forked from mrq/DL-Art-School
65 lines
4.1 KiB
Python
65 lines
4.1 KiB
Python
import argparse
|
|
import os
|
|
|
|
import torch
|
|
import torchaudio
|
|
|
|
from data.audio.unsupervised_audio_dataset import load_audio
|
|
from scripts.audio.gen.speech_synthesis_utils import do_spectrogram_diffusion, \
|
|
load_discrete_vocoder_diffuser, wav_to_mel, convert_mel_to_codes
|
|
from utils.audio import plot_spectrogram
|
|
from utils.util import load_model_from_config
|
|
|
|
|
|
def ceil_multiple(base, multiple):
|
|
res = base % multiple
|
|
if res == 0:
|
|
return base
|
|
return base + (multiple - res)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts_medium\\train_diffusion_tts_medium.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='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts_medium\\models\\38500_generator_ema.pth')
|
|
parser.add_argument('-aligned_codes', type=str, help='Comma-delimited list of integer codes that defines text & prosody. Get this by apply W2V to an existing audio clip or from a bespoke generator.',
|
|
default='0,0,0,0,10,10,0,4,0,7,0,17,4,4,0,25,5,0,13,13,0,22,4,4,0,21,15,15,7,0,0,14,4,4,6,8,4,4,0,0,12,5,0,0,5,0,4,4,22,22,8,16,16,0,4,4,4,0,0,0,0,0,0,0') # Default: 'i am very glad to see you', libritts/train-clean-100/103/1241/103_1241_000017_000001.wav.
|
|
# -cond "Y:\libritts/train-clean-100/103/1241/103_1241_000017_000001.wav"
|
|
parser.add_argument('-cond', type=str, help='Path to the conditioning input audio file.', default='Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav')
|
|
parser.add_argument('-diffusion_steps', type=int, help='Number of diffusion steps to perform to create the generate. Lower steps reduces quality, but >40 is generally pretty good.', default=100)
|
|
parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='../results/use_diffuse_tts')
|
|
parser.add_argument('-sample_rate', type=int, help='Model sample rate', default=11025)
|
|
parser.add_argument('-cond_sample_rate', type=int, help='Model sample rate', default=22050)
|
|
parser.add_argument('-device', type=str, help='Device to run on', default='cpu')
|
|
args = parser.parse_args()
|
|
os.makedirs(args.output_path, exist_ok=True)
|
|
|
|
print("Loading Diffusion Model..")
|
|
diffusion = load_model_from_config(args.opt, args.diffusion_model_name, also_load_savepoint=False,
|
|
load_path=args.diffusion_model_path, device=args.device)
|
|
aligned_codes_compression_factor = args.sample_rate * 221 // 11025
|
|
|
|
print("Loading data..")
|
|
aligned_codes = torch.tensor([int(s) for s in args.aligned_codes.split(',')]).to(args.device)
|
|
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps)
|
|
cond = load_audio(args.cond, args.cond_sample_rate).to(args.device)
|
|
if cond.shape[-1] > 88000:
|
|
cond = cond[:,:88000]
|
|
|
|
with torch.no_grad():
|
|
print("Performing inference..")
|
|
diffusion.eval()
|
|
output_shape = (1, 1, ceil_multiple(aligned_codes.shape[-1]*aligned_codes_compression_factor, 2048))
|
|
|
|
output = diffuser.p_sample_loop(diffusion, output_shape, noise=torch.zeros(output_shape, device=args.device),
|
|
model_kwargs={'tokens': aligned_codes.unsqueeze(0),
|
|
'conditioning_input': cond.unsqueeze(0)})
|
|
torchaudio.save(os.path.join(args.output_path, f'output_mean.wav'), output.cpu().squeeze(0), args.sample_rate)
|
|
|
|
for k in range(5):
|
|
output = diffuser.p_sample_loop(diffusion, output_shape, model_kwargs={'tokens': aligned_codes.unsqueeze(0),
|
|
'conditioning_input': cond.unsqueeze(0)})
|
|
|
|
torchaudio.save(os.path.join(args.output_path, f'output_{k}.wav'), output.cpu().squeeze(0), args.sample_rate)
|