app.py

import gradio as gr
from pydub import AudioSegment
import edge_tts
import os
import asyncio
import uuid
import re

# Function to get the length of an audio file in seconds
def get_audio_length(audio_file):
    audio = AudioSegment.from_file(audio_file)
    return audio.duration_seconds

# Function to format time for SRT
def format_time(seconds):
    millis = int((seconds % 1) * 1000)
    seconds = int(seconds)
    hrs = seconds // 3600
    mins = (seconds % 3600) // 60
    secs = seconds % 60
    return f"{hrs:02}:{mins:02}:{secs:02},{millis:03}"

# Function to split text into segments by punctuation or limit to 7-8 words
def split_text_into_segments(text):
    segments = []
    raw_segments = re.split(r'([.!?])', text)
    for i in range(0, len(raw_segments) - 1, 2):
        sentence = raw_segments[i].strip() + raw_segments[i + 1]
        words = sentence.split()
        
        if len(words) > 8:
            for j in range(0, len(words), 8):
                segments.append(" ".join(words[j:j + 8]))
        else:
            segments.append(sentence.strip())
    
    if len(raw_segments) % 2 == 1:
        remaining_text = raw_segments[-1].strip()
        words = remaining_text.split()
        for j in range(0, len(words), 8):
            segments.append(" ".join(words[j:j + 8]))
    
    return segments

# Function to generate SRT with accurate timing per batch
async def generate_accurate_srt(batch_text, batch_num, start_offset, pitch, rate, voice):
    audio_file = f"batch_{batch_num}_audio.wav"
    
    # Generate the audio using edge-tts
    tts = edge_tts.Communicate(batch_text, voice, rate=rate, pitch=pitch)
    await tts.save(audio_file)

    # Get the actual length of the audio file
    actual_length = get_audio_length(audio_file)

    # Split the text into segments based on punctuation and word count
    segments = split_text_into_segments(batch_text)
    segment_duration = actual_length / len(segments)  # Duration per segment
    start_time = start_offset

    # Initialize SRT content
    srt_content = ""
    for index, segment in enumerate(segments):
        end_time = start_time + segment_duration
        
        if end_time > start_offset + actual_length:
            end_time = start_offset + actual_length

        srt_content += f"{index + 1 + (batch_num * 100)}\n"
        srt_content += f"{format_time(start_time)} --> {format_time(end_time)}\n"
        srt_content += segment + "\n\n"
        
        start_time = end_time

    return srt_content, audio_file, start_time

# Batch processing function
async def batch_process_srt_and_audio(script_text, pitch, rate, voice, progress=gr.Progress()):
    batches = [script_text[i:i + 500] for i in range(0, len(script_text), 500)]
    all_srt_content = ""
    combined_audio = AudioSegment.empty()
    start_offset = 0.0  

    for batch_num, batch_text in enumerate(batches):
        srt_content, audio_file, end_offset = await generate_accurate_srt(batch_text, batch_num, start_offset, pitch, rate, voice)
        all_srt_content += srt_content

        batch_audio = AudioSegment.from_file(audio_file)
        combined_audio += batch_audio
        start_offset = end_offset

        os.remove(audio_file)
        progress((batch_num + 1) / len(batches))

    total_audio_length = combined_audio.duration_seconds
    validated_srt_content = ""
    for line in all_srt_content.strip().splitlines():
        if '-->' in line:
            start_str, end_str = line.split(' --> ')
            start_time = sum(x * float(t) for x, t in zip([3600, 60, 1, 0.001], start_str.replace(',', ':').split(':')))
            end_time = sum(x * float(t) for x, t in zip([3600, 60, 1, 0.001], end_str.replace(',', ':').split(':')))
            if end_time > total_audio_length:
                end_time = total_audio_length
            line = f"{format_time(start_time)} --> {format_time(end_time)}"
        validated_srt_content += line + "\n"

    unique_id = uuid.uuid4()
    final_audio_path = f"final_audio_{unique_id}.mp3"
    final_srt_path = f"final_subtitles_{unique_id}.srt"

    combined_audio.export(final_audio_path, format="mp3", bitrate="320k")

    with open(final_srt_path, "w") as srt_file:
        srt_file.write(validated_srt_content)

    return final_srt_path, final_audio_path

# Gradio interface function
async def process_script(script_text, pitch, rate, voice):
    # Format pitch correctly for edge-tts
    pitch_str = f"{pitch}Hz" if pitch != 0 else "-1Hz"
    formatted_rate = f"{'+' if rate > 1 else ''}{int(rate)}%"  
    srt_path, audio_path = await batch_process_srt_and_audio(script_text, pitch_str, formatted_rate, voice_options[voice])
    return srt_path, audio_path, audio_path

# Gradio interface setup
voice_options = {
    "Andrew Male": "en-US-AndrewNeural",
    "Jenny Female": "en-US-JennyNeural",
    "Guy Male": "en-US-GuyNeural",
    "Ana Female": "en-US-AnaNeural",
    "Aria Female": "en-US-AriaNeural",
    "Brian Male": "en-US-BrianNeural",
    "Christopher Male": "en-US-ChristopherNeural",
    "Eric Male": "en-US-EricNeural",
    "Michelle Male": "en-US-MichelleNeural",
    "Roger Male": "en-US-RogerNeural",
    "Natasha Female": "en-AU-NatashaNeural",
    "William Male": "en-AU-WilliamNeural",
    "Clara Female": "en-CA-ClaraNeural",
    "Liam Female ": "en-CA-LiamNeural",
    "Libby Female": "en-GB-LibbyNeural",
    "Maisie": "en-GB-MaisieNeural",
    "Ryan": "en-GB-RyanNeural",
    "Sonia": "en-GB-SoniaNeural",
    "Thomas": "en-GB-ThomasNeural",
    "Sam": "en-HK-SamNeural",
    "Yan": "en-HK-YanNeural",
    "Connor": "en-IE-ConnorNeural",
    "Emily": "en-IE-EmilyNeural",
    "Neerja": "en-IN-NeerjaNeural",
    "Prabhat": "en-IN-PrabhatNeural",
    "Asilia": "en-KE-AsiliaNeural",
    "Chilemba": "en-KE-ChilembaNeural",
    "Abeo": "en-NG-AbeoNeural",
    "Ezinne": "en-NG-EzinneNeural",
    "Mitchell": "en-NZ-MitchellNeural",
    "James": "en-PH-JamesNeural",
    "Rosa": "en-PH-RosaNeural",
    "Luna": "en-SG-LunaNeural",
    "Wayne": "en-SG-WayneNeural",
    "Elimu": "en-TZ-ElimuNeural",
    "Imani": "en-TZ-ImaniNeural",
    "Leah": "en-ZA-LeahNeural",
    "Luke": "en-ZA-LukeNeural"
}  # All voice options

app = gr.Interface(
    fn=process_script,
    inputs=[
        gr.Textbox(label="Enter Script Text", lines=10),
        gr.Slider(label="Pitch Adjustment (Hz)", minimum=-20, maximum=20, value=0, step=1),
        gr.Slider(label="Rate Adjustment (%)", minimum=-50, maximum=50, value=-1, step=1),
        gr.Dropdown(label="Select Voice", choices=list(voice_options.keys()), value="Andrew Male"),
    ],
    outputs=[
        gr.File(label="Download SRT File"),
        gr.File(label="Download Audio File"),
        gr.Audio(label="Audio Playback")
    ],
    title="HIVEcorp Text-to-Speech with SRT Generation",
    description="Convert your script into audio and generate subtitles.",
    theme="compact",
)

app.launch()