forked from mrq/DL-Art-School
tfdpc_v3 inference
This commit is contained in:
James Betker
parent
7a9c4310e8
commit
42de09d983
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@ -1,20 +1,18 @@
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import itertools
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from time import time
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import os
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchaudio
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import torchvision
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from models.arch_util import ResBlock, AttentionBlock
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from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower
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from models.audio.music.music_quantizer2 import MusicQuantizer2
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from models.audio.tts.lucidrains_dvae import DiscreteVAE
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import TimestepBlock
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from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
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FeedForward
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from trainer.networks import register_model
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from utils.util import checkpoint, print_network
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from utils.util import checkpoint, print_network, load_audio
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class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
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@ -147,7 +145,7 @@ class TransformerDiffusionWithPointConditioning(nn.Module):
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def forward(self, x, timesteps, conditioning_input, conditioning_free=False):
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unused_params = []
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if conditioning_free:
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cond = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
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cond = self.unconditioned_embedding
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else:
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cond = conditioning_input
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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@ -243,7 +241,7 @@ class TransformerDiffusionWithConditioningEncoder(nn.Module):
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@register_model
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def register_tfdpc2(opt_net, opt):
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def register_tfdpc3(opt_net, opt):
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return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
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@ -258,11 +256,93 @@ def test_cheater_model():
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ts = torch.LongTensor([600, 600])
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# For music:
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model = TransformerDiffusionWithConditioningEncoder(model_channels=1024)
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model = TransformerDiffusionWithConditioningEncoder(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, num_heads=8, num_layers=24, dropout=0,
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unconditioned_percentage=.4)
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print_network(model)
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o = model(clip, ts, cl)
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pg = model.get_grad_norm_parameter_groups()
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def inference_tfdpc3_with_cheater():
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with torch.no_grad():
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os.makedirs('results/tfdpc_v3', exist_ok=True)
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#length = 40 * 22050 // 256 // 16
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samples = {'electronica1': load_audio('Y:\\split\\yt-music-eval\\00001.wav', 22050),
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'electronica2': load_audio('Y:\\split\\yt-music-eval\\00272.wav', 22050),
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'e_guitar': load_audio('Y:\\split\\yt-music-eval\\00227.wav', 22050),
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'creep': load_audio('Y:\\separated\\bt-music-3\\[2007] MTV Unplugged (Live) (Japan Edition)\\05 - Creep [Cover On Radiohead]\\00001\\no_vocals.wav', 22050),
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'rock1': load_audio('Y:\\separated\\bt-music-3\\2016 - Heal My Soul\\01 - Daze Of The Night\\00000\\no_vocals.wav', 22050),
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'kiss': load_audio('Y:\\separated\\bt-music-3\\KISS (2001) Box Set CD1\\02 Deuce (Demo Version)\\00000\\no_vocals.wav', 22050),
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'purp': load_audio('Y:\\separated\\bt-music-3\\Shades of Deep Purple\\11 Help (Alternate Take)\\00001\\no_vocals.wav', 22050),
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'western_stars': load_audio('Y:\\separated\\bt-music-3\\Western Stars\\01 Hitch Hikin\'\\00000\\no_vocals.wav', 22050),
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'silk': load_audio('Y:\\separated\\silk\\MonstercatSilkShowcase\\890\\00007\\no_vocals.wav', 22050),
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'long_electronica': load_audio('C:\\Users\\James\\Music\\longer_sample.wav', 22050),}
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for k, sample in samples.items():
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sample = sample.cuda()
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length = sample.shape[0]//256//16
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model = TransformerDiffusionWithConditioningEncoder(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, num_heads=8, num_layers=12, dropout=0,
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use_fp16=False, unconditioned_percentage=0).eval().cuda()
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model.load_state_dict(torch.load('x:/dlas/experiments/train_music_cheater_gen_v3/models/59000_generator_ema.pth'))
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from trainer.injectors.audio_injectors import TorchMelSpectrogramInjector
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spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000, 'true_normalization': True,
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'normalize': True, 'in': 'in', 'out': 'out'}, {}).cuda()
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ref_mel = spec_fn({'in': sample.unsqueeze(0)})['out']
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from trainer.injectors.audio_injectors import MusicCheaterLatentInjector
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cheater_encoder = MusicCheaterLatentInjector({'in': 'in', 'out': 'out'}, {}).cuda()
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ref_cheater = cheater_encoder({'in': ref_mel})['out']
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from models.diffusion.respace import SpacedDiffusion
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from models.diffusion.respace import space_timesteps
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from models.diffusion.gaussian_diffusion import get_named_beta_schedule
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diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [128]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
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conditioning_free=True, conditioning_free_k=1)
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# Conventional decoding method:
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gen_cheater = diffuser.ddim_sample_loop(model, (1,256,length), progress=True, model_kwargs={'true_cheater': ref_cheater})
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# Guidance decoding method:
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#mask = torch.ones_like(ref_cheater)
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#mask[:,:,15:-15] = 0
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#gen_cheater = diffuser.p_sample_loop_with_guidance(model, ref_cheater, mask, model_kwargs={'true_cheater': ref_cheater})
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# Just decode the ref.
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#gen_cheater = ref_cheater
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from models.audio.music.transformer_diffusion12 import TransformerDiffusionWithCheaterLatent
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diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
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conditioning_free=True, conditioning_free_k=1)
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wrap = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, prenet_channels=1024, input_vec_dim=256,
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prenet_layers=6, num_heads=8, num_layers=16, new_code_expansion=True,
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dropout=0, unconditioned_percentage=0).eval().cuda()
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wrap.load_state_dict(torch.load('x:/dlas/experiments/train_music_diffusion_tfd_cheater_from_scratch/models/56500_generator_ema.pth'))
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cheater_to_mel = wrap.diff
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gen_mel = diffuser.ddim_sample_loop(cheater_to_mel, (1,256,gen_cheater.shape[-1]*16), progress=True,
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model_kwargs={'codes': gen_cheater.permute(0,2,1)})
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torchvision.utils.save_image((gen_mel + 1)/2, f'results/tfdpc_v3/{k}.png')
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from utils.music_utils import get_mel2wav_v3_model
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m2w = get_mel2wav_v3_model().cuda()
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spectral_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
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conditioning_free=True, conditioning_free_k=1)
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from trainer.injectors.audio_injectors import denormalize_mel
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gen_mel_denorm = denormalize_mel(gen_mel)
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output_shape = (1,16,gen_mel_denorm.shape[-1]*256//16)
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gen_wav = spectral_diffuser.ddim_sample_loop(m2w, output_shape, model_kwargs={'codes': gen_mel_denorm})
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from trainer.injectors.audio_injectors import pixel_shuffle_1d
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gen_wav = pixel_shuffle_1d(gen_wav, 16)
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torchaudio.save(f'results/tfdpc_v3/{k}.wav', gen_wav.squeeze(1).cpu(), 22050)
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torchaudio.save(f'results/tfdpc_v3/{k}_ref.wav', sample.unsqueeze(0).cpu(), 22050)
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if __name__ == '__main__':
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test_cheater_model()
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#test_cheater_model()
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inference_tfdpc3_with_cheater()
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