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| import random | |
| import uuid | |
| import numpy | |
| import os | |
| import random | |
| import fnmatch | |
| from tqdm.auto import tqdm | |
| from scipy.io import wavfile | |
| from bark.generation import load_model, SAMPLE_RATE | |
| from bark.api import semantic_to_waveform | |
| from bark import text_to_semantic | |
| from bark.generation import load_model | |
| from training.data import load_books, random_split_chunk | |
| output = 'training/data/output' | |
| output_wav = 'training/data/output_wav' | |
| def prepare_semantics_from_text(num_generations): | |
| loaded_data = load_books(True) | |
| print('Loading semantics model') | |
| load_model(use_gpu=True, use_small=False, force_reload=False, model_type='text') | |
| if not os.path.isdir(output): | |
| os.mkdir(output) | |
| loop = 1 | |
| while 1: | |
| filename = uuid.uuid4().hex + '.npy' | |
| file_name = os.path.join(output, filename) | |
| text = '' | |
| while not len(text) > 0: | |
| text = random_split_chunk(loaded_data) # Obtain a short chunk of text | |
| text = text.strip() | |
| print(f'{loop} Generating semantics for text:', text) | |
| loop+=1 | |
| semantics = text_to_semantic(text, temp=round(random.uniform(0.6, 0.8), ndigits=2)) | |
| numpy.save(file_name, semantics) | |
| def prepare_wavs_from_semantics(): | |
| if not os.path.isdir(output): | |
| raise Exception('No \'output\' folder, make sure you run create_data.py first!') | |
| if not os.path.isdir(output_wav): | |
| os.mkdir(output_wav) | |
| print('Loading coarse model') | |
| load_model(use_gpu=True, use_small=False, force_reload=False, model_type='coarse') | |
| print('Loading fine model') | |
| load_model(use_gpu=True, use_small=False, force_reload=False, model_type='fine') | |
| files = fnmatch.filter(os.listdir(output), '*.npy') | |
| current = 1 | |
| total = len(files) | |
| for i, f in tqdm(enumerate(files), total=len(files)): | |
| real_name = '.'.join(f.split('.')[:-1]) # Cut off the extension | |
| file_name = os.path.join(output, f) | |
| out_file = os.path.join(output_wav, f'{real_name}.wav') | |
| if not os.path.isfile(out_file) and os.path.isfile(file_name): # Don't process files that have already been processed, to be able to continue previous generations | |
| print(f'Processing ({i+1}/{total}) -> {f}') | |
| wav = semantic_to_waveform(numpy.load(file_name), temp=round(random.uniform(0.6, 0.8), ndigits=2)) | |
| # Change to PCM16 | |
| # wav = (wav * 32767).astype(np.int16) | |
| wavfile.write(out_file, SAMPLE_RATE, wav) | |
| print('Done!') | |