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README.md

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+# FINETUNE WAV2VEC 2.0 FOR SPEECH RECOGNITION
+## Table of contents
+1. [Documentation](#documentation)
+2. [Installation](#installation)
+3. [Usage](#usage)
+4. [Logs and Visualization](#logs)
+
+<a name = "documentation" ></a>
+## Documentation
+Suppose you need a simple way to fine-tune the Wav2vec 2.0 model for the task of Speech Recognition on your datasets, then you came to the right place.
+</br>
+All documents related to this repo can be found here:
+- [Wav2vec2ForCTC](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC)
+- [Tutorial](https://huggingface.co/blog/fine-tune-wav2vec2-english)
+- [Code reference](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py)
+
+
+<a name = "installation" ></a>
+## Installation
+```
+pip install -r requirements.txt
+```
+
+<a name = "usage" ></a>
+## Usage
+1. Prepare your dataset
+    - Your dataset can be in <b>.txt</b> or <b>.csv</b> format.
+    - <b>path</b> and <b>transcript</b> columns are compulsory. The <b>path</b> column contains the paths to your stored audio files, depending on your dataset location, it can be either absolute paths or relative paths. The <b>transcript</b> column contains the corresponding transcripts to the audio paths. 
+    - Check out our [data_example.csv](dataset/data_example.csv) file for more information.
+2. Configure the config.toml file
+3. Run
+    - Start training:
+        ```
+        python train.py -c config.toml
+        ```
+    - Continue to train from resume:
+        ```
+        python train.py -c config.toml -r
+        ```
+    - Load specific model and start training:
+        ```
+        python train.py -c config.toml -p path/to/your/model.tar
+        ```
+
+<a name = "logs" ></a>
+## Logs and Visualization
+The logs during the training will be stored, and you can visualize it using TensorBoard by running this command:
+```
+# specify the <name> in config.json
+tensorboard --logdir ~/saved/<name>
+
+# specify a port 8080
+tensorboard --logdir ~/saved/<name> --port 8080
+```