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-A proper simple setup to train a Vosk model
+# Vosk API Training
 
-More documentation later
+This directory contains scripts and tools for training speech recognition models using the Kaldi toolkit.
+
+## Table of Contents
+
+1. [Overview](#overview)
+2. [Directory Structure](#directory-structure)
+3. [Installation](#installation)
+4. [Training Process](#training-process)
+    - [Data Preparation](#data-preparation)
+    - [Dictionary Preparation](#dictionary-preparation)
+    - [MFCC Feature Extraction](#mfcc-feature-extraction)
+    - [Acoustic Model Training](#acoustic-model-training)
+    - [TDNN Chain Model Training](#tdnn-chain-model-training)
+    - [Decoding](#decoding)
+5. [Results](#results)
+6. [Contributing](#contributing)
+
+## Overview
+
+This repository provides tools for training custom speech recognition models using Kaldi. It supports acoustic model training, language model creation, and decoding pipelines.
+
+## Directory Structure
+
+```plaintext
+.
+├── cmd.sh                         # Command configuration for training and decoding
+├── conf/
+│   ├── mfcc.conf                  # Configuration for MFCC feature extraction
+│   └── online_cmvn.conf           # Online Cepstral Mean Variance Normalization (currently empty)
+├── local/
+│   ├── chain/
+│   │   ├── run_ivector_common.sh  # Script for i-vector extraction during chain model training
+│   │   └── run_tdnn.sh            # Script for training a TDNN model
+│   ├── data_prep.sh               # Data preparation script for creating Kaldi data directories
+│   ├── download_and_untar.sh      # Script for downloading and extracting datasets
+│   ├── download_lm.sh             # Downloads language models
+│   ├── prepare_dict.sh            # Prepares the pronunciation dictionary
+│   └── score.sh                   # Scoring script for evaluation
+├── path.sh                        # Script for setting Kaldi paths
+├── RESULTS                        # Script for printing the best WER results
+├── RESULTS.txt                    # Contains WER results from decoding
+├── run.sh                         # Main script for the entire training pipeline
+├── steps -> ../../wsj/s5/steps/   # Link to Kaldi’s WSJ steps for acoustic model training
+└── utils -> ../../wsj/s5/utils/   # Link to Kaldi’s utility scripts
+```
+
+### Key Files:
+- **cmd.sh**: Defines commands for running training and decoding tasks.
+- **path.sh**: Sets up paths for Kaldi binaries and scripts.
+- **run.sh**: Main entry point for the training pipeline, running tasks in stages.
+- **RESULTS**: Displays Word Error Rate (WER) for the trained models.
+
+## Installation
+
+### Prerequisites
+- [Kaldi](https://github.com/kaldi-asr/kaldi): Kaldi toolkit must be installed and configured.
+- Required tools: `ffmpeg`, `sox`, `sctk` for data preparation and scoring.
+
+### Steps
+1. Clone the Vosk API repository.
+2. Install Kaldi and ensure the `KALDI_ROOT` is correctly set in `path.sh`.
+3. Set environment variables using `cmd.sh` and `path.sh`.
+
+## Training Process
+
+### Data Preparation
+Run the data preparation stage in `run.sh`:
+```bash
+bash run.sh --stage 0 --stop_stage 0
+```
+This stage downloads and prepares the LibriSpeech dataset.
+
+### Dictionary Preparation
+Prepare the pronunciation dictionary with:
+```bash
+bash run.sh --stage 1 --stop_stage 1
+```
+This step generates the necessary files for Kaldi's `prepare_lang.sh` script.
+
+### MFCC Feature Extraction
+Run the MFCC extraction process:
+```bash
+bash run.sh --stage 2 --stop_stage 2
+```
+This step extracts Mel-frequency cepstral coefficients (MFCC) features and computes Cepstral Mean Variance Normalization (CMVN).
+
+### Acoustic Model Training
+Train monophone, LDA+MLLT, and SAT models:
+```bash
+bash run.sh --stage 3 --stop_stage 3
+```
+This stage trains GMM-based models and aligns the data for TDNN training.
+
+### TDNN Chain Model Training
+Train a Time-Delay Neural Network (TDNN) chain model:
+```bash
+bash run.sh --stage 4 --stop_stage 4
+```
+The chain model uses i-vectors for speaker adaptation.
+
+### Decoding
+After training, decode the test data:
+```bash
+bash run.sh --stage 5 --stop_stage 5
+```
+This step decodes using the trained model and evaluates the Word Error Rate (WER).
+
+## Results
+
+WER can be evaluated by running:
+```bash
+bash RESULTS
+```
+Example of `RESULTS.txt`:
+```plaintext
+%WER 14.10 [ 2839 / 20138, 214 ins, 487 del, 2138 sub ] exp/chain/tdnn/decode_test/wer_11_0.0
+%WER 12.67 [ 2552 / 20138, 215 ins, 406 del, 1931 sub ] exp/chain/tdnn/decode_test_rescore/wer_11_0.0
+```
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