Update app.py

app.py

@@ -6,7 +6,7 @@ import random
 import torch
 
 IS_DUPLICATE = not os.getenv('SPACE_ID', '').startswith('hexgrad/')
-CHAR_LIMIT = None if IS_DUPLICATE else 500000
+CHAR_LIMIT = None
 
 CUDA_AVAILABLE = torch.cuda.is_available()
 models = {gpu: KModel().to('cuda' if gpu else 'cpu').eval() for gpu in [False] + ([True] if CUDA_AVAILABLE else [])}
@@ -164,8 +164,8 @@ with gr.Blocks() as generate_tab:
 
 BANNER_TEXT = '''
 [***Kokoro*** **is an open-weight TTS model with 82 million parameters.**](https://huggingface.co/hexgrad/Kokoro-82M)
-This is our work on Kokoro TTS [**V1 Model GPU**](https://shukdevdatta123-kokoro-tts-translate-gpu.hf.space) and the next version Kokoro TTS [**V2 Model CPU**](https://shukdevdatta123-kokoro-tts.hf.space).
-If you would like to use our V2 Model with GPU, then go to this [link](https://colab.research.google.com/drive/1DIpBzJSBBeTcpkyxkHcpngLumMapEWQz?usp=sharing).
+As of January 31st, 2025, Kokoro was the most-liked [**TTS model**](https://huggingface.co/models?pipeline_tag=text-to-speech&sort=likes) and the most-liked [**TTS space**](https://huggingface.co/spaces?sort=likes&search=tts) on Hugging Face.
+This demo only showcases English, but you can directly use the model to access other languages.
 '''
 
 API_OPEN = os.getenv('SPACE_ID') != 'hexgrad/Kokoro-TTS'
@@ -190,7 +190,7 @@ with gr.Blocks() as app:
         with gr.Column():
             gr.TabbedInterface([generate_tab], ['Generate'])
     random_btn.click(fn=get_random_text, inputs=[voice], outputs=[text], api_name=API_NAME)
-    generate_btn.click(fn=generate_all, inputs=[text, voice, speed, use_gpu], outputs=[out_audio, out_ps], api_name=API_NAME)
+    generate_btn.click(fn=generate_first, inputs=[text, voice, speed, use_gpu], outputs=[out_audio, out_ps], api_name=API_NAME)
     tokenize_btn.click(fn=tokenize_first, inputs=[text, voice], outputs=[out_ps], api_name=API_NAME)
     predict_btn.click(fn=predict, inputs=[text, voice, speed], outputs=[out_audio], api_name=API_NAME)