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import gradio as gr
from gradio_client import Client, handle_file
import os
# Get Hugging Face token from environment variable
hf_token = os.getenv("your_huggingface_token") # Make sure this is set in your environment
# Initialize the Gradio client with the token
client = Client("ResembleAI/Chatterbox", hf_token)
# Define the function to call the model and return the generated audio file
def generate_tts_audio(text_input, audio_prompt_url, exaggeration_input, temperature_input, seed_num_input, cfgw_input):
try:
print("sending request")
# Only process the audio prompt if the field is not empty
if audio_prompt_url and audio_prompt_url.strip():
audio_prompt = handle_file(audio_prompt_url)
else:
audio_prompt = None # This is the KEY change
result = client.predict(
text_input=text_input,
audio_prompt_path_input=audio_prompt,
exaggeration_input=exaggeration_input,
temperature_input=temperature_input,
seed_num_input=seed_num_input,
cfgw_input=cfgw_input,
api_name="/generate_tts_audio"
)
print("API Result:", result)
return result
except Exception as e:
print(f"An error occurred: {e}")
return None
# Create the Gradio interface
interface = gr.Interface(
fn=generate_tts_audio,
inputs=[
gr.Textbox(label="Text to Synthesize", placeholder="Enter your text here..."),
gr.Audio(sources=["upload", "microphone"], type="filepath", label="Reference Audio File (Optional)"),
gr.Slider(minimum=0, maximum=1, value=0.5, label="Exaggeration"),
gr.Slider(minimum=0, maximum=1, value=0.8, label="Temperature"),
gr.Number(label="Seed Number", value=0),
gr.Slider(minimum=0, maximum=1, value=0.5, label="CFG/Pace")
],
outputs=gr.Audio(label="Generated Audio")
)
# Launch the Gradio interface
if __name__ == "__main__":
interface.launch()
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