forked from mrq/DL-Art-School
81 lines
4.5 KiB
Python
81 lines
4.5 KiB
Python
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import argparse
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import os
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import random
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import torch
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import torchaudio
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import yaml
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from data.audio.unsupervised_audio_dataset import load_audio
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from data.util import is_audio_file, find_files_of_type
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from models.tacotron2.text import text_to_sequence
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from scripts.audio.gen.speech_synthesis_utils import do_spectrogram_diffusion, \
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load_discrete_vocoder_diffuser, wav_to_mel, convert_mel_to_codes
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from trainer.injectors.base_injectors import MelSpectrogramInjector
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from utils.audio import plot_spectrogram
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from utils.options import Loader
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from utils.util import load_model_from_config
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import torch.nn.functional as F
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def do_vocoding(dvae, vocoder, diffuser, codes, cond=None, plot_spec=False):
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return
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def load_conditioning_candidates(path, num_conds, sample_rate=22050, cond_length=44100):
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candidates = find_files_of_type('img', path, qualifier=is_audio_file)[0]
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# Sample with replacement. This can get repeats, but more conveniently handles situations where there are not enough candidates.
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related_mels = []
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for k in range(num_conds):
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rel_clip = load_audio(candidates[k], sample_rate)
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gap = rel_clip.shape[-1] - cond_length
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if gap < 0:
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rel_clip = F.pad(rel_clip, pad=(0, abs(gap)))
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elif gap > 0:
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rand_start = random.randint(0, gap)
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rel_clip = rel_clip[:, rand_start:rand_start + cond_length]
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mel_clip = MelSpectrogramInjector({'in': 'wav', 'out': 'mel'},{})({'wav': rel_clip.unsqueeze(0)})['mel'].squeeze(0)
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related_mels.append(mel_clip)
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return torch.stack(related_mels, dim=0)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt_diffuse', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae.yml')
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parser.add_argument('-diffusion_model_name', type=str, help='Name of the diffusion model in opt.', default='generator')
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parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\6100_generator_ema.pth')
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parser.add_argument('-dvae_model_name', type=str, help='Name of the DVAE model in opt.', default='dvae')
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parser.add_argument('-opt_gpt_tts', type=str, help='Path to options YAML file used to train the GPT-TTS model', default='X:\\dlas\\experiments\\train_gpt_tts.yml')
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parser.add_argument('-gpt_tts_model_name', type=str, help='Name of the GPT TTS model in opt.', default='gpt')
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parser.add_argument('-gpt_tts_model_path', type=str, help='GPT TTS model checkpoint to load.', default='X:\\dlas\\experiments\\train_gpt_tts\\models\\22000_gpt.pth')
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parser.add_argument('-text', type=str, help='Text to speak.', default="I'm a language model that has learned to speak.")
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parser.add_argument('-cond_path', type=str, help='Folder containing conditioning samples.', default='Z:\\clips\\books1\\3042_18_Holden__000000000')
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parser.add_argument('-num_cond', type=int, help='Number of conditioning samples to load.', default=3)
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args = parser.parse_args()
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print("Loading GPT TTS..")
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with open(args.opt_gpt_tts, mode='r') as f:
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gpt_opt = yaml.load(f, Loader=Loader)
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gpt_opt['networks'][args.gpt_tts_model_name]['kwargs']['checkpointing'] = False # Required for beam search
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gpt = load_model_from_config(preloaded_options=gpt_opt, model_name=args.gpt_tts_model_name, also_load_savepoint=False, load_path=args.gpt_tts_model_path)
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print("Loading data..")
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text = torch.IntTensor(text_to_sequence(args.text, ['english_cleaners'])).unsqueeze(0).cuda()
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conds = load_conditioning_candidates(args.cond_path, args.num_cond).unsqueeze(0).cuda()
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print("Performing GPT inference..")
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codes = gpt.inference(text, conds, num_beams=4) #TODO: check the text length during training and match that during inference.
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# Delete the GPT TTS model to free up GPU memory
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del gpt
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print("Loading DVAE..")
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dvae = load_model_from_config(args.opt_diffuse, args.dvae_model_name)
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print("Loading Diffusion Model..")
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diffusion = load_model_from_config(args.opt_diffuse, args.diffusion_model_name, also_load_savepoint=False, load_path=args.diffusion_model_path)
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diffuser = load_discrete_vocoder_diffuser()
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print("Performing vocoding..")
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wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, codes, conds[:, 0], spectrogram_compression_factor=128, plt_spec=True)
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torchaudio.save('gpt_tts_output.wav', wav.squeeze(0), 10025)
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