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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

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Whisper as a Service (GUI and API with queuing for OpenAI Whisper)

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README 

        
# WaaS - Whisper as a Service



GUI and API for [OpenAI Whisper](https://github.com/openai/whisper)





  

  No video support








  

      

  

  

      

  

  

      

  

  

    WAAS is released under the Apache-2.0 license

  

  

    

  

  

    

  




## 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 `