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Fix MDF evaluator for current generation of

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This commit is contained in:
James Betker 2022-06-12 14:41:06 -06:00
parent a3da7f186e
commit 0c95be1624
3 changed files with 29 additions and 29 deletions
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9
codes/models/audio/music/transformer_diffusion11.py
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@ -355,8 +355,7 @@ def register_transformer_diffusion11_with_ar_prior(opt_net, opt):
def test_quant_model():
clip = torch.randn(2, 256, 400)
cond = torch.randn(2, 256, 400)
clip = torch.randn(2, 100, 400)
ts = torch.LongTensor([600, 600])
"""
@ -371,19 +370,19 @@ def test_quant_model():
"""
# For TTS:
model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1024,
model = TransformerDiffusionWithQuantizer(in_channels=100, out_channels=200, model_channels=1024,
prenet_channels=1024, num_heads=4,
input_vec_dim=1024, num_layers=12, prenet_layers=10,
quantizer_dims=[1024,768,512], quantizer_codebook_size=64,
quantizer_codebook_groups=4,
dropout=.1)
quant_weights = torch.load('X:\\dlas\\experiments\\train_tts_quant_64\\models\\15500_generator.pth')
quant_weights = torch.load('X:\\dlas\\experiments\\train_tts_quant_128\\models\\4000_generator.pth')
model.quantizer.load_state_dict(quant_weights, strict=False)
torch.save(model.state_dict(), 'sample.pth')
print_network(model)
o = model(clip, ts, clip, cond)
o = model(clip, ts, clip)
model.get_grad_norm_parameter_groups()

17
codes/trainer/eval/audio_diffusion_fid.py
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@ -20,7 +20,7 @@ from models.clip.mel_text_clip import MelTextCLIP
from models.audio.tts.tacotron2 import text_to_sequence
from scripts.audio.gen.speech_synthesis_utils import load_discrete_vocoder_diffuser, wav_to_mel, load_speech_dvae, \
convert_mel_to_codes, load_univnet_vocoder, wav_to_univnet_mel, load_clvp
from trainer.injectors.audio_injectors import denormalize_mel, TorchMelSpectrogramInjector
from trainer.injectors.audio_injectors import denormalize_mel, TorchMelSpectrogramInjector, normalize_mel
from utils.util import ceil_multiple, opt_get, load_model_from_config, pad_or_truncate
@ -214,12 +214,9 @@ class AudioDiffusionFid(evaluator.Evaluator):
'conditioning_input': None,
'disable_diversity': True})
# denormalize mel
gen_mel = denormalize_mel(gen_mel)
gen_wav = self.local_modules['vocoder'].inference(gen_mel)
real_dec = self.local_modules['vocoder'].inference(mel)
return gen_wav.float(), real_dec, SAMPLE_RATE
gen_wav = self.local_modules['vocoder'].inference(denormalize_mel(gen_mel))
real_dec = self.local_modules['vocoder'].inference(denormalize_mel(mel))
return gen_wav.float(), real_dec, gen_mel, mel, SAMPLE_RATE
def load_projector(self):
"""
@ -337,12 +334,12 @@ if __name__ == '__main__':
if __name__ == '__main__':
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_speech_diffusion_from_ctc_und10\\train.yml', 'generator',
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_tts_diffusion_tfd11_quant\\train.yml', 'generator',
also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\train_speech_diffusion_from_ctc_und10\\models\\43000_generator_ema.pth').cuda()
load_path='X:\\dlas\\experiments\\train_tts_diffusion_tfd11_quant\\models\\14500_generator_ema.pth').cuda()
opt_eval = {'eval_tsv': 'Y:\\libritts\\test-clean\\transcribed-oco-realtext.tsv', 'diffusion_steps': 100,
'conditioning_free': False, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'tfd'}
'diffusion_schedule': 'cosine', 'diffusion_type': 'tfd'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval', 'step': 100, 'device': 'cuda', 'opt': {}}
eval = AudioDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())

32
codes/trainer/eval/music_diffusion_fid.py
View File

@ -48,6 +48,9 @@ class MusicDiffusionFid(evaluator.Evaluator):
self.diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [diffusion_steps]), model_mean_type='epsilon',
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule(diffusion_schedule, 4000),
conditioning_free=conditioning_free_diffusion_enabled, conditioning_free_k=conditioning_free_k)
self.spectral_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [100]), model_mean_type='epsilon',
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
conditioning_free=False, conditioning_free_k=1)
self.dev = self.env['device']
mode = opt_get(opt_eval, ['diffusion_type'], 'tts')
@ -106,7 +109,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
gen_mel_denorm = denormalize_mel(gen_mel)
output_shape = (1,16,audio.shape[-1]//16)
self.spec_decoder = self.spec_decoder.to(audio.device)
gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape,
gen_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'aligned_conditioning': gen_mel_denorm})
gen_wav = pixel_shuffle_1d(gen_wav, 16)
@ -127,14 +130,14 @@ class MusicDiffusionFid(evaluator.Evaluator):
# x = x.clamp(-s, s) / s
# return x
gen_mel = self.diffuser.p_sample_loop(self.model, mel_norm.shape, #denoised_fn=denoising_fn, clip_denoised=False,
model_kwargs={'truth_mel': mel,
'conditioning_input': torch.zeros_like(mel_norm[:,:,:390]),
model_kwargs={'truth_mel': mel_norm,
'conditioning_input': None,
'disable_diversity': True})
gen_mel_denorm = denormalize_mel(gen_mel)
output_shape = (1,16,audio.shape[-1]//16)
self.spec_decoder = self.spec_decoder.to(audio.device)
gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape,
gen_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'aligned_conditioning': gen_mel_denorm})
gen_wav = pixel_shuffle_1d(gen_wav, 16)
@ -160,7 +163,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
gen_mel_denorm = denormalize_mel(gen_mel)
output_shape = (1,16,audio.shape[-1]//16)
self.spec_decoder = self.spec_decoder.to(audio.device)
gen_wav = self.diffuser.p_sample_loop(self.spec_decoder, output_shape,
gen_wav = self.spectral_diffuser.p_sample_loop(self.spec_decoder, output_shape,
model_kwargs={'aligned_conditioning': gen_mel_denorm})
gen_wav = pixel_shuffle_1d(gen_wav, 16)
@ -236,7 +239,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
for i in tqdm(list(range(0, len(self.data), self.skip))):
path = self.data[(i + self.env['rank']) % len(self.data)]
audio = load_audio(path, 22050).to(self.dev)
audio = audio[:, :22050*10]
audio = audio[:, :100000]
sample, ref, sample_mel, ref_mel, sample_rate = self.diffusion_fn(audio)
gen_projections.append(self.project(sample, sample_rate).cpu()) # Store on CPU to avoid wasting GPU memory.
@ -266,16 +269,17 @@ class MusicDiffusionFid(evaluator.Evaluator):
if __name__ == '__main__':
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_ar_prior.yml', 'generator',
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd_quant.yml', 'generator',
also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\train_music_diffusion_ar_prior\\models\\22000_generator_ema.pth'
load_path='X:\\dlas\\experiments\\train_music_diffusion_tfd11\\models\\24000_generator_ema.pth'
).cuda()
opt_eval = {#'path': 'Y:\\split\\yt-music-eval',
'path': 'E:\\music_eval',
'diffusion_steps': 100,
opt_eval = {'path': 'Y:\\split\\yt-music-eval', # eval music, mostly electronica. :)
#'path': 'E:\\music_eval', # this is music from the training dataset, including a lot more variety.
'diffusion_steps': 200,
'conditioning_free': False, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'partial_from_codes_quant',
'partial_low': 128, 'partial_high': 192}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 504, 'device': 'cuda', 'opt': {}}
'diffusion_schedule': 'cosine', 'diffusion_type': 'from_codes_quant',
#'partial_low': 128, 'partial_high': 192
}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 600, 'device': 'cuda', 'opt': {}}
eval = MusicDiffusionFid(diffusion, opt_eval, env)
print(eval.perform_eval())