In the picture below, you see the GUI we created for our transcription server. To start this server, follow the Quick Start <quick_start> .
By pressing on the red plus button, one can add different models, that can be specified in a respective config file.<br> Then you can record your voice, by clicking on the 'RECORD' button, listen to it by pressing the 'PLAY' button and finally transcribing it with 'TRANSCRIBE'.
The transcription of both models is displayed in the respective block. Further, a popup shows up, where you are asked to correct the transcription. When you click on 'IMPROVE', the audio and the respective transcription are saves to ~/.sonosco/audio_data/web_collected/ . If one uses the 'Comparison' toggle, the transcriptions are additionally compared to the corrected transcription.
Of course you can also use the transcription server without docker. To do so, you first need to specify the model you want to use with its inference code in the model_loader.py, this file can be found at server > model_loader.py.
For the LAS model, this looks like this: :: from sonosco.inference.las_inference import LasInference
- model_id_to_inference = {
"las": LasInference}
Afterwards, just specify the model, with the path to the checkpoint file in the server > config.yaml: :: models: - id: 'las' path: '~/pretrained/las.pt' decoder: 'greedy' name: 'LAS'
The next thing to do is build the frontend, for this, navigate to server > frontend and run npm run build.
When this is finished, start the transcription server by running python app.py in the server directory. Open http://localhost:5000 and use your model.
In order to use your own model with the model trainer, additionally to the serialization guide, you need to implement an inference snippet. For this, simply follow the example of the other models. And then specify your model in the ../sonosco/server/model_loader.py script. (Have a look at the repo )