TensorFlow Speech Recognition Challenge

This repo contains the code for winning entry in the TensorFlow Speech Recognition Challenge hosted by kaggle. The task was to build an algorithm that understands simple speech commands.

The score of this model is: 0.91060.

Appendix

A1.) Model Execution Time

• What software did you use for training and prediction?
  • see Requirements
• What hardware (CPUS spec, number of CPU cores, memory)?
  • a single GCP instance with 4 vCPUs, 18.5 GB memory and 1 x NVIDIA Tesla K80
• How long does it take to train your model?
  • ~4-8 hours (depends on the model) for a single model

A2.) Requirements:

• tensorflow>=2.3.0
• tensorflow-gpu>=2.3.0
• Keras>=2.4.3
• tqdm>=4.48.0
• scipy>=1.4.1
• numpy>=1.19.1
• pandas>=0.24.2
• pandas-ml>=0.6.1

All packages can be installed via pip install. Other versions will probably work too. I tested it with Python 3.7 using Ubuntu 20.04.

Note that installing scipy will take a couple hours. I tried removing this dependency by using the wave and struct modules but then reading the wavs is super slow (2 hours for all test files). I tested it with Raspbian GNU/Linux 8 (Jessie) and Python 3.4.2.

Installing dependencies

run below command to install dependencies

> git clone https://github.com/abidaks/tensorflow-speech-recognition
> cd tensorflow-speech-recognition
> pip install requirements.txt

Downloading dataset

Before training the model you need to download the training dataset from Kaggle and place it under /data/.

Model training

Run the jupyter notebook trainer.ipynb to train the model.