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formatting

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SWivid
2025-11-09 18:25:30 +08:00
parent bc15df2b57
commit d9a69452ce
1 changed files with 6 additions and 6 deletions
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12
src/f5_tts/model/backbones/dit.py
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@@ -66,18 +66,18 @@ class TextEmbedding(nn.Module):
valid_ind = torch.where(valid_mask)[0] valid_ind = torch.where(valid_mask)[0]
valid_data = text[0, valid_ind, :] # [valid_len, text_dim] valid_data = text[0, valid_ind, :] # [valid_len, text_dim]
base_repeat = audio_len // valid_len base_repeat = audio_len // valid_len
remainder = audio_len % valid_len remainder = audio_len % valid_len
indices = [] indices = []
for j in range(valid_len): for j in range(valid_len):
repeat_count = base_repeat + (1 if j >= valid_len - remainder else 0) repeat_count = base_repeat + (1 if j >= valid_len - remainder else 0)
indices.extend([j] * repeat_count) indices.extend([j] * repeat_count)
indices = torch.tensor(indices[:audio_len], device=text.device, dtype=torch.long) indices = torch.tensor(indices[:audio_len], device=text.device, dtype=torch.long)
upsampled = valid_data[indices] # [audio_len, text_dim] upsampled = valid_data[indices] # [audio_len, text_dim]
upsampled_text[0, :audio_len, :] = upsampled upsampled_text[0, :audio_len, :] = upsampled
return upsampled_text return upsampled_text
@@ -245,7 +245,7 @@ class DiT(nn.Module):
text_embed = self.text_embed(text, x.shape[1], drop_text=drop_text) text_embed = self.text_embed(text, x.shape[1], drop_text=drop_text)
else: else:
batch = x.shape[0] batch = x.shape[0]
seq_lens = audio_mask.sum(dim=1) # Calculate the actual sequence length for each sample seq_lens = audio_mask.sum(dim=1) # Calculate the actual sequence length for each sample
text_embed_list = [] text_embed_list = []
for i in range(batch): for i in range(batch):
text_embed_i = self.text_embed( text_embed_i = self.text_embed(
@@ -325,4 +325,4 @@ class DiT(nn.Module):
x = self.norm_out(x, t) x = self.norm_out(x, t)
output = self.proj_out(x) output = self.proj_out(x)
return output return output