Dataset work for audio quality processor

This commit is contained in:
James Betker 2021-10-24 09:09:34 -06:00
parent 0ee1c67ce5
commit c3421b7f6d
6 changed files with 22 additions and 6 deletions

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@ -75,6 +75,8 @@ def create_dataset(dataset_opt, return_collate=False):
collate = C(dataset_opt) collate = C(dataset_opt)
elif mode == 'unsupervised_audio': elif mode == 'unsupervised_audio':
from data.audio.unsupervised_audio_dataset import UnsupervisedAudioDataset as D from data.audio.unsupervised_audio_dataset import UnsupervisedAudioDataset as D
elif mode == 'unsupervised_audio_with_noise':
from data.audio.audio_with_noise_dataset import AudioWithNoiseDataset as D
else: else:
raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode)) raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
dataset = D(dataset_opt) dataset = D(dataset_opt)

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@ -109,7 +109,7 @@ class AudioWithNoiseDataset(Dataset):
clip = clip + aug clip = clip + aug
clip.clip_(-1, 1) clip.clip_(-1, 1)
except: except:
print("Exception encountered processing {item}, re-trying because this is often just a failed aug.") print(f"Exception encountered processing {item}, re-trying because this is often just a failed aug.")
return self[item] return self[item]
out['clip'] = clip out['clip'] = clip

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@ -2,6 +2,7 @@ import os
import pathlib import pathlib
import random import random
import sys import sys
from warnings import warn
import torch import torch
import torch.utils.data import torch.utils.data
@ -32,8 +33,8 @@ def load_audio(audiopath, sampling_rate):
audio = audio[:, 0] audio = audio[:, 0]
if lsr != sampling_rate: if lsr != sampling_rate:
if lsr < sampling_rate: #if lsr < sampling_rate:
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.') # 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.')
audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=sampling_rate/lsr, mode='nearest', recompute_scale_factor=False).squeeze() audio = torch.nn.functional.interpolate(audio.unsqueeze(0).unsqueeze(1), scale_factor=sampling_rate/lsr, mode='nearest', recompute_scale_factor=False).squeeze()
# 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. # 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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@ -578,6 +578,20 @@ def imresize_np(img, scale, antialiasing=True):
return out_2.numpy() return out_2.numpy()
def load_paths_from_cache(paths, cache_path):
if not isinstance(paths, list):
paths = [paths]
if os.path.exists(cache_path):
output = torch.load(cache_path)
else:
print(f"Building cache for contents of {paths}..")
output = []
for p in paths:
output.extend(find_files_of_type('img', p, qualifier=is_audio_file)[0])
torch.save(output, cache_path)
return output
if __name__ == '__main__': if __name__ == '__main__':
# test imresize function # test imresize function
# read images # read images

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@ -284,7 +284,7 @@ class Trainer:
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_dvae_audio_clips_with_quantizer_compression.yml') parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_noisy_audio_clips_classifier.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0) parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args() args = parser.parse_args()

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@ -539,8 +539,7 @@ class MelSpectrogramInjector(Injector):
from munch import munchify from munch import munchify
from models.tacotron2 import hparams from models.tacotron2 import hparams
hp = munchify(hparams.create_hparams()) # Just use the default tacotron values for the MEL spectrogram. Noone uses anything else anyway. hp = munchify(hparams.create_hparams()) # Just use the default tacotron values for the MEL spectrogram. Noone uses anything else anyway.
self.stft = TacotronSTFT(hp.filter_length, hp.hop_length, hp.win_length, self.stft = TacotronSTFT(hp.filter_length, hp.hop_length, hp.win_length, hp.n_mel_channels, hp.sampling_rate, hp.mel_fmin, hp.mel_fmax)
hp.n_mel_channels, hp.sampling_rate, hp.mel_fmin, hp.mel_fmax)
def forward(self, state): def forward(self, state):
inp = state[self.input] inp = state[self.input]