Added description in README

Author: Waguramu

README.md

@@ -1,2 +1,109 @@
 # Roboy Sonosco
 Roboy Sonosco (from Lat. sonus - sound and nōscō - I know, recognize) - a library for Speech Recognition based on Deep Learning models
+
+## Installation
+
+The supported OS is Ubuntu 18.04 LTS (however, it should work fine on other distributions).
+Supported Python version is 3.6+.
+Supported CUDA version is 10.0.
+Supported PyTorch version is 1.0.
+
+---
+
+Install CUDA 10.0 from [NVIDIA website](https://developer.nvidia.com/cuda-10.0-download-archive). Make sure that your local gcc, g++, cmake versions are not older than the ones used to compile your OS kernel.
+
+You will need to download the latest [cuDNN](https://developer.nvidia.com/rdp/cudnn-archive) for CUDA 10.0. 
+Unzip it:
+```
+tar -xzvf cudnn-9.0-linux-x64-v7.tgz
+```
+Run
+```
+sudo cp cuda/include/cudnn.h /usr/local/cuda/include
+sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64
+sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
+```
+
+**All of the following steps you may perform inside [Anaconda](https://www.anaconda.com/) or [virtualenv](https://virtualenv.pypa.io/en/latest/)**
+
+Install [PyTorch](https://pytorch.org/get-started/locally/). For your particular configuration, you may want to build it from the [sources](https://github.com/pytorch/pytorch).
+
+Install SeanNaren's fork for Warp-CTC bindings. **Deprecated**: will be updated to use [built-in](https://pytorch.org/docs/stable/nn.html#torch.nn.CTCLoss) functions.
+```
+git clone https://github.com/SeanNaren/warp-ctc.git
+cd warp-ctc; mkdir build; cd build; cmake ..; make
+export CUDA_HOME="/usr/local/cuda"
+cd ../pytorch_binding && python setup.py install
+```
+
+Install pytorch audio:
+```
+sudo apt-get install sox libsox-dev libsox-fmt-all
+git clone https://github.com/pytorch/audio.git
+cd audio && python setup.py install
+```
+
+If you want decoding to support beam search with an optional language model, install [ctcdecode](https://github.com/parlance/ctcdecode):
+```
+git clone --recursive https://github.com/parlance/ctcdecode.git
+cd ctcdecode && pip install .
+```
+
+Clone this repo and run this within the repo:
+```
+pip install -r requirements.txt
+```
+
+### Mixed Precision
+If you want to use mixed precision training, you have to install [NVIDIA Apex](https://devblogs.nvidia.com/apex-pytorch-easy-mixed-precision-training/):
+
+```
+git clone --recursive https://github.com/NVIDIA/apex.git
+cd apex && pip install .
+```
+
+## Usage
+
+### Dataset
+
+To create a dataset you must create a CSV manifest file containing the locations of the training data. This has to be in the format of:
+
+```
+/path/to/audio.wav,/path/to/text.txt
+/path/to/audio2.wav,/path/to/text2.txt
+...
+```
+There is an example in examples directory.
+
+### Training, Testing and Inference
+
+Fundamentally, you can run the scripts the same way:
+
+```
+python3 train.py --config /path/to/config/file.yaml
+python3 test.py --config /path/to/config/file.yaml
+python3 infer.py --config /path/to/config/file.yaml
+```
+The scripts are initialised via configuration files.
+
+#### Configuration
+
+Configuration file contains arguments for ModelWrapper initialisation as well as extra parameters. Like this:
+
+```
+train:
+  ...
+  log-dir: 'logs' # Location for log files
+  def-dir: 'examples/checkpoints/', # Default location to save/load models
+  model-name: 'asr_final.pth' # File name to save the best model
+  sample-rate: 16000 # Sample rate
+  window: 'hamming' # Window type for spectrogram generation
+  batch-size: 32 # Batch size for training
+  checkpoint: True # Enables checkpoint saving of model
+  ...
+```
+More configuration examples with descriptions you may find in the config directory.
+
+## Acknowledgements
+
+This project is partially based on SeanNaren's [deepspeech.pytorch](https://github.com/SeanNaren/deepspeech.pytorch) repository.

config/train.yaml

@@ -4,8 +4,8 @@ train:
   labels-path: 'examples/labels.json' # Contains all characters for transcription
   log-dir: 'logs' # Location for log files
   def-dir: 'examples/checkpoints/', # Default location to save/load models
-  model-name: 'deepspeech_final.pth' # File name to save the best model
-  load-from: 'deepspeech_final.pth' # File name containing a checkpoint to continue/finetune
+git
+  load-from: 'asr_final.pth' # File name containing a checkpoint to continue/finetune
 
   sample-rate: 16000 # Sample rate
   window-size: 0.02 # Window size for spectrogram in seconds