reflector/server/gpu/modal_deployments

Reflector GPU implementation - Transcription and LLM

This repository hold an API for the GPU implementation of the Reflector API service, and use Modal.com

• reflector_diarizer.py - Diarization API
• reflector_transcriber.py - Transcription API
• reflector_translator.py - Translation API

Modal.com deployment

Create a modal secret, and name it reflector-gpu. It should contain an REFLECTOR_APIKEY environment variable with a value.

The deployment is done using Modal.com service.

$ modal deploy reflector_transcriber.py
...
└── 🔨 Created web => https://xxxx--reflector-transcriber-web.modal.run

$ modal deploy reflector_llm.py
...
└── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run

Then in your reflector api configuration .env, you can set theses keys:

TRANSCRIPT_BACKEND=modal
TRANSCRIPT_URL=https://xxxx--reflector-transcriber-web.modal.run
TRANSCRIPT_MODAL_API_KEY=REFLECTOR_APIKEY

LLM_BACKEND=modal
LLM_URL=https://xxxx--reflector-llm-web.modal.run
LLM_MODAL_API_KEY=REFLECTOR_APIKEY

API

Authentication must be passed with the Authorization header, using the bearer scheme.

Authorization: bearer <REFLECTOR_APIKEY>

LLM

POST /llm

request

{
    "prompt": "xxx"
}

response

{
    "text": "xxx completed"
}

Transcription

POST /transcribe

request (multipart/form-data)

• file - audio file
• language - language code (e.g. en)

response

{
    "text": "xxx",
    "words": [
        {"text": "xxx", "start": 0.0, "end": 1.0}
    ]
}