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# app.py
import gradio as gr
import torch
import torchaudio
import google.generativeai as genai
from e2_tts_pytorch import E2TTS, DurationPredictor
import numpy as np
import os
import requests
from tqdm import tqdm
# (Keep the model loading and initialization code as before)
def generate_podcast_script(api_key, content, duration):
genai.configure(api_key=api_key)
model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
prompt = f"""
Create a podcast script for two people discussing the following content:
{content}
The podcast should last approximately {duration}. Include natural speech patterns,
humor, and occasional off-topic chit-chat. Use speech fillers like "um", "ah",
"yes", "I see", "Ok now". Vary the emotional tone (e.g., regular, happy, sad, surprised)
and indicate these in [square brackets]. Format the script as follows:
Host 1: [emotion] Dialog
Host 2: [emotion] Dialog
Ensure the conversation flows naturally and stays relevant to the topic.
"""
response = model.generate_content(prompt)
return response.text
def text_to_speech(text, speaker_id):
# For simplicity, we'll use a random mel spectrogram as input
# In a real scenario, you'd use the actual mel spectrogram from the cloned voice
mel = torch.randn(1, 80, 100)
# Generate speech
with torch.no_grad():
sampled = e2tts.sample(mel[:, :5], text=[text])
return sampled.cpu().numpy().squeeze()
def create_podcast(api_key, content, duration, voice1, voice2):
script = generate_podcast_script(api_key, content, duration)
return render_podcast(api_key, script, voice1, voice2)
def gradio_interface(api_key, content, duration, voice1, voice2):
script = generate_podcast_script(api_key, content, duration)
return script
def render_podcast(api_key, script, voice1, voice2):
lines = script.split('\n')
audio_segments = []
for line in lines:
if line.startswith("Host 1:"):
audio = text_to_speech(line[7:], speaker_id=0)
audio_segments.append(audio)
elif line.startswith("Host 2:"):
audio = text_to_speech(line[7:], speaker_id=1)
audio_segments.append(audio)
if not audio_segments:
return (22050, np.zeros(22050)) # Return silence if no audio was generated
# Concatenate audio segments
podcast_audio = np.concatenate(audio_segments)
return (22050, podcast_audio) # Assuming 22050 Hz sample rate
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# AI Podcast Generator")
api_key_input = gr.Textbox(label="Enter your Gemini API Key", type="password")
with gr.Row():
content_input = gr.Textbox(label="Paste your content or upload a document")
document_upload = gr.File(label="Upload Document")
duration = gr.Radio(["1-5 min", "5-10 min", "10-15 min"], label="Estimated podcast duration")
with gr.Row():
voice1_upload = gr.Audio(label="Upload Voice 1", type="filepath")
voice2_upload = gr.Audio(label="Upload Voice 2", type="filepath")
generate_btn = gr.Button("Generate Script")
script_output = gr.Textbox(label="Generated Script", lines=10)
render_btn = gr.Button("Render Podcast")
audio_output = gr.Audio(label="Generated Podcast")
generate_btn.click(gradio_interface, inputs=[api_key_input, content_input, duration, voice1_upload, voice2_upload], outputs=script_output)
render_btn.click(render_podcast, inputs=[api_key_input, script_output, voice1_upload, voice2_upload], outputs=audio_output)
demo.launch() |