Tf Speech Recognition Challenge Solution
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TF Speech Recognition Challenge
Tensorflow Speech Recognition Challenge was a Kaggle competition organised by Google Brain to use the Speech Commands Dataset to build an algorithm that understands simple spoken commands. https://www.kaggle.com/c/tensorflow-speech-recognition-challenge
This solution achieved a rank of 63 on private leaderboard (top 5%).
Project Structure
- data
- raw
- train (Training audio files)
- test (Test audio files used for evaluation
- raw
- libs
- classification (All scripts used for training and evaluation)
- notebooks
- scripts (Executable scripts)
- models (Pretrained Models)
Requirements
- Tensorflow 1.4
- librosa
- scikit-learn
- Python 3.x
Running
Download the Speech Commands Dataset and extract the dataset in the train folder. Test Audio can be placed in data/test/audio folder.
The notebooks can be run individually using Jupyter. To run the scripts from command line edit the notebooks using Jupyter and run:
./script/execute_notebook.py
and select the notebook to run. The results are stored in results/notebook_name.log
P0 Predict Test WAV.ipynb can be used to predict audio files using a trained graphdef model.
Architecture
Models used
- A variant of Convolutional LSTM (https://arxiv.org/pdf/1610.00277.pdf)
- LSTM-L (https://arxiv.org/pdf/1711.07128.pdf)
- C-RNN (https://arxiv.org/pdf/1711.07128.pdf)
- GRU-L (https://arxiv.org/pdf/1711.07128.pdf)
- Resnet
Training
The model was trained using a GCP instance with the following specifications:
- NVIDIA Tesla P100 X 1
- 16 GB RAM
- 35 GB SSD
Most of the models converged in 30k steps. Pseudo Labelling on test data was used to improve the model performance.
Prediction
The final model was a ensemble 13 models. Weighted Averaging and Stacking was used to generate the final predictions.
Aknowledgements
- ML-KWS-for-MCU (ARM-software/ML-KWS-for-MCU)
- Very Deep Convolutional Neural Network for Robust Speech Recognition (https://arxiv.org/pdf/1610.00277.pdf)
- Speech Commands Dataset (https://research.googleblog.com/2017/08/launching-speech-commands-dataset.html)
If you like this project or have any queries don't hesitate to send an email to [email protected]
