TANMAY-555/Whisper_AI

Whisper Transcribe

A local speech-to-text service powered by faster-whisper and exposed publicly via ngrok. Upload any audio/video file and get back a full transcript with timestamps.

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Quick start

1. Install dependencies

pip install -r requirements.txt

2. Set your ngrok auth token

Open .env and paste your token after NGROK_AUTHTOKEN=:

NGROK_AUTHTOKEN=your_token_here

Get your free token at: https://dashboard.ngrok.com/get-started/your-authtoken

3. Run

python run.py

The terminal will print a public URL like:

================================================================
   Whisper Transcribe is live -> https://xxxx.ngrok-free.app
================================================================

Open that URL in any browser to use the web interface.

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Configuration (.env)

Variable	Default	Description
NGROK_AUTHTOKEN	(empty)	Your ngrok auth token (required for stable tunnels)
WHISPER_MODEL_SIZE	large-v3	Model to use: tiny, base, small, medium, large-v3
HOST	0.0.0.0	Flask bind address
PORT	5000	Flask port
MAX_UPLOAD_MB	100	Max upload size in MB

Smaller models (tiny, base, small) load faster and use less memory; large-v3 gives the best accuracy.

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API

POST /api/transcribe

Upload an audio or video file.

Request — multipart/form-data

Field	Type	Description
file	file	Audio/video to transcribe

Supported formats: .mp3, .wav, .m4a, .aac, .ogg, .flac, .mp4, .webm

Response — 200 OK

{
  "text": "Full transcript as a single string.",
  "language": "en",
  "duration": 12.34,
  "segments": [
    { "start": 0.0, "end": 3.5, "text": "Hello world." },
    { "start": 3.5, "end": 6.1, "text": "This is a test." }
  ]
}

GET /api/progress/<job_id>

Server-sent events (SSE) progress stream for a queued transcription job.

GET /api/download/<job_id>

Download the generated output file once the job is completed.

GET /health

Returns { "status": "ok", "model_size": "large-v3" }.

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Deploying on Render

Use this app as a single web service (frontend + backend served by Flask) or split frontend/backend services.

• Single service: no extra frontend configuration is needed.
• Split services: set frontend API base to your backend URL by either:
  • adding ?api_base=https://your-backend.onrender.com to the frontend URL, or
  • adding <meta name="api-base" content="https://your-backend.onrender.com"> in frontend/index.html.

The frontend always calls /api/* endpoints now, and backend supports both /api/* and legacy non-/api/* routes.

---

Project structure

.
├── backend/
│   ├── app.py          # Flask routes
│   ├── config.py       # Settings loaded from .env
│   └── transcriber.py  # faster-whisper inference
├── frontend/
│   ├── index.html
│   ├── style.css
│   └── app.js
├── outputs/            # Temporary upload scratch space (auto-cleaned)
├── run.py              # Entry point — starts Flask + ngrok tunnel
├── requirements.txt
└── .env                # Your local config (never commit this)

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Notes

• The model weights are downloaded automatically on the first run (~3 GB for large-v3). Subsequent runs use the cached weights.
• Transcription runs on CPU by default (compute_type=int8). If you have a CUDA GPU, edit transcriber.py and change device="cpu" to device="cuda" and compute_type to "float16".
• Without an ngrok token the tunnel still works on the anonymous free tier but may be rate-limited.

Whisper_AI_Transcription_model