Alternative way to run wav2letter Facebook AI Research Speech to Text model on Windows Machine
Wav2Letter has different train- and inference-time dependencies.
I am assuming you will be performing training on CUDA backend. If so, you need ArrayFire and Flashlight.
For inference, besides basic dependancies (such as cereal for serialization, etc.) you don't need either. The FAIR team has provided their own implementations of neural network layers (linear, conv1d, etc.) based on FBGEMM (FB General Matrix Multiplication) backend. FBGEMM can both be compiled for CPU and CUDA backend - on intel-based CPUs, it can further be accelerated using intel's optimized MKL math library and on CUDA backend, using cuDNN.
You are free to add your own implementations of backend based on say, LibTorch or C++ TensorFlow and submit a PR.
I'm trying to implement Speech-to-text using wav2letter. As far as I have researched the model uses the Arrayfire tensor library with dependency on flashlight ML library.
Now, the flashlight library is built for Linux based system.
Is there any way to run this model on the Windows-based system.