End-to-end trained speech recognition system, based on RNNs and the connectionist temporal classification (CTC) cost function.
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README.md

End-to-End Speech Recognition System Using Connectionist Temporal Classification

Automatic speech recognition (ASR) system implementation that utilizes the connectionist temporal classification (CTC) cost function. It's inspired by Baidu's Deep Speech: Scaling up end-to-end speech recognition and Deep Speech 2: End-to-End Speech Recognition in English and Mandarin papers. The system is trained on a combined corpus, containing 900+ hours. It achieves a word error rate (WER) of 12.6% on the test dataset, without the use of an external language model.

Deep Speech 1 and 2 network architectures

(a) shows the Deep Speech (1) model and (b) a version of the Deep Speech 2 model architecture.

Contents

Installation

The system was tested on Arch Linux and Ubuntu 16.04, with Python version 3.5+ and the 1.12.0 version of TensorFlow. It's highly recommended to use TensorFlow with GPU support for training.

Arch Linux

# Install dependencies.
sudo pacman -S sox python-tensorflow-opt-cuda tensorbaord

# Install optional dependencies. LaTeX is only required to plot nice looking graphs.
sudo pacman -S texlive-most

# Clone reposetory and install Python depdendencies.
git clone https://github.com/mdangschat/ctc-asr.git
cd speech
git checkout <release_tag>

# Setup optional virtual environment.
pip install -r requirements.txt

Ubuntu

Be aware that the requirements.txt file lists tensorflow as dependency, if you install TensorFlow through pip consider removing it as dependency and install tensorflow-gpu instead. Based on my experience it's worth the effort to build TensorFlow from source.

# Install dependencies.
sudo apt install python3-tk sox libsox-fmt-all

# Install optional dependencies. LaTeX is only required to plot nice looking graphs.
sudo apt install texlive

# Clone reposetory and install Python depdendencies. Don't forget to use tensorflow-gpu.
git clone https://github.com/mdangschat/ctc-asr.git
cd speech
git checkout <release_tag>

# Setup optional virtual environment.
pip3 install -r requirements.txt

Configuration

The network architecture and training parameters can be configured by adding the appropriate flags or by directly editing the asr/params.py configuration file.

Corpus

The following datasets were used for training and are listed in the data directory, however, the individual datasets are not part of this repository and have to be acquired by each user.

The test dataset consists of all clean training subsets from those datasets. Only the LibriSpeech clean dev set is used as the validation/development set and the LibriSpeech and Common Voice clean test sets are used as testing dataset. The ASR system works on 16 kHz mono WAV files.

A helper that downloads the free corpora and prepares the data and creates the merged corpora can be found in asr/dataset/generate_dataset.py. The file needs to be adjusted for the datasets that should be used. It further expects the TIMIT dataset to be present in the data/corpus/timit/TIMIT directory. If TIMIT should not be part of the training corpus, there is a flag to disable it in the generate_dataset.py.

The following tree shows a possible folder structure for the data directory.

data/
├── cache
│   ├── cv_corpus_v1.tar.gz
│   ├── dev-clean.tar.gz
│   ├── .gitignore
│   ├── tatoeba_audio_eng.zip
│   ├── TEDLIUM_release2.tar.gz
│   ├── test-clean.tar.gz
│   ├── train-clean-100.tar.gz
│   └── train-clean-360.tar.gz
├── commonvoice_dev.txt
├── commonvoice_test.txt
├── commonvoice_train.txt
├── corpus
│   ├── cv_corpus_v1
│   ├── .gitignore
│   ├── LibriSpeech
│   ├── tatoeba_audio_eng
│   ├── TEDLIUM_release2
│   └── timit
├── corpus.json
├── dev.txt
├── .gitignore
├── librispeech_dev.txt
├── librispeech_test.txt
├── librispeech_train.txt
├── tatoeba_train.txt
├── tedlium_dev.txt
├── tedlium_test.txt
├── tedlium_train.txt
├── test.txt
├── timit_test.txt
├── timit_train.txt
└── train.txt

train.csv 1050+ Hours

Examples shorter than 0.7 and longer than 17.0 seconds have been removed. TEDLIUM examples with labels shorter than 5 words have been removed. train.csv is sorted by feature sequence length in ascending order.

  • commonvoice_train.csv
  • librispeech_train.csv
  • tatoeba_train.csv
  • tedlium_train.csv
  • timit_train.csv

dev.csv

  • librispeech_dev.csv

test.csv

  • commonvoice_test.csv
  • librispeech_test.csv

Statistics

ipython python/dataset/word_counts.py 
Calculating statistics for /home/gpuinstall/workspace/ctc-asr/data/train.csv
Word based statistics:
        total_words = 10,069,671
        number_unique_words = 81,161
        mean_sentence_length = 14.52 words
        min_sentence_length = 1 words
        max_sentence_length = 84 words
        Most common words:  [('the', 551055), ('to', 306197), ('and', 272729), ('of', 243032), ('a', 223722), ('i', 192151), ('in', 149797), ('that', 146820), ('you', 144244), ('it', 118133)]
        27416 words occurred only 1 time; 37,422 words occurred only 2 times; 49,939 words occurred only 5 times; 58,248 words occurred only 10 times.

Character based statistics:
        total_characters = 52,004,043
        mean_label_length = 75.00 characters
        min_label_length = 2 characters
        max_label_length = 422 characters
        Most common characters: [(' ', 9376326), ('e', 5264177), ('t', 4205041), ('o', 3451023), ('a', 3358945), ('i', 2944773), ('n', 2858788), ('s', 2624239), ('h', 2598897), ('r', 2316473), ('d', 1791668), ('l', 1686896), ('u', 1234080), ('m', 1176076), ('w', 1052166), ('c', 999590), ('y', 974918), ('g', 888446), ('f', 851710), ('p', 710252), ('b', 646150), ('v', 421126), ('k', 387714), ('x', 62547), ('j', 61048), ('q', 34558), ('z', 26416)]
        Most common characters: [' ', 'e', 't', 'o', 'a', 'i', 'n', 's', 'h', 'r', 'd', 'l', 'u', 'm', 'w', 'c', 'y', 'g', 'f', 'p', 'b', 'v', 'k', 'x', 'j', 'q', 'z']

Training

Start training by invoking asr/train.py. Use asr/train.py -- --delete to start a clean run and remove the old checkpoints. Please note that all commands are expected to be executed from the projects root folder. The additional -- before the actual flags begin is used to indicate the end of IPython flags.

The training progress can be monitored using Tensorboard. To start Tensorboard use tensorboard --logdir <checkpoint_directory>. By default it can then be accessed via localhost:6006.

Evaluation

Evaluate the current model by invoking asr/evaluate.py. Use asr/evaluate.py -- --dev to run on the development dataset, instead of the test set.

Prediction

To evaluate a given 16 kHz, mono WAV file use asr/predict.py --input <wav_path>. Using asr/predict.py without --input flag evaluates the provided example file ("I don't understand a word you just said.").