move speech utils
This commit is contained in:
parent
e7957e4897
commit
62c8ed9a29
|
@ -147,7 +147,7 @@ def register_gpt_tts_hf(opt_net, opt):
|
|||
|
||||
|
||||
if __name__ == '__main__':
|
||||
gpt = GptTtsHf()
|
||||
gpt = GptTtsHf(model_dim=1024, heads=16)
|
||||
l = gpt(torch.randint(high=len(symbols), size=(2,100)),
|
||||
torch.randn(2,2,80,800),
|
||||
torch.randint(high=8192, size=(2,200)),
|
||||
|
|
0
codes/scripts/audio/gen/__init__.py
Normal file
0
codes/scripts/audio/gen/__init__.py
Normal file
|
@ -54,7 +54,7 @@ def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusi
|
|||
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps))
|
||||
|
||||
|
||||
def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128, plt_spec=False, am=None):
|
||||
def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128, plt_spec=False):
|
||||
"""
|
||||
Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip.
|
||||
"""
|
||||
|
@ -62,11 +62,8 @@ def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, c
|
|||
dvae_model.eval()
|
||||
with torch.no_grad():
|
||||
mel = dvae_model.decode(mel_codes)[0]
|
||||
|
||||
if plt_spec:
|
||||
plot_spectrogram(mel[0].cpu())
|
||||
m=mel[:,:,:am.shape[-1]]
|
||||
print(torch.nn.MSELoss()(am,m))
|
||||
|
||||
# Pad MEL to multiples of 4096//spectrogram_compression_factor
|
||||
msl = mel.shape[-1]
|
|
@ -3,7 +3,7 @@ import argparse
|
|||
import torchaudio
|
||||
|
||||
from data.audio.unsupervised_audio_dataset import load_audio
|
||||
from scripts.audio.speech_synthesis_utils import do_spectrogram_diffusion, \
|
||||
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
|
||||
|
@ -19,17 +19,17 @@ def roundtrip_vocoding(dvae, vocoder, diffuser, clip, cond=None, plot_spec=False
|
|||
if plot_spec:
|
||||
plot_spectrogram(mel[0].cpu())
|
||||
codes = convert_mel_to_codes(dvae, mel)
|
||||
return do_spectrogram_diffusion(vocoder, dvae, diffuser, codes, cond, spectrogram_compression_factor=128, plt_spec=plot_spec, am=mel)
|
||||
return do_spectrogram_diffusion(vocoder, dvae, diffuser, codes, cond, spectrogram_compression_factor=128, plt_spec=plot_spec)
|
||||
|
||||
|
||||
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_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='Name of the diffusion model in opt.', default='X:\\dlas\\experiments\\train_diffusion_vocoder_with_cond_new_dvae_full\\models\\200_generator_ema.pth')
|
||||
parser.add_argument('-diffusion_model_path', type=str, help='Name of the diffusion model in opt.', 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('-input_file', type=str, help='Path to the input audio file.', default='Z:\\clips\\books1\\3_dchha04 Romancing The Tribes\\00036.wav')
|
||||
parser.add_argument('-cond', type=str, help='Path to the conditioning input audio file.', default=None)
|
||||
parser.add_argument('-cond', type=str, help='Path to the conditioning input audio file.', default='Z:\\clips\\books1\\3042_18_Holden__000000000\\00037.wav')
|
||||
args = parser.parse_args()
|
||||
|
||||
print("Loading DVAE..")
|
||||
|
@ -40,7 +40,10 @@ if __name__ == '__main__':
|
|||
print("Loading data..")
|
||||
diffuser = load_discrete_vocoder_diffuser()
|
||||
inp = load_audio(args.input_file, 22050).cuda()
|
||||
cond = None if args.cond is None else load_audio(args.cond, 22050).cuda()
|
||||
cond = inp if args.cond is None else load_audio(args.cond, 22050)
|
||||
if cond.shape[-1] > 44100+10000:
|
||||
cond = cond[:,10000:54100]
|
||||
cond = torchaudio.transforms.Resample(22050, 10025)(cond.cpu()).cuda()
|
||||
|
||||
print("Performing inference..")
|
||||
roundtripped = roundtrip_vocoding(dvae, diffusion, diffuser, inp, cond).cpu()
|
|
@ -61,12 +61,12 @@ class GaussianDiffusionInjector(Injector):
|
|||
|
||||
|
||||
def closest_multiple(inp, multiple):
|
||||
div = inp / multiple
|
||||
div = inp // multiple
|
||||
mod = inp % multiple
|
||||
if mod == 0:
|
||||
return inp
|
||||
else:
|
||||
return (div+1)*multiple
|
||||
return int((div+1)*multiple)
|
||||
|
||||
|
||||
# Performs inference using a network trained to predict a reverse diffusion process, which nets a image.
|
||||
|
|
Loading…
Reference in New Issue
Block a user