c65712426aa8fe18a6b40b71a9a6b2c9681e9ec2,TTS/bin/tune_wavegrad.py,,,#,15

Before Change

best_schedule = None
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):
    model.compute_noise_level(num_steps=args.num_iter, min_val=1e-6, max_val=1e-1, base_vals=base)
    for data in loader:
        mel, audio = data
        y_hat = model.inference(mel.cuda() if args.use_cuda else mel)

After Change

// setup optimization parameters
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_schedule = None
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):
    beta = exponents * base
    model.compute_noise_level(beta)
    for data in loader:
        mel, audio = data
        y_hat = model.inference(mel.cuda() if args.use_cuda else mel)

Instances