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import json
import os
import asyncio
from moviepy.editor import AudioFileClip, concatenate_audioclips
from huggingface_hub import InferenceClient
import torch
import edge_tts
import tempfile
import gradio as gr
# Initialize Hugging Face Inference Client
Client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
generator = torch.Generator().manual_seed(42)
async def text_to_speech(text, voice, filename):
communicate = edge_tts.Communicate(text, voice)
await communicate.save(filename)
async def generate_conversation(script):
title = script['title']
content = script['content']
temp_files = []
tasks = []
for key, text in content.items():
speaker = key.split('_')[0] # Extract the speaker name
index = key.split('_')[1] # Extract the dialogue index
voice = "en-US-JennyNeural" if speaker == "Alice" else "en-US-GuyNeural"
# Create temporary file for each speaker's dialogue
temp_file = tempfile.NamedTemporaryFile(suffix='.mp3', delete=False)
temp_files.append(temp_file.name)
filename = temp_file.name
tasks.append(text_to_speech(text, voice, filename))
print(f"Generated audio for {speaker}_{index}: {filename}")
await asyncio.gather(*tasks)
# Combine the audio files using moviepy
audio_clips = [AudioFileClip(temp_file) for temp_file in temp_files]
combined = concatenate_audioclips(audio_clips)
# Create temporary file for the combined output
output_filename = tempfile.NamedTemporaryFile(suffix='.mp3', delete=False).name
# Save the combined file
combined.write_audiofile(output_filename)
print(f"Combined audio saved as: {output_filename}")
# Clean up temporary files
for temp_file in temp_files:
os.remove(temp_file)
print(f"Deleted temporary file: {temp_file}")
return output_filename
# Function to generate podcast based on user input
async def generate_podcast(topic, seed=42):
system_instructions = '''[SYSTEM] You are an educational podcast generator. You have to create a podcast between Alice and Bob that gives an overview of the topic given by the user.
Please provide the script in the following JSON format:
{
"title": "[string]",
"content": {
"Alice_0": "[string]",
"BOB_0": "[string]",
...
}
}
Be concise.
'''
text = f" Topic: {topic} json:"
formatted_prompt = system_instructions + text
stream = Client.text_generation(formatted_prompt, max_new_tokens=1024, seed=seed, stream=True, details=True, return_full_text=False)
generated_script = ""
for response in stream:
if not response.token.text == "</s>":
generated_script += response.token.text
print("Generated Script:"+generated_script)
# Check if the generated_script is empty or not valid JSON
if not generated_script or not generated_script.strip().startswith('{'):
raise ValueError("Failed to generate a valid script.")
script_json = json.loads(generated_script) # Use the generated script as input
output_filename = await generate_conversation(script_json)
print("Output File:"+output_filename)
# Read the generated audio file
return output_filename
# Gradio Interface
DESCRIPTION = """ # <center><b>PODGEN 📻</b></center>
### <center>Generate a podcast on any topic</center>
### <center>Use the Power of llms to understand any topic better</center>
"""
with gr.Blocks(theme=gr.themes.Soft(),css="#important{display: none;}") as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
input = gr.Textbox(label="Topic", placeholder="Enter a topic")
output = gr.Audio(label="Podgen", type="filepath", interactive=False, autoplay=True, elem_classes="audio")
gr.Interface(
fn=generate_podcast,
inputs=[input],
outputs=[output],
)
if __name__ == "__main__":
demo.queue(max_size=200).launch()
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