app.py

import gradio as gr
import edge_tts
import asyncio
import tempfile
import os
from moviepy.editor import AudioFileClip
import re

# Get all available voices
async def get_voices():
    voices = await edge_tts.list_voices()
    return {f"{v['ShortName']} - {v['Locale']} ({v['Gender']})": v['ShortName'] for v in voices}

# Text to speech function
async def text_to_speech(text, voice, rate, pitch):
    if not text.strip():
        return None, gr.Warning("Please enter the text to convert.")
    if not voice:
        return None, gr.Warning("Please select a voice.")
    
    voice_short_name = voice.split(" - ")[0]
    rate_str = f"{rate:+d}%"
    pitch_str = f"{pitch:+d}Hz"
    communicate = edge_tts.Communicate(text, voice_short_name, rate=rate_str, pitch=pitch_str)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    return tmp_path, None

# Generate SRT based on estimated timing
def generate_srt(text, speech_rate, max_words_per_line):
    # Clean up input text
    text = re.sub(r'\s+', ' ', text.strip())  # Remove excessive whitespace

    # Split into words
    words = text.split()
    
    # Calculate timing for each line
    srt_lines = []
    current_line = []
    current_time = 0.0  # Start time in seconds
    total_words = len(words)

    for i, word in enumerate(words):
        current_line.append(word)
        
        # Calculate current line length
        if len(current_line) >= max_words_per_line or i == total_words - 1:
            # Create SRT entry
            line_text = ' '.join(current_line)
            duration = len(line_text.split()) / speech_rate  # Estimate duration based on speech rate
            
            # Format timing
            start_time = current_time
            end_time = current_time + duration
            
            start_time_str = f"{int(start_time // 3600):02}:{int((start_time % 3600) // 60):02}:{int(start_time % 60):02},{int((start_time % 1) * 1000):03}"
            end_time_str = f"{int(end_time // 3600):02}:{int((end_time % 3600) // 60):02}:{int(end_time % 60):02},{int((end_time % 1) * 1000):03}"
            
            srt_lines.append(f"{len(srt_lines) + 1}\n{start_time_str} --> {end_time_str}\n{line_text}\n")
            
            # Move to the next line
            current_line = []
            current_time += duration  # Update current time

    return ''.join(srt_lines)

# Gradio interface function
def tts_interface(text, voice, rate, pitch, speech_rate, max_words_per_line):
    audio_path, warning = asyncio.run(text_to_speech(text, voice, rate, pitch))
    if warning:
        return None, None, warning
    
    # Generate SRT file
    srt_content = generate_srt(text, speech_rate, max_words_per_line)
    srt_path = audio_path.replace('.mp3', '_subtitle.srt')
    
    with open(srt_path, 'w') as f:
        f.write(srt_content)

    return audio_path, srt_path, None

# Create Gradio app
async def create_demo():
    voices = await get_voices()
    
    demo = gr.Interface(
        fn=tts_interface,
        inputs=[
            gr.Textbox(label="Input Text", lines=5),
            gr.Dropdown(choices=[""] + list(voices.keys()), label="Select Voice", value=""),
            gr.Slider(minimum=-50, maximum=50, value=0, label="Rate Adjustment (%)", step=1),
            gr.Slider(minimum=-20, maximum=20, value=0, label="Pitch Adjustment (Hz)", step=1),
            gr.Slider(minimum=100, maximum=300, value=150, label="Speech Rate (words per minute)", step=1),
            gr.Slider(minimum=3, maximum=8, value=5, label="Max Words per Line", step=1),
        ],
        outputs=[
            gr.Audio(label="Generated Audio", type="filepath"),
            gr.File(label="Generated Subtitle (.srt)"),
            gr.Markdown(label="Warning", visible=False)
        ],
        title="Edge TTS Text to Speech with SRT",
        description="Convert text to speech and generate synchronized subtitles based on speech rate.",
        analytics_enabled=False,
        allow_flagging=False,
    )
    
    return demo

# Run the app
if __name__ == "__main__":
    demo = asyncio.run(create_demo())
    demo.launch()