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https://github.com/schibsted/WAAS
Whisper as a Service (GUI and API with queuing for OpenAI Whisper)
https://github.com/schibsted/WAAS
Last synced: 2 months ago
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Whisper as a Service (GUI and API with queuing for OpenAI Whisper)
- Host: GitHub
- URL: https://github.com/schibsted/WAAS
- Owner: schibsted
- License: apache-2.0
- Created: 2022-10-13T09:59:25.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-26T01:54:09.000Z (3 months ago)
- Last Synced: 2024-10-26T22:23:09.742Z (3 months ago)
- Language: JavaScript
- Homepage:
- Size: 29.4 MB
- Stars: 1,830
- Watchers: 21
- Forks: 94
- Open Issues: 36
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
- awesome-whisper - WaaS - GUI and API for Whisper. (Web apps / Self-hosted)
- project-awesome - schibsted/WAAS - Whisper as a Service (GUI and API with queuing for OpenAI Whisper) (JavaScript)
- awesome-openai-whisper - Whisper as a Service (GUI and API for OpenAI Whisper)
README
# WaaS - Whisper as a Service
GUI and API for [OpenAI Whisper](https://github.com/openai/whisper)
No video support
## What is Jojo?
Jojo is a GUI for upload and transcribe a audio or video file. After the transcription is done you get an email with download links.
You can directly download a Jojo-file, SRT, or text from the email. Then you can upload a Jojo file to the frontend to come into an editor (see video).
### Editor
The editor works 100% local in your browser. Here can you listen to segments and fix transcriptions errors. After you are done just save the Jojo-file to your desktop. An easy way to play the selected segment is by holding down the Control-key on the keyboard.
## This project started out by VG
## 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 or `webhook_id`: 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/`
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/`
Get the available options for the download route.
### GET `/v1/jobs/`
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`.
### Webhook response
If using `webhook_id` in the request parameters you will get a `POST` to the webhook url of your choice.
The request will contain a `X-WAAS-Signature` header with a hash that can be used to verify the content.
Check `tests/test_webhook.py` for an example on how to verify this signature using Python on the receiving end.
The post payload will be a json with this content
On success:
```
{
"source": "waas",
"job_id": "09d2832d-cf3e-4719-aea7-1319000ef372",
"success": true,
"url": "https://example.com/v1/download/09d2832d-cf3e-4719-aea7-1319000ef372",
"filename": "untitled-transcription"
}
```
On failure:
```
{
"source": "waas",
"job_id": "09d2832d-cf3e-4719-aea7-1319000ef372",
"success": false
}
```
## 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](https://github.com/openai/whisper#available-models-and-languages) 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](https://github.com/openai/whisper#setup) requirements.
### Installation
```sh
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:
```sh
export BASE_URL=https://example.com
export [email protected]
export EMAIL_SENDER_PASSWORD=example
export EMAIL_SENDER_HOST=smtp.example.com
export DISCLAIMER='This is a disclaimer'
export ALLOWED_WEBHOOKS_FILE='allowed_webhooks.json'
```
Add a json file named `allowed_webhooks.json` to the root folder of the project. This file is ignored by git.
The content should be a list of valid webhooks, urls and your generated tokens like this:
```
[
{
"id": "77c500b2-0e0f-4785-afc7-f94ed529c897",
"url": "https://myniceserver.com/mywebhook",
"token": "frKPI6p5LxxpJa8tCvVr=u5NvU66EJCQdybPuEmzKNeyCDul2-zrOx05?LwIhL5N"
}
]
```
For testing you could use the https://webhook.site service (as long as you do not post/transcribe private data)
And set the env variable `ALLOWED_WEBHOOKS_FILE=allowed_webhooks.json`
Then run the following command
```sh
docker-compose --env-file .envrc up
```
This will start three docker containers.
- redis
- api running flask fra src
- worker running rq from src
### Using NVIDIA CUDA with docker-compose
If you have a NVIDIA GPU and want to use it with docker-compose,
you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker).
To enable CUDA support, you need to edit the `docker-compose.yml` file to use the `nvidia` runtime:
```yaml
build:
context: .
# use Dockerfile.gpu when using a NVIDIA GPU
dockerfile: Dockerfile.gpu
```
You also have to uncomment the device reservation in the `docker-compose.yml` file:
```yaml
deploy:
resources:
reservations:
devices:
- driver: nvidia
capabilities: [gpu]
```
Then run the following command as usual:
```sh
docker-compose --env-file .envrc up
```
The worker will now use the GPU acceleration.
### Running full setup using devcontainers
Install remote-development extensions (containers)
And then in vscode do `Devcontainers: open folder in container`
Then you are inside the api-container and can do stuff
### curl
To upload a file called audunspodssounds.mp3 in norwegian from your download directory
With email callback:
```sh
curl --location --request POST 'localhost:3000/v1/transcribe?output=vtt&email_callback=test@localhost&language=norwegian&model=large' \
--header 'Content-Type: audio/mpeg' \
--data-binary '@/Users//Downloads/audunspodssounds.mp3'
```
With webhook callback:
```sh
curl --location --request POST 'localhost:3000/v1/transcribe?output=vtt&language=norwegian&model=large&webhook_callback_url=https://myniceserver.something/mywebhookid' \
--header 'Content-Type: audio/mpeg' \
--data-binary '@/Users//Downloads/audunspodssounds.mp3'
```
### Running tests
```bash
$ pytest
```
## 🥳 Contributing
## FAQ
### How to fix `[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate`?
```sh
$ /Applications/Python\ 3.7/Install\ Certificates.command
```
### How to run tests outside the docker container?
Make sure you have fired up the Redis using docker-compose and then use:
`ENVIRONMENT=test BASE_URL=http://localhost REDIS_URL=redis://localhost:6379 pytest -v `