This project works as an extension to this DeepSpeech2 implementation.
Since this project is an extension for DeepSpeech2, you need to follow the installation instructions mentioned there.
Simply, copy the content of this folder and paste it inside deepspeech.pytorch folder.
I'll illustrate it on this pretrained acoustic model and this ARPA language model. You can find other models here.
You need to edit some files before you run the server application.
- Open the
run_decoder_server.shfile and change the following variables:
--lm-path: the path of the language model.
--model-path: the path of the acoustic model.
--port: the port that the server will be listening on.
python decoder_server.py --host 0.0.0.0 \
--port 8888 \
--lm-path /volume/3-gram.pruned.3e-7.arpa \
--decoder beam --alpha 1.97 --beta 4.36 \
--model-path /volume/librispeech_pretrained_v2.pth \
--beam-width 1024 \
--cuda
- Open
js/app.jsand find the following variables and change them:
X_seconds: record and send data eachX_secondsseconds.
var X_seconds = 3;ws_ip: the IP address of the computer that runs therun_decoder_server.sh script.
ws_port: the port that you use in therun_decoder_server.sh script.
var ws_ip = '0.0.0.0'
var ws_port = '8888'- Copy
data/extended_data_loader.pyfrom this project to thedatafolder in thedeepspeech.pytorchfolder.
Finally, run the following in different terminals:
> python website_server.py> ./run_decoder_server.sh- Faris Alasmary - farisalasmary
This project is licensed under the MIT License - see the LICENSE file for details
Many thanks for those who made it possible for this project to be realized! This project uses the functionalities of different open-source projects that are mentioned below.