, sample_length, sample_rate, n=3, include_self=True, fallback_to_self=True):
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sim_path = os.path.join(os.path.dirname(path), 'similarities.pth')
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candidates = []
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if os.path.exists(sim_path):
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similarities = torch.load(sim_path)
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fname = os.path.basename(path)
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if fname in similarities.keys():
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candidates = [os.path.join(os.path.dirname(path), s) for s in similarities[fname]]
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else:
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print(f'Similarities list found for {path} but {fname} was not in that list.')
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if len(candidates) == 0:
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if fallback_to_self:
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candidates = [path]
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else:
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candidates = find_files_of_type('img', os.path.dirname(path), qualifier=is_audio_file)[0]
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assert len(candidates) < 50000 # Sanity check to ensure we aren't loading "related files" that aren't actually related.
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if not include_self:
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candidates.remove(path)
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if len(candidates) == 0:
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print(f"No conditioning candidates found for {path}")
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raise NotImplementedError()
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# Sample with replacement. This can get repeats, but more conveniently handles situations where there are not enough candidates.
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related_clips = []
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for k in range(n):
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rel_clip = load_audio(random.choice(candidates), sample_rate)
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gap = rel_clip.shape[-1] - sample_length
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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+sample_length]
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related_clips.append(rel_clip)
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if n > 1:
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return torch.stack(related_clips, dim=0)
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else:
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return related_clips[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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exclusions = []
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if 'exclusions' in opt.keys():
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for exc in opt['exclusions']:
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with open(exc, 'r') as f:
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exclusions.extend(f.read().splitlines())
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self.audiopaths = load_paths_from_cache(path, cache_path, exclusions)
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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.min_length = opt_get(opt, ['min_length'], 0)
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# "Resampled clip" is audio data pulled from the basis of "clip" but with randomly different bounds. There are no
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# guarantees that "clip_resampled" is different from "clip": in fact, if "clip" is less than pad_to_seconds/samples,
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self.should_resample_clip = opt_get(opt, ['resample_clip'], False)
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# "Extra samples" are other audio clips pulled from wav files in the same directory as the 'clip' wav file.
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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'], 44000)
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self.debug_loading_failures = opt_get(opt, ['debug_loading_failures'], True)
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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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assert audio.shape[1] > self.min_length
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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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return load_similar_clips(audiopath, self.extra_sample_len, self.sampling_rate, n=self.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 = self.get_related_audio_for_index(index)
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except:
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if self.debug_loading_failures:
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print(f"Error loading audio for file {self.audiopaths[index]} {sys.exc_info()}")
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return self[index+1]
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# When generating resampled clips, skew is a bias that tries to spread them out from each other, reducing their
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# influence on one another.
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skew = [-1, 1] if self.should_resample_clip else [0]
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# To increase variability, which skew is applied to the clip and resampled_clip is randomized.
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random.shuffle(skew)
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clips = []
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for sk in skew:
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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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clips.append(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 = min(max(random.randint(0, gap-1) + sk * gap // 2, 0), gap-1)
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clips.append(audio_norm[:, start:start+self.pad_to])
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else:
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clips.append(audio_norm)
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output = {
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'clip': clips[0],
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'clip_lengths': audio_norm.shape[-1],
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'path': filename,
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}
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if self.should_resample_clip:
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output['resampled_clip'] = clips[1]
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if self.extra_samples > 0:
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output['alt_clips'] = alt_files
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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': ['\\\\192.168.5.3\\rtx3080_audio\\split\\cleaned\\books0'],
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'cache_path': 'E:\\audio\\remote-cache3.pth',
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'sampling_rate': 22050,
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'pad_to_samples': 40960,
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'phase': 'train',
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'n_workers': 1,
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'batch_size': 16,
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'extra_samples': 4,
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'resample_clip': True,
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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(b['clip'].shape[0]):
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#pass
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torchaudio.save(f'{i}_clip_{b_}.wav', b['clip'][b_], ds.sampling_rate)
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torchaudio.save(f'{i}_resampled_clip_{b_}.wav', b['resampled_clip'][b_], ds.sampling_rate)
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i += 1
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