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app.py

@@ -8,11 +8,8 @@ import gradio as gr
 import asyncio
 import tempfile
 import os
-import nest_asyncio
 import edge_tts
 
-nest_asyncio.apply()
-
 # High-quality neural voices per language
 VOICES = {
     "en": "en-US-GuyNeural",
@@ -43,6 +40,18 @@ async def _generate_async(text: str, voice: str, output_path: str) -> None:
     await communicate.save(output_path)
 
 
+def _run_async(coro):
+    """Run async coroutine, handling both fresh and existing event loops."""
+    try:
+        loop = asyncio.get_running_loop()
+        import concurrent.futures
+        with concurrent.futures.ThreadPoolExecutor() as pool:
+            future = pool.submit(asyncio.run, coro)
+            return future.result()
+    except RuntimeError:
+        return asyncio.run(coro)
+
+
 def generate_speech(text: str, language: str, voice_type: str) -> str:
     """Generate speech in the specified language."""
     if not text.strip():
@@ -54,7 +63,7 @@ def generate_speech(text: str, language: str, voice_type: str) -> str:
     voice = voices[language]
 
     output_path = tempfile.mktemp(suffix=".mp3")
-    asyncio.run(_generate_async(text, voice, output_path))
+    _run_async(_generate_async(text, voice, output_path))
 
     return output_path
 
@@ -72,7 +81,7 @@ def batch_generate(texts: str, language: str, voice_type: str):
     for i, segment in enumerate(segments):
         print(f"[{i+1}/{len(segments)}] {segment[:60]}...")
         output_path = tempfile.mktemp(suffix=f"_seg{i+1:03d}.mp3")
-        asyncio.run(_generate_async(segment, voice, output_path))
+        _run_async(_generate_async(segment, voice, output_path))
         results.append(output_path)
 
     return results