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This commit is contained in:
James Betker 2022-05-06 21:56:49 -06:00
parent f541610256
commit 6c8032b4be
3 changed files with 30 additions and 36 deletions
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6
codes/models/audio/music/music_gen_fill_gaps.py
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@ -117,8 +117,8 @@ class MusicGenerator(nn.Module):
layer_drop=.1,
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
# Masking parameters.
time_mask_percent_max=.4,
frequency_mask_percent_max=.4,
frequency_mask_percent_max=0,
time_mask_percent_max=0,
):
super().__init__()
@ -172,7 +172,7 @@ class MusicGenerator(nn.Module):
def do_masking(self, truth):
b, c, s = truth.shape
mask = torch.ones_like(truth)
if random.random() < .5:
if self.frequency_mask_percent_mask > 0:
# Frequency mask
cs = random.randint(0, c-10)
ce = min(c-1, cs+random.randint(1, int(self.frequency_mask_percent_mask*c)))

6
codes/models/clip/contrastive_audio.py
View File

@ -219,6 +219,12 @@ class ContrastiveAudio(nn.Module):
def update_for_step(self, step, __):
self.to_latent2.weight.data = self.to_latent2.weight.data * .99 + self.to_latent.weight.data * .01
def project(self, mel):
h1 = self.emb(mel).permute(0, 2, 1)
h1 = self.transformer(h1)
h1 = self.to_latent(h1)
return h1
def forward(
self,
mel_input1,

54
codes/trainer/eval/music_diffusion_fid.py
View File

@ -17,6 +17,7 @@ from data.audio.paired_voice_audio_dataset import load_tsv_aligned_codes
from data.audio.unsupervised_audio_dataset import load_audio
from data.audio.voice_tokenizer import VoiceBpeTokenizer
from models.audio.music.unet_diffusion_waveform_gen import DiffusionWaveformGen
from models.clip.contrastive_audio import ContrastiveAudio
from models.clip.mel_text_clip import MelTextCLIP
from models.audio.tts.tacotron2 import text_to_sequence
from models.diffusion.gaussian_diffusion import get_named_beta_schedule
@ -58,7 +59,9 @@ class MusicDiffusionFid(evaluator.Evaluator):
num_heads=8,
dropout=0, kernel_size=3, scale_factor=2, time_embed_dim_multiplier=4, unconditioned_percentage=0)
self.spec_decoder.load_state_dict(torch.load('../experiments/music_waveform_gen.pth', map_location=torch.device('cpu')))
self.local_modules = {'spec_decoder': self.spec_decoder}
self.projector = ContrastiveAudio(model_dim=512, transformer_heads=8, dropout=0, encoder_depth=8, mel_channels=256)
#self.projector.load_state_dict(torch.load('../experiments/music_eval_projector.pth', map_location=torch.device('cpu')))
self.local_modules = {'spec_decoder': self.spec_decoder, 'projector': self.projector}
if mode == 'spec_decode':
self.diffusion_fn = self.perform_diffusion_spec_decode
@ -127,20 +130,11 @@ class MusicDiffusionFid(evaluator.Evaluator):
return gen, real_resampled, sample_rate
def load_projector(self):
# TODO: implement for music.
model = MelTextCLIP(dim_text=512, dim_latent=512, dim_speech=512, num_text_tokens=148, text_enc_depth=8,
text_seq_len=400, text_heads=8, speech_enc_depth=10, speech_heads=8, speech_seq_len=1000,
text_mask_percentage=.15, voice_mask_percentage=.15)
weights = torch.load('../experiments/clip_text_to_voice_for_speech_fid.pth')
model.load_state_dict(weights)
return model
def project(self, projector, sample, sample_rate):
# TODO: implement for music.
def project(self, sample, sample_rate):
sample = torchaudio.functional.resample(sample, sample_rate, 22050)
mel = wav_to_mel(sample)
return projector.get_speech_projection(mel).squeeze(0) # Getting rid of the batch dimension means it's just [hidden_dim]
mel = self.spec_fn({'in': sample})['out']
projection = self.projector.project(mel)
return projection.squeeze(0) # Getting rid of the batch dimension means it's just [hidden_dim]
def compute_frechet_distance(self, proj1, proj2):
# I really REALLY FUCKING HATE that this is going to numpy. Why does "pytorch_fid" operate in numpy land. WHY?
@ -156,41 +150,35 @@ class MusicDiffusionFid(evaluator.Evaluator):
save_path = osp.join(self.env['base_path'], "../", "audio_eval", str(self.env["step"]))
os.makedirs(save_path, exist_ok=True)
#projector = self.load_projector().to(self.env['device'])
#projector.eval()
self.projector = self.projector.to(self.dev)
self.projector.eval()
# Attempt to fix the random state as much as possible. RNG state will be restored before returning.
rng_state = torch.get_rng_state()
torch.manual_seed(5)
self.model.eval()
frechet_distance = 0
with torch.no_grad():
gen_projections = []
real_projections = []
for i in tqdm(list(range(0, len(self.data), self.skip))):
path = self.data[i + self.env['rank']]
audio = load_audio(path, 22050).to(self.dev)
mel = self.spec_fn({'in': audio})['out']
mel_norm = (mel + mel.min().abs())
mel_norm = mel_norm / mel_norm.max(dim=-1, keepdim=True).values
torchvision.utils.save_image(mel_norm.unsqueeze(1), 'mel.png')
audio = audio[:, :22050*5]
sample, ref, sample_rate = self.diffusion_fn(audio)
#gen_projections.append(self.project(projector, sample, sample_rate).cpu()) # Store on CPU to avoid wasting GPU memory.
#real_projections.append(self.project(projector, ref, sample_rate).cpu())
gen_projections.append(self.project(sample, sample_rate).cpu()) # Store on CPU to avoid wasting GPU memory.
real_projections.append(self.project(ref, sample_rate).cpu())
torchaudio.save(os.path.join(save_path, f"{self.env['rank']}_{i}_gen.wav"), sample.squeeze(0).cpu(), sample_rate)
torchaudio.save(os.path.join(save_path, f"{self.env['rank']}_{i}_real.wav"), ref.cpu(), sample_rate)
#gen_projections = torch.stack(gen_projections, dim=0)
#real_projections = torch.stack(real_projections, dim=0)
#frechet_distance = torch.tensor(self.compute_frechet_distance(gen_projections, real_projections), device=self.env['device'])
gen_projections = torch.stack(gen_projections, dim=0)
real_projections = torch.stack(real_projections, dim=0)
frechet_distance = torch.tensor(self.compute_frechet_distance(gen_projections, real_projections), device=self.env['device'])
#if distributed.is_initialized() and distributed.get_world_size() > 1:
# distributed.all_reduce(frechet_distance)
# frechet_distance = frechet_distance / distributed.get_world_size()
# distributed.all_reduce(intelligibility_loss)
# intelligibility_loss = intelligibility_loss / distributed.get_world_size()
if distributed.is_initialized() and distributed.get_world_size() > 1:
distributed.all_reduce(frechet_distance)
frechet_distance = frechet_distance / distributed.get_world_size()\
self.model.train()
torch.set_rng_state(rng_state)
@ -205,8 +193,8 @@ class MusicDiffusionFid(evaluator.Evaluator):
if __name__ == '__main__':
diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_gap_filler.yml', 'generator',
also_load_savepoint=False,
load_path='X:\\dlas\\experiments\\train_music_gap_filler\\models\\5000_generator.pth').cuda()
opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 100,
load_path='X:\\dlas\\experiments\\train_music_gap_filler\\models\\14000_generator.pth').cuda()
opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 500,
'conditioning_free': False, 'conditioning_free_k': 1,
'diffusion_schedule': 'linear', 'diffusion_type': 'gap_fill_freq'}
env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 2, 'device': 'cuda', 'opt': {}}