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import random |
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import uuid |
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import numpy |
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import os |
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import random |
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import fnmatch |
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from tqdm.auto import tqdm |
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from scipy.io import wavfile |
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from bark.generation import load_model, SAMPLE_RATE |
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from bark.api import semantic_to_waveform |
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from bark import text_to_semantic |
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from bark.generation import load_model |
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from training.data import load_books, random_split_chunk |
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output = 'training/data/output' |
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output_wav = 'training/data/output_wav' |
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def prepare_semantics_from_text(num_generations): |
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loaded_data = load_books(True) |
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print('Loading semantics model') |
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load_model(use_gpu=True, use_small=False, force_reload=False, model_type='text') |
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if not os.path.isdir(output): |
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os.mkdir(output) |
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loop = 1 |
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while 1: |
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filename = uuid.uuid4().hex + '.npy' |
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file_name = os.path.join(output, filename) |
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text = '' |
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while not len(text) > 0: |
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text = random_split_chunk(loaded_data) |
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text = text.strip() |
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print(f'{loop} Generating semantics for text:', text) |
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loop+=1 |
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semantics = text_to_semantic(text, temp=round(random.uniform(0.6, 0.8), ndigits=2)) |
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numpy.save(file_name, semantics) |
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def prepare_wavs_from_semantics(): |
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if not os.path.isdir(output): |
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raise Exception('No \'output\' folder, make sure you run create_data.py first!') |
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if not os.path.isdir(output_wav): |
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os.mkdir(output_wav) |
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print('Loading coarse model') |
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load_model(use_gpu=True, use_small=False, force_reload=False, model_type='coarse') |
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print('Loading fine model') |
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load_model(use_gpu=True, use_small=False, force_reload=False, model_type='fine') |
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files = fnmatch.filter(os.listdir(output), '*.npy') |
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current = 1 |
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total = len(files) |
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for i, f in tqdm(enumerate(files), total=len(files)): |
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real_name = '.'.join(f.split('.')[:-1]) |
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file_name = os.path.join(output, f) |
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out_file = os.path.join(output_wav, f'{real_name}.wav') |
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if not os.path.isfile(out_file) and os.path.isfile(file_name): |
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print(f'Processing ({i+1}/{total}) -> {f}') |
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wav = semantic_to_waveform(numpy.load(file_name), temp=round(random.uniform(0.6, 0.8), ndigits=2)) |
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wavfile.write(out_file, SAMPLE_RATE, wav) |
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print('Done!') |
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