v1.1.8

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "f5-tts"
-version = "1.1.7"
+version = "1.1.8"
 description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
 readme = "README.md"
 license = {text = "MIT License"}
@@ -20,7 +20,7 @@ dependencies = [
     "click",
     "datasets",
     "ema_pytorch>=0.5.2",
-    "gradio>=3.45.2",
+    "gradio>=5.0.0",
     "hydra-core>=1.3.0",
     "jieba",
     "librosa",

src/f5_tts/model/backbones/dit.py

@@ -29,11 +29,16 @@ from f5_tts.model.modules import (
 
 
 class TextEmbedding(nn.Module):
-    def __init__(self, text_num_embeds, text_dim, mask_padding=True, conv_layers=0, conv_mult=2):
+    def __init__(
+        self, text_num_embeds, text_dim, mask_padding=True, average_upsampling=False, conv_layers=0, conv_mult=2
+    ):
         super().__init__()
         self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim)  # use 0 as filler token
 
         self.mask_padding = mask_padding  # mask filler and batch padding tokens or not
+        self.average_upsampling = average_upsampling  # zipvoice-style text late average upsampling (after text encoder)
+        if average_upsampling:
+            assert mask_padding, "text_embedding_average_upsampling requires text_mask_padding to be True"
 
         if conv_layers > 0:
             self.extra_modeling = True
@@ -45,11 +50,47 @@ class TextEmbedding(nn.Module):
         else:
             self.extra_modeling = False
 
-    def forward(self, text: int["b nt"], seq_len, drop_text=False):  # noqa: F722
+    def average_upsample_text_by_mask(self, text, text_mask, audio_mask):
+        batch, text_len, text_dim = text.shape
+
+        if audio_mask is None:
+            audio_mask = torch.ones_like(text_mask, dtype=torch.bool)
+        valid_mask = audio_mask & text_mask
+        audio_lens = audio_mask.sum(dim=1)  # [batch]
+        valid_lens = valid_mask.sum(dim=1)  # [batch]
+
+        upsampled_text = torch.zeros_like(text)
+
+        for i in range(batch):
+            audio_len = audio_lens[i].item()
+            valid_len = valid_lens[i].item()
+
+            if valid_len == 0:
+                continue
+
+            valid_ind = torch.where(valid_mask[i])[0]
+            valid_data = text[i, valid_ind, :]  # [valid_len, text_dim]
+
+            base_repeat = audio_len // valid_len
+            remainder = audio_len % valid_len
+
+            indices = []
+            for j in range(valid_len):
+                repeat_count = base_repeat + (1 if j >= valid_len - remainder else 0)
+                indices.extend([j] * repeat_count)
+
+            indices = torch.tensor(indices[:audio_len], device=text.device, dtype=torch.long)
+            upsampled = valid_data[indices]  # [audio_len, text_dim]
+
+            upsampled_text[i, :audio_len, :] = upsampled
+
+        return upsampled_text
+
+    def forward(self, text: int["b nt"], seq_len, drop_text=False, audio_mask: bool["b n"] | None = None):  # noqa: F722
         text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
         text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
         batch, text_len = text.shape[0], text.shape[1]
-        text = F.pad(text, (0, seq_len - text_len), value=0)
+        text = F.pad(text, (0, seq_len - text_len), value=0)  # (opt.) if not self.average_upsampling:
         if self.mask_padding:
             text_mask = text == 0
 
@@ -61,7 +102,7 @@ class TextEmbedding(nn.Module):
         # possible extra modeling
         if self.extra_modeling:
             # sinus pos emb
-            batch_start = torch.zeros((batch,), dtype=torch.long)
+            batch_start = torch.zeros((batch,), device=text.device, dtype=torch.long)
             pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
             text_pos_embed = self.freqs_cis[pos_idx]
             text = text + text_pos_embed
@@ -75,6 +116,9 @@ class TextEmbedding(nn.Module):
             else:
                 text = self.text_blocks(text)
 
+        if self.average_upsampling:
+            text = self.average_upsample_text_by_mask(text, ~text_mask, audio_mask)
+
         return text
 
 
@@ -113,6 +157,7 @@ class DiT(nn.Module):
         text_num_embeds=256,
         text_dim=None,
         text_mask_padding=True,
+        text_embedding_average_upsampling=False,
         qk_norm=None,
         conv_layers=0,
         pe_attn_head=None,
@@ -127,7 +172,11 @@ class DiT(nn.Module):
         if text_dim is None:
             text_dim = mel_dim
         self.text_embed = TextEmbedding(
-            text_num_embeds, text_dim, mask_padding=text_mask_padding, conv_layers=conv_layers
+            text_num_embeds,
+            text_dim,
+            mask_padding=text_mask_padding,
+            average_upsampling=text_embedding_average_upsampling,
+            conv_layers=conv_layers,
         )
         self.text_cond, self.text_uncond = None, None  # text cache
         self.input_embed = InputEmbedding(mel_dim, text_dim, dim)
@@ -190,19 +239,20 @@ class DiT(nn.Module):
         drop_audio_cond: bool = False,
         drop_text: bool = False,
         cache: bool = True,
+        audio_mask: bool["b n"] | None = None,  # noqa: F722
     ):
         seq_len = x.shape[1]
         if cache:
             if drop_text:
                 if self.text_uncond is None:
-                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True)
+                    self.text_uncond = self.text_embed(text, seq_len, drop_text=True, audio_mask=audio_mask)
                 text_embed = self.text_uncond
             else:
                 if self.text_cond is None:
-                    self.text_cond = self.text_embed(text, seq_len, drop_text=False)
+                    self.text_cond = self.text_embed(text, seq_len, drop_text=False, audio_mask=audio_mask)
                 text_embed = self.text_cond
         else:
-            text_embed = self.text_embed(text, seq_len, drop_text=drop_text)
+            text_embed = self.text_embed(text, seq_len, drop_text=drop_text, audio_mask=audio_mask)
 
         x = self.input_embed(x, cond, text_embed, drop_audio_cond=drop_audio_cond)
 
@@ -230,13 +280,19 @@ class DiT(nn.Module):
         # t: conditioning time, text: text, x: noised audio + cond audio + text
         t = self.time_embed(time)
         if cfg_infer:  # pack cond & uncond forward: b n d -> 2b n d
-            x_cond = self.get_input_embed(x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache)
-            x_uncond = self.get_input_embed(x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache)
+            x_cond = self.get_input_embed(
+                x, cond, text, drop_audio_cond=False, drop_text=False, cache=cache, audio_mask=mask
+            )
+            x_uncond = self.get_input_embed(
+                x, cond, text, drop_audio_cond=True, drop_text=True, cache=cache, audio_mask=mask
+            )
             x = torch.cat((x_cond, x_uncond), dim=0)
             t = torch.cat((t, t), dim=0)
             mask = torch.cat((mask, mask), dim=0) if mask is not None else None
         else:
-            x = self.get_input_embed(x, cond, text, drop_audio_cond=drop_audio_cond, drop_text=drop_text, cache=cache)
+            x = self.get_input_embed(
+                x, cond, text, drop_audio_cond=drop_audio_cond, drop_text=drop_text, cache=cache, audio_mask=mask
+            )
 
         rope = self.rotary_embed.forward_from_seq_len(seq_len)