From b60c37b31ae0409de078d798c99fbfa5f187146d Mon Sep 17 00:00:00 2001
From: 0xCAFEBABE0 <gnornehc@gmail.com>
Date: Mon, 30 Dec 2024 10:48:43 +0800
Subject: [PATCH] fix(bug).when generating text that contains only punctuation
 marks or whitespace characters, the CPU usage reaches 100%, and the process
 crashes.

---
 cosyvoice/cli/frontend.py         | 6 +++++-
 cosyvoice/cli/model.py            | 5 -----
 cosyvoice/utils/common.py         | 7 -------
 cosyvoice/utils/frontend_utils.py | 7 +++++++
 4 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/cosyvoice/cli/frontend.py b/cosyvoice/cli/frontend.py
index 9885a0f..31926f0 100644
--- a/cosyvoice/cli/frontend.py
+++ b/cosyvoice/cli/frontend.py
@@ -31,7 +31,7 @@ except ImportError:
     from tn.chinese.normalizer import Normalizer as ZhNormalizer
     from tn.english.normalizer import Normalizer as EnNormalizer
     use_ttsfrd = False
-from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph
+from cosyvoice.utils.frontend_utils import contains_chinese, replace_blank, replace_corner_mark, remove_bracket, spell_out_number, split_paragraph, is_only_punctuation
 
 
 class CosyVoiceFrontEnd:
@@ -109,6 +109,10 @@ class CosyVoiceFrontEnd:
 
     def text_normalize(self, text, split=True):
         text = text.strip()
+        # When generating text that contains only punctuation marks or whitespace characters
+        # - Returning empty texts ensures consistent processing logic.
+        if is_only_punctuation(text):
+            return []
         if contains_chinese(text):
             if self.use_ttsfrd:
                 texts = [i["text"] for i in json.loads(self.frd.do_voicegen_frd(text))["sentences"]]
diff --git a/cosyvoice/cli/model.py b/cosyvoice/cli/model.py
index 5314e8b..b9f555b 100644
--- a/cosyvoice/cli/model.py
+++ b/cosyvoice/cli/model.py
@@ -19,7 +19,6 @@ from torch.nn import functional as F
 from contextlib import nullcontext
 import uuid
 from cosyvoice.utils.common import fade_in_out
-from cosyvoice.utils.common import is_only_punctuation
 
 
 class CosyVoiceModel:
@@ -146,10 +145,6 @@ class CosyVoiceModel:
             llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
             flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
             prompt_speech_feat=torch.zeros(1, 0, 80), stream=False, speed=1.0, **kwargs):
-        # When generating text that contains only punctuation marks or whitespace characters
-        # - Returning 10ms of silence ensures consistent processing logic.
-        if is_only_punctuation(text):
-            return {'tts_speech': torch.zeros(1, int(0.01 * 22050))}
         # this_uuid is used to track variables related to this inference thread
         this_uuid = str(uuid.uuid1())
         with self.lock:
diff --git a/cosyvoice/utils/common.py b/cosyvoice/utils/common.py
index f162cbe..b356f0c 100644
--- a/cosyvoice/utils/common.py
+++ b/cosyvoice/utils/common.py
@@ -20,7 +20,6 @@ from typing import List
 
 import numpy as np
 import torch
-import regex
 
 IGNORE_ID = -1
 
@@ -156,12 +155,6 @@ def set_all_random_seed(seed):
     torch.cuda.manual_seed_all(seed)
 
 
-def is_only_punctuation(text):
-    # Regular expression: Match strings that consist only of punctuation marks or are empty.
-    punctuation_pattern = r'^[\p{P}\p{S}]*$'
-    return bool(regex.fullmatch(punctuation_pattern, text))
-
-
 def mask_to_bias(mask: torch.Tensor, dtype: torch.dtype) -> torch.Tensor:
     assert mask.dtype == torch.bool
     assert dtype in [torch.float32, torch.bfloat16, torch.float16]
diff --git a/cosyvoice/utils/frontend_utils.py b/cosyvoice/utils/frontend_utils.py
index ab01a1f..ea1c9fc 100644
--- a/cosyvoice/utils/frontend_utils.py
+++ b/cosyvoice/utils/frontend_utils.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import re
+import regex
 chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]+')
 
 
@@ -127,3 +128,9 @@ def replace_blank(text: str):
         else:
             out_str.append(c)
     return "".join(out_str)
+
+
+def is_only_punctuation(text):
+    # Regular expression: Match strings that consist only of punctuation marks or are empty.
+    punctuation_pattern = r'^[\p{P}\p{S}]*$'
+    return bool(regex.fullmatch(punctuation_pattern, text))