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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
# Initialize Gemini AI
genai.configure(api_key='YOUR_GEMINI_API_KEY')
model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
# Initialize E2-TTS model
duration_predictor = DurationPredictor(
transformer=dict(
dim=512,
depth=8,
)
)
e2tts = E2TTS(
duration_predictor=duration_predictor,
transformer=dict(
dim=512,
depth=8
),
)
# Load the pre-trained model
model_path = "ckpts/E2TTS_Base/model_1200000.safetensors"
e2tts.load_state_dict(torch.load(model_path))
e2tts.eval()
def generate_podcast_script(content, duration):
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()
def create_podcast(content, duration, voice1, voice2):
script = generate_podcast_script(content, duration)
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)
# Concatenate audio segments
podcast_audio = np.concatenate(audio_segments)
return (22050, podcast_audio) # Assuming 22050 Hz sample rate
def gradio_interface(content, duration, voice1, voice2):
script = generate_podcast_script(content, duration)
return script
def render_podcast(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)
podcast_audio = np.concatenate(audio_segments)
return (22050, podcast_audio)
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# AI Podcast Generator")
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=[content_input, duration, voice1_upload, voice2_upload], outputs=script_output)
render_btn.click(render_podcast, inputs=[script_output, voice1_upload, voice2_upload], outputs=audio_output)
demo.launch() |