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import os
import fnmatch
import shutil
import numpy
import torchaudio
import gradio
from bark.hubert.pre_kmeans_hubert import CustomHubert
from bark.hubert.customtokenizer import auto_train
from tqdm.auto import tqdm
def training_prepare_files(path, model,progress=gradio.Progress(track_tqdm=True)):
semanticsfolder = "./training/data/output"
wavfolder = "./training/data/output_wav"
ready = os.path.join(path, 'ready')
testfiles = fnmatch.filter(os.listdir(ready), '*.npy')
if(len(testfiles) < 1):
# prepare and copy for training
hubert_model = CustomHubert(checkpoint_path=model)
wavfiles = fnmatch.filter(os.listdir(wavfolder), '*.wav')
for i, f in tqdm(enumerate(wavfiles), total=len(wavfiles)):
semaname = '.'.join(f.split('.')[:-1]) # Cut off the extension
semaname = f'{semaname}.npy'
semafilename = os.path.join(semanticsfolder, semaname)
if not os.path.isfile(semafilename):
print(f'Skipping {f} no semantics pair found!')
continue
print('Processing', f)
wav, sr = torchaudio.load(os.path.join(wavfolder, f))
if wav.shape[0] == 2: # Stereo to mono if needed
wav = wav.mean(0, keepdim=True)
output = hubert_model.forward(wav, input_sample_hz=sr)
out_array = output.cpu().numpy()
fname = f'{i}_semantic_features.npy'
numpy.save(os.path.join(ready, fname), out_array)
fname = f'{i}_semantic.npy'
shutil.copy(semafilename, os.path.join(ready, fname))
def train(path, save_every, max_epochs):
auto_train(path, save_epochs=save_every)
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