Whisper API

Speech-to-text with Whisper (PyTorch) behind an OpenAI-compatible /v1/audio/transcriptions endpoint.

What's inside

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Hugging Face transformers Whisper pipeline (float16, chunked long audio)
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POST /v1/audio/transcriptions (OpenAI format)
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API key = your rental's app token

How you connect

Access	Where	Auth
SSH (root)	`ssh -p <port> root@clearlayer.ca`	Your SSH key
JupyterLab	`https://r<id>-<random>.gpu.clearlayer.ca`	Token in the link
Transcription API (port 8000)	`https://r<id>-<random>-app.gpu.clearlayer.ca`	`Authorization: Bearer <app token>`

Environment variables

Variable	Default
`WHISPER_MODEL`	`openai/whisper-large-v3`
`APP_TOKEN`	random per rental
`HF_TOKEN`	optional, for gated Hugging Face models

Models from the model library set these for you.

Quick start

curl

curl https://r12-abcd1234-app.gpu.clearlayer.ca/v1/audio/transcriptions \
  -H "Authorization: Bearer <app-token>" \
  -F file=@meeting.mp3 -F model=whisper-1

Python

from openai import OpenAI

client = OpenAI(base_url="https://r12-abcd1234-app.gpu.clearlayer.ca/v1", api_key="<app-token>")
with open("meeting.mp3", "rb") as f:
    print(client.audio.transcriptions.create(model="whisper-1", file=f).text)