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codes generation script

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This commit is contained in:
James Betker 2022-06-03 11:02:28 -06:00
parent 2f4d990ad1
commit d3a60633a3
2 changed files with 44 additions and 0 deletions
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3
codes/data/audio/unsupervised_audio_dataset.py
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@ -111,6 +111,7 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
self.pad_to *= self.sampling_rate self.pad_to *= self.sampling_rate
self.pad_to = opt_get(opt, ['pad_to_samples'], self.pad_to) self.pad_to = opt_get(opt, ['pad_to_samples'], self.pad_to)
self.min_length = opt_get(opt, ['min_length'], 0) self.min_length = opt_get(opt, ['min_length'], 0)
self.dont_clip = opt_get(opt, ['dont_clip'], False)
# "Resampled clip" is audio data pulled from the basis of "clip" but with randomly different bounds. There are no # "Resampled clip" is audio data pulled from the basis of "clip" but with randomly different bounds. There are no
# guarantees that "clip_resampled" is different from "clip": in fact, if "clip" is less than pad_to_seconds/samples, # guarantees that "clip_resampled" is different from "clip": in fact, if "clip" is less than pad_to_seconds/samples,
@ -126,6 +127,8 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
audiopath = self.audiopaths[index] audiopath = self.audiopaths[index]
audio = load_audio(audiopath, self.sampling_rate) audio = load_audio(audiopath, self.sampling_rate)
assert audio.shape[1] > self.min_length assert audio.shape[1] > self.min_length
if self.dont_clip:
assert audio.shape[1] <= self.pad_to
return audio, audiopath return audio, audiopath
def get_related_audio_for_index(self, index): def get_related_audio_for_index(self, index):

41
codes/scripts/audio/preparation/gen_dvae_codes.py Normal file
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@ -0,0 +1,41 @@
import os
import torch
from tqdm import tqdm
from scripts.audio.gen.speech_synthesis_utils import load_speech_dvae, wav_to_mel
if __name__ == '__main__':
input_folder = 'C:\\Users\\James\\Downloads\\lex2\\lexfridman_training_mp3'
output_folder = 'C:\\Users\\James\\Downloads\\lex2\\quantized'
params = {
'mode': 'unsupervised_audio',
'path': [input_folder],
'cache_path': f'{input_folder}/cache.pth',
'sampling_rate': 22050,
'pad_to_samples': 441000,
'resample_clip': False,
'extra_samples': 0,
'phase': 'train',
'n_workers': 2,
'batch_size': 64,
}
from data import create_dataset, create_dataloader
os.makedirs(output_folder, exist_ok=True)
ds = create_dataset(params)
dl = create_dataloader(ds, params)
dvae = load_speech_dvae().cuda()
with torch.no_grad():
for batch in tqdm(dl):
audio = batch['clip'].cuda()
mel = wav_to_mel(audio)
codes = dvae.get_codebook_indices(mel)
for i in range(audio.shape[0]):
c = codes[i, :batch['clip_lengths'][i]//1024+4] # +4 seems empirically to be a good clipping point - it seems to preserve the termination codes.
fn = batch['path'][i]
outp = os.path.join(output_folder, os.path.relpath(fn, input_folder) + ".pth")
os.makedirs(os.path.dirname(outp), exist_ok=True)
torch.save(c.tolist(), outp)