Show HN: Self-host Whisper As a Service with GUI and queueing

WaaS - Whisper as a Service

Backend flask application for the Speech To Text service.

This service is powered by OpenAI Whisper

API Documentation

POST /v1/transcribe

Add a new transcribe job to the queue. The job will be processed by the worker asynchroniously.

The response will be a JSON object with job_id that can be used to check the status of the job.

Query parameters:

• REQUIRED: email_callback: string
• OPTIONAL: language: string (default: automatic detection)
• OPTIONAL: model: string (default: tiny)
• OPTIONAL: task: string (default: transcribe)
  • transcribe: Transcribe audio to text
  • translate: Transcribe then translate audio to text
• OPTIONAL: filename: string (default: untitled-transcription)

Body:

• REQUIRED: binary data: Raw data with the audio content to transcribe

OPTIONS /v1/transcribe

Get the available options for the transcribe route.

POST /v1/detect

Detect the language of the audio file.

Query parameters:

• OPTIONAL: model: string (default: tiny)

Body:

• REQUIRED: binary data: Raw data with the audio content to detect the language for

OPTIONS /v1/detect

Get the available options for the detect route.

GET /v1/download/<job_id>

Receive the finished job result as the requested output format.

Query parameters:

• OPTIONAL: output: string (default: srt)
  • json: JSON response of the model output
  • timecode_txt: Plain text file with timecodes(srt)
  • txt: Plain text file of the detected text
  • vtt: WebVTT file with the detected text
  • srt: WebVTT file with the detected text

OPTIONS /v1/download/<job_id>

Get the available options for the download route.

GET /v1/jobs/<job_id>

Get the status and metadata of the provided job.

GET /v1/queue

Get the available length of the queue as JSON object with the key length.

Contributing

Requirements

Required amount of VRAM depends on the model used. The smallest model is tiny which requires about 1GB of VRAM.

You can see the full list of models here with information about the required VRAM.

The codebase is expected to be compatible with Python 3.8-3.10. This would be the same as the OpenAI Whisper requirements.

Installation

python3 -mvenv .venv
source .venv/bin/activate
pip install -r requirements.txt

Running full setup using docker-compose

First create a .envrc file with the following content:

export BASE_URL=https://example.com
export EMAIL_SENDER_ADDRESS=example@example.com
export EMAIL_SENDER_PASSWORD=example
export EMAIL_SENDER_HOST=smtp.example.com

export DISCLAIMER='This is a <a href="example.com">disclaimer</a>'

Then run the following command

docker-compose --env-file .envrc up

This will start three docker containes.

• redis
• api running flask fra src
• worker running rq from src

Running full setup using devcontainers

Install remote-development extensions (containes) And then in vscode do Devcontaines: open folder in container Then you are inside the api-containe and can do stuff

curl

To upload a file called audunspodssounds.mp3 in norwegian from your download directory

curl --location --request POST 'localhost:5000/v1/transcribe?output=vtt' \
  --header 'Content-Type: audio/mpeg' \
  --data-binary '@/Users/<user>/Downloads/audunspodssounds.mp3'

Running tests

FAQ

How to fix [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate?

$ /Applications/Python\ 3.7/Install\ Certificates.command

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