Replace jieba pkg with rjieba - a jieba-rs Python binding

Fix Average Upsampling conflict logic, introduced from the previous batch inference fix.

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "f5-tts"
-version = "1.1.9"
+version = "1.1.10"
 description = "F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching"
 readme = "README.md"
 license = {text = "MIT License"}
@@ -22,13 +22,13 @@ dependencies = [
     "ema_pytorch>=0.5.2",
     "gradio>=5.0.0",
     "hydra-core>=1.3.0",
-    "jieba",
     "librosa",
     "matplotlib",
     "numpy<=1.26.4; python_version<='3.10'",
     "pydantic<=2.10.6",
     "pydub",
     "pypinyin",
+    "rjieba",
     "safetensors",
     "soundfile",
     "tomli",

src/f5_tts/model/backbones/dit.py

@@ -43,7 +43,7 @@ class TextEmbedding(nn.Module):
 
         if conv_layers > 0:
             self.extra_modeling = True
-            self.precompute_max_pos = 4096  # ~44s of 24khz audio
+            self.precompute_max_pos = 8192  # 8192 is ~87.38s of 24khz audio; 4096 is ~43.69s of 24khz audio
             self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
             self.text_blocks = nn.Sequential(
                 *[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
@@ -51,33 +51,29 @@ class TextEmbedding(nn.Module):
         else:
             self.extra_modeling = False
 
-    def average_upsample_text_by_mask(self, text, text_mask, audio_mask):
+    def average_upsample_text_by_mask(self, text, text_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]
+        audio_len = text_len  # cuz text already padded to same length as audio sequence
+        text_lens = text_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()
+            text_len = text_lens[i].item()
 
-            if valid_len == 0:
+            if text_len == 0:
                 continue
 
-            valid_ind = torch.where(valid_mask[i])[0]
-            valid_data = text[i, valid_ind, :]  # [valid_len, text_dim]
+            valid_ind = torch.where(text_mask[i])[0]
+            valid_data = text[i, valid_ind, :]  # [text_len, text_dim]
 
-            base_repeat = audio_len // valid_len
-            remainder = audio_len % valid_len
+            base_repeat = audio_len // text_len
+            remainder = audio_len % text_len
 
             indices = []
-            for j in range(valid_len):
-                repeat_count = base_repeat + (1 if j >= valid_len - remainder else 0)
+            for j in range(text_len):
+                repeat_count = base_repeat + (1 if j >= text_len - remainder else 0)
                 indices.extend([j] * repeat_count)
 
             indices = torch.tensor(indices[:audio_len], device=text.device, dtype=torch.long)
@@ -87,7 +83,7 @@ class TextEmbedding(nn.Module):
 
         return upsampled_text
 
-    def forward(self, text: int["b nt"], seq_len, drop_text=False, audio_mask: bool["b n"] | None = None):
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):
         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
         text = F.pad(text, (0, seq_len - text.shape[1]), value=0)  # (opt.) if not self.average_upsampling:
@@ -114,7 +110,7 @@ class TextEmbedding(nn.Module):
                 text = self.text_blocks(text)
 
         if self.average_upsampling:
-            text = self.average_upsample_text_by_mask(text, ~text_mask, audio_mask)
+            text = self.average_upsample_text_by_mask(text, ~text_mask)
 
         return text
 
@@ -247,17 +243,16 @@ class DiT(nn.Module):
     ):
         if self.text_uncond is None or self.text_cond is None or not cache:
             if audio_mask is None:
-                text_embed = self.text_embed(text, x.shape[1], drop_text=drop_text, audio_mask=audio_mask)
+                text_embed = self.text_embed(text, x.shape[1], drop_text=drop_text)
             else:
                 batch = x.shape[0]
-                seq_lens = audio_mask.sum(dim=1)
+                seq_lens = audio_mask.sum(dim=1)  # Calculate the actual sequence length for each sample
                 text_embed_list = []
                 for i in range(batch):
                     text_embed_i = self.text_embed(
                         text[i].unsqueeze(0),
-                        seq_lens[i].item(),
+                        seq_len=seq_lens[i].item(),
                         drop_text=drop_text,
-                        audio_mask=audio_mask,
                     )
                     text_embed_list.append(text_embed_i[0])
                 text_embed = pad_sequence(text_embed_list, batch_first=True, padding_value=0)

src/f5_tts/model/utils.py

@@ -7,7 +7,7 @@ import random
 from collections import defaultdict
 from importlib.resources import files
 
-import jieba
+import rjieba
 import torch
 from pypinyin import Style, lazy_pinyin
 from torch.nn.utils.rnn import pad_sequence
@@ -146,10 +146,6 @@ def get_tokenizer(dataset_name, tokenizer: str = "pinyin"):
 
 
 def convert_char_to_pinyin(text_list, polyphone=True):
-    if jieba.dt.initialized is False:
-        jieba.default_logger.setLevel(50)  # CRITICAL
-        jieba.initialize()
-
     final_text_list = []
     custom_trans = str.maketrans(
         {";": ",", "“": '"', "”": '"', "‘": "'", "’": "'"}
@@ -163,7 +159,7 @@ def convert_char_to_pinyin(text_list, polyphone=True):
     for text in text_list:
         char_list = []
         text = text.translate(custom_trans)
-        for seg in jieba.cut(text):
+        for seg in rjieba.cut(text):
             seg_byte_len = len(bytes(seg, "UTF-8"))
             if seg_byte_len == len(seg):  # if pure alphabets and symbols
                 if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":

src/f5_tts/runtime/triton_trtllm/Dockerfile.server

@@ -1,3 +1,3 @@
 FROM nvcr.io/nvidia/tritonserver:24.12-py3
-RUN pip install tritonclient[grpc] tensorrt-llm==0.16.0 torchaudio==2.5.1 jieba pypinyin librosa vocos
+RUN pip install tritonclient[grpc] tensorrt-llm==0.16.0 torchaudio==2.5.1 rjieba pypinyin librosa vocos
 WORKDIR /workspace

src/f5_tts/runtime/triton_trtllm/model_repo_f5_tts/f5_tts/1/model.py

@@ -26,7 +26,7 @@
 import json
 import os
 
-import jieba
+import rjieba
 import torch
 import torchaudio
 import triton_python_backend_utils as pb_utils
@@ -66,7 +66,7 @@ def convert_char_to_pinyin(reference_target_texts_list, polyphone=True):
     for text in reference_target_texts_list:
         char_list = []
         text = text.translate(custom_trans)
-        for seg in jieba.cut(text):
+        for seg in rjieba.cut(text):
             seg_byte_len = len(bytes(seg, "UTF-8"))
             if seg_byte_len == len(seg):  # if pure alphabets and symbols
                 if char_list and seg_byte_len > 1 and char_list[-1] not in " :'\"":

src/f5_tts/train/datasets/prepare_emilia.py

@@ -225,5 +225,5 @@ if __name__ == "__main__":
     # bad zh asr cnt        230435   (samples)
     # bad eh asr cnt         37217   (samples)
 
-    # vocab size may be slightly different due to jieba tokenizer and pypinyin (e.g. way of polyphoneme)
+    # vocab size may be slightly different due to rjieba tokenizer and pypinyin (e.g. way of polyphoneme)
     # please be careful if using pretrained model, make sure the vocab.txt is same

src/f5_tts/train/datasets/prepare_wenetspeech4tts.py

@@ -122,5 +122,5 @@ if __name__ == "__main__":
     #                           -            -        1459   (polyphone)
     # char   vocab size      5264         5219        5042
 
-    # vocab size may be slightly different due to jieba tokenizer and pypinyin (e.g. way of polyphoneme)
+    # vocab size may be slightly different due to rjieba tokenizer and pypinyin (e.g. way of polyphoneme)
     # please be careful if using pretrained model, make sure the vocab.txt is same