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Adjustments to diffusion networks

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This commit is contained in:
James Betker 2022-01-30 16:14:06 -07:00
parent e58dab14c3
commit fbea6e8eac
5 changed files with 28 additions and 15 deletions
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7
codes/models/gpt_voice/unet_diffusion_tts5.py
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@ -178,6 +178,7 @@ class DiffusionTts(nn.Module):
scale_factor=2,
conditioning_inputs_provided=True,
time_embed_dim_multiplier=4,
transformer_depths=8,
nil_guidance_fwd_proportion=.3,
):
super().__init__()
@ -220,7 +221,7 @@ class DiffusionTts(nn.Module):
use_pos_emb=False,
attn_layers=Encoder(
dim=embedding_dim,
depth=8,
depth=transformer_depths,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
@ -292,7 +293,7 @@ class DiffusionTts(nn.Module):
use_pos_emb=False,
attn_layers=Encoder(
dim=ch,
depth=8,
depth=transformer_depths,
heads=num_heads,
ff_dropout=dropout,
attn_dropout=dropout,
@ -301,8 +302,6 @@ class DiffusionTts(nn.Module):
rotary_pos_emb=True,
)
)
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,

4
codes/scripts/audio/gen/speech_synthesis_utils.py
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@ -46,12 +46,12 @@ def load_gpt_conditioning_inputs_from_directory(path, num_candidates=3, sample_r
return torch.stack(related_mels, dim=0)
def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200):
def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, schedule='linear'):
"""
Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
"""
return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon',
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps))
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule(schedule, trained_diffusion_steps))
def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128, plt_spec=False):

29
codes/scripts/audio/gen/use_diffuse_tts.py
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@ -19,19 +19,32 @@ def ceil_multiple(base, multiple):
if __name__ == '__main__':
conditioning_clips = {
# Male
'simmons': 'Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav',
'carlin': 'Y:\\clips\\books1\\12_dchha13 Bubonic Nukes\\00097.wav',
'entangled': 'Y:\\clips\\books1\\3857_25_The_Entangled_Bank__000000000\\00123.wav',
'snowden': 'Y:\\clips\\books1\\7658_Edward_Snowden_-_Permanent_Record__000000004\\00027.wav',
# Female
'the_doctor': 'Y:\\clips\\books2\\37062___The_Doctor__000000003\\00206.wav',
'puppy': 'Y:\\clips\\books2\\17830___3_Puppy_Kisses__000000002\\00046.wav',
'adrift': 'Y:\\clips\\books2\\5608_Gear__W_Michael_-_Donovan_1-5_(2018-2021)_(book_4_Gear__W_Michael_-_Donovan_5_-_Adrift_(2021)_Gear__W_Michael_-_Adrift_(Donovan_5)_—_82__000000000\\00019.wav',
}
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('-opt', type=str, help='Path to options YAML file used to train the diffusion model', default='X:\\dlas\\experiments\\train_diffusion_tts5_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('-diffusion_model_path', type=str, help='Path to saved model weights', default='X:\\dlas\\experiments\\train_diffusion_tts5_medium\\models\\14500_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('-cond', type=str, help='Type of conditioning voice', default='adrift')
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('-diffusion_schedule', type=str, help='Type of diffusion schedule that was used', default='cosine')
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')
parser.add_argument('-sample_rate', type=int, help='Model sample rate', default=5500)
parser.add_argument('-cond_sample_rate', type=int, help='Conditioning sample rate', default=5500)
parser.add_argument('-device', type=str, help='Device to run on', default='cuda')
args = parser.parse_args()
os.makedirs(args.output_path, exist_ok=True)
@ -42,8 +55,8 @@ if __name__ == '__main__':
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)
diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=args.diffusion_steps, schedule=args.diffusion_schedule)
cond = load_audio(conditioning_clips[args.cond], args.cond_sample_rate).to(args.device)
if cond.shape[-1] > 88000:
cond = cond[:,:88000]

2
codes/train.py
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@ -299,7 +299,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_tts5_medium.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_diffusion_tts_experimental_fp16/train_diffusion_tts.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()

1
codes/trainer/steps.py
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@ -145,6 +145,7 @@ class ConfigurableStep(Module):
opt = ZeroRedundancyOptimizer(params_weights, optimizer_class=torch.optim.AdamW, lr=opt_config['lr'],
weight_decay=opt_get(opt_config, ['weight_decay'], 1e-2),
betas=(opt_get(opt_config, ['beta1'], .9), opt_get(opt_config, ['beta2'], .999)))
opt.param_groups[0]['initial_lr'] = opt_config['lr']
opt._group_names = []
elif self.step_opt['optimizer'] == 'lars':
from trainer.optimizers.larc import LARC