diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..538366641698917c322e2a6880543afe470bf449
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+++ b/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/src/data/lagrangian_datatools.py b/src/data/lagrangian_datatools.py
index d60356566d9eeefdf57dd086bfaeee5696defc7f..c5f4c19a691f607b9b175027c62c31a476cfa30c 100644
--- a/src/data/lagrangian_datatools.py
+++ b/src/data/lagrangian_datatools.py
@@ -75,6 +75,7 @@ class CollatorForDiffwave:
pass
def collate(self, minibatch):
+ # shape is (batch_size, trajectory_length)
trajectories = np.stack([record ['audio']for record in minibatch])
return {
'audio': torch.from_numpy(trajectories),
diff --git a/src/diffwave/learner.py b/src/diffwave/learner.py
index 554966e9a1d1e4ff10664b05252e4c51bf301ff3..2dfa424ac2f015a1c938c10e968aacc408c45a37 100644
--- a/src/diffwave/learner.py
+++ b/src/diffwave/learner.py
@@ -28,6 +28,17 @@ from params import AttrDict
def _nested_map(struct, map_fn):
+ '''
+ This function will dive into an structure until it finds a tensor, and then
+ send it to a device.
+ Example:
+ if struct is a dict like:
+ x = {"audio": Tensor(64,22000),
+ "spectrogram": Tensor(64,1024,128)}
+ and map_fn is a function that sends a tensor to a device, then the result is
+ x = {"audio": Tensor(64,22000).to(device),
+ "spectrogram": Tensor(64,1024,128).to(device)}
+ '''
if isinstance(struct, tuple):
return tuple(_nested_map(x, map_fn) for x in struct)
if isinstance(struct, list):
@@ -101,6 +112,8 @@ class DiffWaveLearner:
def train(self, max_steps=None):
device = next(self.model.parameters()).device
while True:
+ # number of epochs = max_steps / num_batches
+ # e.g. for max_steps = 100000 and num_batches = 1000, we have 100 epochs
for features in tqdm(self.dataset, desc=f'Epoch {self.step // len(self.dataset)}') if self.is_master else self.dataset:
if max_steps is not None and self.step >= max_steps:
# Save final checkpoint.
diff --git a/src/diffwave/model.py b/src/diffwave/model.py
index 0691fe3371c461d81e1cfa6130e1c3453f7ca3a9..387bf6f4025a9731cd0e9cd665b28360cd861352 100644
--- a/src/diffwave/model.py
+++ b/src/diffwave/model.py
@@ -37,6 +37,9 @@ def silu(x):
class DiffusionEmbedding(nn.Module):
+ '''
+ Sinusoidal embedding for diffusion step.
+ '''
def __init__(self, max_steps):
super().__init__()
self.register_buffer('embedding', self._build_embedding(max_steps), persistent=False)
@@ -147,7 +150,8 @@ class DiffWave(nn.Module):
def forward(self, audio, diffusion_step, spectrogram=None):
assert (spectrogram is None and self.spectrogram_upsampler is None) or \
(spectrogram is not None and self.spectrogram_upsampler is not None)
- x = audio.unsqueeze(1) # watch out for this, we can leave this to the dataloader actually
+ # watch out for this, we can leave this to the dataloader actually
+ x = audio.unsqueeze(1) # shape is (batch_size, 1, trajectory_length)
x = self.input_projection(x)
x = F.relu(x)