pending update
I started this project to immerse myself in the world of open-source AI models. My goal is to use this to learn a language by practicing speaking the sentences I actually want to say, rather than the ones on the language-learning app. It would entail:
- speech to text model
- language-to-language translation model
- text to speech model that will 'speak' the result to me in MY intonation [a voice cloning model] so I can directly copy that.
I'm using python Python 3.10.0rc2
I'm using python venv
setting up one venv on cmd
python -m venv name_of_virtual_env
to activate the venv, use .\name_of_virtual_env\Scripts\Activate.ps1
on PowerShell and .\name_of_virtual_env\Scripts\activate.bat
on CMD (though this might work for either?)
if you're using vscode, install the Python extension and set up the venv inside your project folder. This way, VS Code will run the venv automatically when you run a Python file.
I used a requirements file because it's cleaner for me to keep track of. I added comments to it to let me know which model needs which deps since I'm using more than one model.
pip install -r .\requirements.txt
I also added a req_list_from_pip.txt
file that has all the versions, even of dependency dependencies.
git clone https://github.com/huggingface/transformers.git
cd transformers
pip install -e .
https://www.wikihow.com/Install-FFmpeg-on-Windows to download and install
then ffmpeg
and ffmpeg-python
added to the deps
- ✅Finished setting up Whisper STT
- Whisper that I used seems to be old. I'm getting a
ERROR: Exception in ASGI application
that doesn't stop the app from running but is definitely not happy. Other than that, it works fine. - Example output:
- Result:
STT result: Buenos días, buenos tardes, buenos noches, hola
- Time taken:
STT finished in 0.0136 minutes
- Result:
- *️⃣Next step is to add keywords like
language='es'
so that whatever I speak is returned as spanish.