music joiner checkin
This commit is contained in:
James Betker
parent
0824708dc7
commit
625d7b6f38
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@ -16,82 +16,109 @@ 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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def join_music(clip1, clip1_cut, clip2, clip2_cut, mix_time, results_dir):
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def join_music_with_cheaters(clip1_cheater, clip2_cheater, results_dir):
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clip1_leadin = clip1_cheater[:,:,-60:]
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clip1_cheater = clip1_cheater[:,:,:-60]
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clip2_leadin = clip2_cheater[:,:,:60]
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clip2_cheater = clip2_cheater[:,:,60:]
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"""
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# Original model
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model = TransformerDiffusionWithPointConditioning(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, time_proj=True).eval().cuda()
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model.load_state_dict(torch.load('x:/dlas/experiments/train_music_cheater_gen_v5/models/206000_generator_ema.pth'))
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diffusion_type = 'linear'
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"""
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model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, num_heads=8, num_layers=32, dropout=0,
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use_fp16=False, unconditioned_percentage=0, time_proj=False,
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new_cond=True, regularization=False).eval().cuda()
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model.load_state_dict(torch.load('x:/dlas/experiments/train_music_cheater_gen_v5_cosine_40_lyr/models/64000_generator_ema.pth'))
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diffusion_type = 'cosine'
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diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [256]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule(diffusion_type, 4000),
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conditioning_free=True, conditioning_free_k=2)
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inp = torch.cat([clip1_leadin, torch.zeros(1, 256, 240, device='cuda'), clip2_leadin], dim=-1)
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mask = torch.ones_like(inp)
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mask[:, :, 60:-60] = 0
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gen_cheater = diffuser.ddim_sample_loop_with_guidance(model, inp, mask, # causal=True, causal_slope=4,
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model_kwargs={'cond_left': clip1_cheater,
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'cond_right': clip2_cheater})
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cheater_to_mel = get_cheater_decoder().diff.cuda()
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cheater_decoder_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [64]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse',
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betas=get_named_beta_schedule('linear', 4000),
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conditioning_free=True, conditioning_free_k=1)
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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',
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betas=get_named_beta_schedule('linear', 4000),
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conditioning_free=True, conditioning_free_k=1)
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MAX_CONTEXT = 30 * 22050 // 4096
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chunks = torch.split(gen_cheater, MAX_CONTEXT, dim=-1)
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gen_wavs = []
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for i, chunk_cheater in enumerate(tqdm(chunks)):
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gen_mel = cheater_decoder_diffuser.ddim_sample_loop(cheater_to_mel, (1, 256, chunk_cheater.shape[-1] * 16),
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progress=True,
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model_kwargs={'codes': chunk_cheater.permute(0, 2, 1)})
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torchvision.utils.save_image((gen_mel + 1) / 2, f'{results_dir}/mel_{i}.png')
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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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wav = spectral_diffuser.ddim_sample_loop(m2w, output_shape, progress=True,
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model_kwargs={'codes': gen_mel_denorm})
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gen_wavs.append(pixel_shuffle_1d(wav, 16))
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gen_wav = torch.cat(gen_wavs, dim=-1)
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torchaudio.save(f'{results_dir}/out.wav', gen_wav.squeeze(1).cpu(), 22050)
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def join_music(clip1, clip2, results_dir):
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with torch.no_grad():
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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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cheater_encoder = MusicCheaterLatentInjector({'in': 'in', 'out': 'out'}, {}).cuda()
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"""
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# Original model
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model = TransformerDiffusionWithPointConditioning(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, time_proj=True).eval().cuda()
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model.load_state_dict(torch.load('x:/dlas/experiments/train_music_cheater_gen_v5/models/206000_generator_ema.pth'))
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diffusion_type = 'linear'
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"""
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model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
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contraction_dim=512, num_heads=8, num_layers=32, dropout=0,
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use_fp16=False, unconditioned_percentage=0, time_proj=False,
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new_cond=True, regularization=False).eval().cuda()
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model.load_state_dict(torch.load('x:/dlas/experiments/train_music_cheater_gen_v5_cosine_40_lyr/models/40000_generator_ema.pth'))
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diffusion_type = 'cosine'
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diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [256]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule(diffusion_type, 4000),
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conditioning_free=True, conditioning_free_k=1)
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clip1 = load_audio(clip1, 22050).cuda()
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clip1_mel = spec_fn({'in': clip1.unsqueeze(0)})['out']
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clip1_cheater = cheater_encoder({'in': clip1_mel})['out']
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clip1_leadin = clip1_cheater[:,:,-60:]
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clip1_cheater = clip1_cheater[:,:,:-60]
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clip2 = load_audio(clip2, 22050).cuda()
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clip2_mel = spec_fn({'in': clip2.unsqueeze(0)})['out']
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clip2_cheater = cheater_encoder({'in': clip2_mel})['out']
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clip2_leadin = clip2_cheater[:,:,:60]
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clip2_cheater = clip2_cheater[:,:,60:]
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join_music_with_cheaters(clip1_cheater, clip2_cheater, results_dir)
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inp = torch.cat([clip1_leadin, torch.zeros(1,256,240, device='cuda'), clip2_leadin], dim=-1)
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mask = torch.ones_like(inp)
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mask[:,:,60:-60] = 0
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gen_cheater = diffuser.ddim_sample_loop_with_guidance(model, inp, mask, # causal=True, causal_slope=4,
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model_kwargs={'cond_left': clip1_cheater, 'cond_right': clip2_cheater})
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cheater_to_mel = get_cheater_decoder().diff.cuda()
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cheater_decoder_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [64]), 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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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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MAX_CONTEXT = 30 * 22050 // 4096
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chunks = torch.split(gen_cheater, MAX_CONTEXT, dim=-1)
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gen_wavs = []
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for i, chunk_cheater in enumerate(tqdm(chunks)):
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gen_mel = cheater_decoder_diffuser.ddim_sample_loop(cheater_to_mel, (1,256,chunk_cheater.shape[-1]*16), progress=True,
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model_kwargs={'codes': chunk_cheater.permute(0,2,1)})
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torchvision.utils.save_image((gen_mel + 1)/2, f'{results_dir}/mel_{i}.png')
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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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wav = spectral_diffuser.ddim_sample_loop(m2w, output_shape, progress=True, model_kwargs={'codes': gen_mel_denorm})
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gen_wavs.append(pixel_shuffle_1d(wav, 16))
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gen_wav = torch.cat(gen_wavs, dim=-1)
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torchaudio.save(f'{results_dir}/out.wav', gen_wav.squeeze(1).cpu(), 22050)
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if __name__ == '__main__':
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results_dir = '../results/audio_joiner'
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#clip1 = 'Y:\\sources\\music\\manual_podcasts_music\\2\\The Glitch Mob - Discography\\2014 - Love, Death Immortality\\2. Our Demons (feat. Aja Volkman).mp3'
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clip1 = 'Y:\\separated\\bt-music-5\\[2002] Gutterflower\\02 - Think About Me\\00000\\no_vocals.wav'
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clip1_cut = 35 # Seconds
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#clip2 = 'Y:\\sources\\music\\manual_podcasts_music\\2\\The Glitch Mob - Discography\\2014 - Love, Death Immortality\\9. Carry The Sun.mp3'
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clip2 = 'Y:\\separated\\bt-music-5\\[2002] Gutterflower\\02 - Think About Me\\00003\\no_vocals.wav'
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clip2_cut = 1
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mix_time = 10
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"""
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things_to_try = {
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'goo': ('Y:\\separated\\bt-music-5\\[2002] Gutterflower\\02 - Think About Me', 0),
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'sm1': ('Y:\\separated\\silk\\MonstercatSilkShowcase\\910', 79),
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'sm2': ('Y:\\separated\\silk\\MonstercatSilkShowcase\\1025', 105),
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'sm3': ('Y:\\separated\\silk\\MonstercatSilkShowcase\\1026', 43),
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'sm4': ('Y:\\separated\\silk\\MonstercatSilkShowcase\\1026', 77),
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'sm5': ('Y:\\separated\\silk\\MonstercatSilkShowcase\\1027', 8),
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'sm6': ('Y:\\separated\\silk\\MonstercatSilkShowcase\\1027', 28),
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'sm6': ('Y:\\separated\\silk\\MonstercatSilkShowcase\\1017', 90),
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'tron': ('Y:\\separated\\bt-music-2\\2011 - TRON Legacy - Translucence (EP) - (320 kbps)\\01 Derezzed', 0),
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'lateralus': ('Y:\\separated\\bt-music-2\\Lateralus\\09 - lateralus\\00011', 11),
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'streets_have_no_name': ('Y:\\separated\\bt-music-2\\U2 - (1987) The Joshua Tree\\01-Where The Streets Have No Name', 1),
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'shinra': ('Y:\\separated\\bt-music-1\\final_fantasy_vii_soundtrack\\20-Infiltrating Shinra Tower', 1),
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'bombing_run': ('Y:\\separated\\bt-music-1\\final_fantasy_vii_soundtrack\\02-Opening ~ Bombing Mission', 2),
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'machine_gun': ('Y:\\separated\\bt-music-1\\ff8-fithos_lusec_wecos_vinosec-1999\\08 - The Man with the Machine Gun', 2),
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}
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for k, v in things_to_try.items():
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results_dir = f'../results/audio_joiner/{k}'
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src_path, start = v
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clip1 = f'{src_path}\\{start:05d}\\no_vocals.wav'
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clip2 = f'{src_path}\\{(start+2):05d}\\no_vocals.wav'
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os.makedirs(results_dir, exist_ok=True)
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join_music(clip1, clip2, results_dir)
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"""
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results_dir = f'../results/audio_joiner/machine_gun_from_cheater'
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os.makedirs(results_dir, exist_ok=True)
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join_music(clip1, clip1_cut, clip2, clip2_cut, mix_time, results_dir)
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cheater = torch.tensor(np.load('Y:\\separated\\large_mel_cheaters\\bt-music-1\\ff8-fithos_lusec_wecos_vinosec-1999\\08 - The Man with the Machine Gun\\0.npz')['arr_0']).cuda()
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join_music_with_cheaters(cheater[:,:,:230], cheater[:,:,-230:], results_dir)
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