DL-Art-School/codes/scripts/audio/gen/use_gpt_tts.py
2021-12-16 23:28:54 -07:00

81 lines
4.5 KiB
Python

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