PRD.md

PRD.md — AI Subtitle Generator MVP

Goal

Build a simple web app where users can:

1. Upload a video

2. Generate English subtitles using AI speech-to-text

3. Translate subtitles into:

   • Malayalam
   • Tamil
   • Hindi

4. Download .srt subtitle files

The MVP should be:

• Extremely simple
• Fast to build
• Vibecoding-friendly
• Localhost only

---

Core Features

1. Upload Video

Support:

• .mp4
• .mov
• .mkv
• .webm

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2. Extract Audio

Use FFmpeg to extract audio from the uploaded video.

Example:

ffmpeg -i input.mp4 -ar 16000 -ac 1 output.wav

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3. Speech to Text

Use local:

faster-whisper

Generate:

• English transcript
• English .srt
• Timestamps

MVP Decision

The MVP will use local Faster-Whisper instead of cloud APIs.

Why?

• Free
• Fast enough for short videos
• Better privacy
• Works offline
• Easy localhost setup
• Easy to vibecode

Suggested Model

Start with:

base

Upgrade later if needed:

• small
• medium

---

Example

from faster_whisper import WhisperModel

model = WhisperModel("base")
segments, info = model.transcribe("audio.wav")

---

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4. Translate Subtitles

Use a small translation adapter layer.

The app should NOT directly depend on one translation provider.

This makes it easy to:

• start simple
• swap providers later
• experiment with better translation models

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MVP Translation Provider

Start with:

deep-translator

Translate English subtitles into:

• Malayalam (ml)
• Tamil (ta)
• Hindi (hi)

---

Future Translation Provider

Later we can swap in:

• IndicTrans2
• LibreTranslate
• OpenAI models
• Other local translation models

without changing the main application flow.

---

Suggested Adapter Design

services/
└── translators/
    ├── base.py
    ├── deep_translator_adapter.py
    └── indictrans_adapter.py

---

Example Interface

class Translator:
    def translate(self, text: str, target_lang: str) -> str:
        pass

---

Example MVP Usage

translator = DeepTranslatorAdapter()
translated = translator.translate(text, "ml")

---

---

5. Generate .srt

Generate downloadable subtitle files.

Example:

1
00:00:01,000 --> 00:00:03,000
Hello everyone

---

Tech Stack

Backend

• FastAPI

Frontend

• HTML
• CSS
• Minimal JavaScript
• Jinja2 Templates

AI/Processing

• Faster-Whisper
• FFmpeg
• deep-translator
• pysrt

---

Simple Architecture

Upload Video
   ↓
Extract Audio
   ↓
Whisper Transcription
   ↓
Translate Text
   ↓
Generate .srt
   ↓
Download File

---

Suggested Folder Structure

app/
├── main.py
├── templates/
│   └── index.html
├── static/
│   └── styles.css
├── uploads/
├── subtitles/
└── services/
    ├── transcribe.py
    ├── translate.py
    └── srt_generator.py

---

Main UI

Single page with:

• Upload input
• Language dropdown
• Generate button
• Loading spinner
• Download links

---

Main API

Generate Subtitles

POST /generate-subtitles

Inputs:

• video file
• target language

Outputs:

• English .srt
• Translated .srt

---

Suggested Dependencies

fastapi
uvicorn
jinja2
python-multipart
faster-whisper
ffmpeg-python
deep-translator
pysrt

---

Run Locally

uvicorn app.main:app --reload

---

MVP Rules

• Keep everything in ONE FastAPI app
• Store files locally
• Use sync processing
• No authentication
• No database
• No React
• No Docker initially
• No microservices
• No overengineering

---

Build Order

1. Upload video
2. Extract audio
3. Generate English transcript
4. Generate English .srt
5. Add translation
6. Generate translated .srt
7. Improve UI later

---

Success Criteria

The MVP is successful if:

• Video upload works
• English subtitles are generated
• Translation works
• .srt download works
• End-to-end pipeline works locally