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change noise scheduling for wavegrad. Compute beta values externally to enable better flexibility

This commit is contained in:
erogol 2020-11-14 13:01:10 +01:00
parent 5a59467f34
commit c65712426a
3 changed files with 12 additions and 11 deletions
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10
TTS/bin/tune_wavegrad.py
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@ -59,11 +59,13 @@ if args.use_cuda:
# setup optimization parameters # setup optimization parameters
base_values = sorted(np.random.uniform(high=10, size=args.search_depth)) base_values = sorted(np.random.uniform(high=10, size=args.search_depth))
exponents = 10 ** np.linspace(-6, -2, num=args.num_iter)
best_error = float('inf') best_error = float('inf')
best_schedule = None best_schedule = None
total_search_iter = len(base_values)**args.num_iter total_search_iter = len(base_values)**args.num_iter
for base in tqdm(cartesian_product(base_values, repeat=args.num_iter), total=total_search_iter): for base in tqdm(cartesian_product(base_values, repeat=args.num_iter), total=total_search_iter):
model.compute_noise_level(num_steps=args.num_iter, min_val=1e-6, max_val=1e-1, base_vals=base) beta = exponents * base
model.compute_noise_level(beta)
for data in loader: for data in loader:
mel, audio = data mel, audio = data
y_hat = model.inference(mel.cuda() if args.use_cuda else mel) y_hat = model.inference(mel.cuda() if args.use_cuda else mel)
@ -78,11 +80,11 @@ for base in tqdm(cartesian_product(base_values, repeat=args.num_iter), total=tot
mel_hat.append(torch.from_numpy(m)) mel_hat.append(torch.from_numpy(m))
mel_hat = torch.stack(mel_hat) mel_hat = torch.stack(mel_hat)
mse = torch.sum((mel - mel_hat) ** 2) mse = torch.sum((mel - mel_hat) ** 2).mean()
if mse.item() < best_error: if mse.item() < best_error:
best_error = mse.item() best_error = mse.item()
best_schedule = {'num_steps': args.num_iter, 'min_val':1e-6, 'max_val':1e-1, 'base_vals':base} best_schedule = {'beta': beta}
print(" > Found a better schedule.") print(f" > Found a better schedule. - MSE: {mse.item()}")
np.save(args.output_path, best_schedule) np.save(args.output_path, best_schedule)

2
TTS/vocoder/configs/wavegrad_libritts.json
View File

@ -72,6 +72,8 @@
// TRAINING // TRAINING
"batch_size": 96, // Batch size for training. "batch_size": 96, // Batch size for training.
// NOISE SCHEDULE PARAMS - Only effective at training time.
"train_noise_schedule":{ "train_noise_schedule":{
"min_val": 1e-6, "min_val": 1e-6,
"max_val": 1e-2, "max_val": 1e-2,

11
TTS/vocoder/models/wavegrad.py
View File

@ -79,8 +79,8 @@ class Wavegrad(nn.Module):
return x return x
def load_noise_schedule(self, path): def load_noise_schedule(self, path):
sched = np.load(path, allow_pickle=True).item() beta = np.load(path, allow_pickle=True).item()['beta']
self.compute_noise_level(**sched) self.compute_noise_level(beta)
@torch.no_grad() @torch.no_grad()
def inference(self, x, y_n=None): def inference(self, x, y_n=None):
@ -113,16 +113,13 @@ class Wavegrad(nn.Module):
noisy_audio = noise_scale * y_0 + (1.0 - noise_scale**2)**0.5 * noise noisy_audio = noise_scale * y_0 + (1.0 - noise_scale**2)**0.5 * noise
return noise.unsqueeze(1), noisy_audio.unsqueeze(1), noise_scale[:, 0] return noise.unsqueeze(1), noisy_audio.unsqueeze(1), noise_scale[:, 0]
def compute_noise_level(self, num_steps, min_val, max_val, base_vals=None): def compute_noise_level(self, beta):
"""Compute noise schedule parameters""" """Compute noise schedule parameters"""
beta = np.linspace(min_val, max_val, num_steps) self.num_steps = len(beta)
if base_vals is not None:
beta *= base_vals
alpha = 1 - beta alpha = 1 - beta
alpha_hat = np.cumprod(alpha) alpha_hat = np.cumprod(alpha)
noise_level = np.concatenate([[1.0], alpha_hat ** 0.5], axis=0) noise_level = np.concatenate([[1.0], alpha_hat ** 0.5], axis=0)
self.num_steps = num_steps
# pylint: disable=not-callable # pylint: disable=not-callable
self.beta = torch.tensor(beta.astype(np.float32)) self.beta = torch.tensor(beta.astype(np.float32))
self.alpha = torch.tensor(alpha.astype(np.float32)) self.alpha = torch.tensor(alpha.astype(np.float32))