This is a basic automatic speech recognition (ASR) model able to recognize 8 commands/words.
If you wish to run experiments, training the model, you will need the following dependencies.
- Data Version Control (DVC): used for MLOps practices
💡 Make sure DVC (1) is installed, together with the Google Cloud Storage (GCS) driver (2), e.g.:
pip install dvc
pip install "dvc[gs]"
- Google Cloud CLI: used by DVC
- Jupyter notebook (for manual evaluation)
- Run
dvc pull
to get the training data and custom data (used for manual evaluation). - Edit source code e.g. the
params.yaml
file ormodel.py
. - Run
dvc exp run
to run a new experiment. - Run
dvc exp diff
to see the difference of your experiment, compared to the main branch.
If you wish to manually test the model, without your own data, run dvc pull custom_data
to get the data. Start a jupyter notebook server and run the
./src/manual_evaluation.sync.ipynb
notebook.
To manually test the model with your own data, load in your own speech which
are .wav
files with similar specs as the data it was trained on:
Format : Wave
File size : 31.3 KiB
Duration : 1 s 0 ms
Overall bit rate mode : Constant
Overall bit rate : 256 kb/s
Audio
Format : PCM
Format settings : Little / Signed
Codec ID : 1
Duration : 1 s 0 ms
Bit rate mode : Constant
Bit rate : 256 kb/s
Channel(s) : 1 channel
Sampling rate : 16.0 kHz
Bit depth : 16 bits
Stream size : 31.2 KiB (100%)
Check out the manual evaluation notebook on how to load in the model and make new predictions.