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Speech recognition with RNN and CTC

Build Status

Table of Contents

Project description

This repository is a part of TDAT3001 Bachelor Thesis in Computer Engineering at NTNU, project number 61:

"End-to-end speech recognition with recurrent neural networks and connectionist temporal classification" by Anita Kristine Aune and Marit Sundet-Holm, 2018.

The purpose of this project was to test different neural networks' performance on speech recognition, using recurrent neural networks (RNN) and connectionist temporal classification (CTC).

During this project we have tested various nettwork models for speech recognition. The resulting logs and saved models can be found at train-logs.

Installation

This project uses Python 2.7, TensorFlow version 1.6.1 and Keras version 2.1.5.

This installation guide is for macOS and Ubuntu. TensorFlow also supports Windows, but this project is not tested on Windows.

  1. Install Python
    Download and install Python 2.7 from Python download

  2. Install TensorFlow
    Install TensorFlow for Python 2.7 in a virtual environment following the TensorFlow installation
    If possible, GPU installation is recommended as it speeds up training significantly.

  3. Install requirements
    Fork and download or clone the project, and enter the downloaded directory:

    $ git clone https://github.com/holm-aune-bachelor2018/ctc.git
$ cd ctc

    Ensure that the python environment where you installed TensorFlow is active and install the requirements:

    $ source /home/<user>/tensorflow/bin/activate
(tensorflow) $ pip install -r requirements.txt

NOTE - multi GPU training
As per Keras version 2.1.5, there is a bug when trying to save the model during training when using the multi_gpu_model().
Please refer to this Multi-GPU Model Keras guide regarding how to save and load a multi-GPU model, including a work-around for the bug.


Running

Download LibriSpeech

Ensure that the TensorFlow environment is active and that you are in the root project directory (ctc/) This will download 55 GB of speech data into the data_dir directory

(tensorflow) $ import_librispeech.py data_dir

Running training
If using TensorFlow with CPU or 1 GPU, to run the training with default parameters, simply do:

(tensorflow) $ train.py

This sets up training with the default BRNN model, using a small amount of data for testing.

Example BRNN
Setting up a BRNN network, with 512 units, training on batch_size=64, epoch_len=256
That is, 64x256=16384 files or ~25 hours of data on the train-clean-360 dataset
Train for epochs = 50
Save the .csv log file as "logs/brnn_25hours"
Save the model every 10 epochs at "models/brnn_25hours.h5" 

(tensorflow) $ train.py --units=512 --batch_size=64 --epoch_len=256 --epochs=50 --model_type='brnn' --model_save='models/brnn_25hours.h5' --log_file='logs/brnn_25hours'

Example loading
To continue training the same model for another 50 epochs, use the model_load argument:

(tensorflow) $ train.py --model_load='models/brnn_25hours.h5' --units=512 --batch_size=64 --epoch_len=256 --epochs=50 --model_save='models/continued_brnn_25hours.h5' --log_file='logs/continued_brnn_25hours'

Parallel GPU training
If running on multiple GPUs, enable multiGPU training:

(tensorflow) $ train.py --multi_GPU=2

Must be an even number of GPUs.

Example CuDNNLSTM
ONLY WORKS WITH GPU
With the GPU TensorFlow back you may wish to try the CuDNN optimised LSTM

(tensorflow) $ train.py --model_type=blstm --cudnn --units=512 --batch_size=64 --epoch_len=256 --epochs=50 --model_save='models/blstm_25hours.h5' --log_file='logs/blstm_25hours'

Usage

train.py is used to train models. predict.py is used to load already trained models, and produces predicions.

Parameters for train.py:

Training params 

--batch_size: Number of files in one batch. Default=32
--epoch_len: Number of batches per epoch. 0 trains on full dataset. Default=32
--epochs: Number of epochs to train. Default=10
--lr: Learning rate. Default=0.0001
--log_file: Path to log stats to .csv file. Default='logs'

Multi GPU or single GPU / CPU training 

--num_gpu: No. of gpu for training. (0,1) sets up training for one GPU or for CPU.
           MultiGPU training must be an even number larger than 1. Default=1

Preprocessing params

--feature_type: What features to extract: mfcc, spectrogram. Default='mfcc'
--mfccs: Number of mfcc features per frame to extract. Default=26
--mels: Number of mels to use in feature extraction. Default=40

Model params

--model_type: What model to train: brnn, blstm, deep_rnn, deep_lstm, cnn_blstm. Default='brnn'
--units: Number of hidden nodes. Default=256
--dropout: Set dropout value (0-1). Default=0.2
--layers: Number of recurrent or deep layers. Default=1
--cudnn: Include to use cudnn optimized LSTM.

Saving and loading model params

--model_save: Path, where to save model.
--checkpoint: No. of epochs before save during training. Default=10
--model_load: Path of existing model to load. If empty creates new model.
--load_multi: Include to load multi gpu model (saved during parallel GPU training).

Additional training settings

--save_best_val: Include to save additional version of model if val_loss improves.
--shuffle_indexes: Include to shuffle batches after each epoch. 
--reduce_lr: Include to reduce the learning rate if model stops improving val_loss.
--early_stopping: Include to stop the training early if val_loss stops improving.

Architecture overview

alt text

Shows the overall structure of the project.

  • train.py sets up network training.

  • models.py sets up the model build.

  • data.py generates a DataFrame containing filename (path to audio files), filesize and transcripts.

  • DataGenerator.py supplies the fit_generator() in train.py with batches of data during training.

  • LossCallback.py is used by the fit_generator() in train to calculate WER and save model and logs during training.

  • The remaning is varying utilities.

Additionally, predict.py loads a trained model and creates prediction samples. It can also calculate WER.


Licence

This file is part of Speech recognition with CTC in Keras.

The project is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

The project is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this project. If not, see http://www.gnu.org/licenses/.