An opensource speech-to-text software written in tensorflow
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README.md

speechT

An opensource speech-to-text software written in tensorflow. Achieving a Letter Error Rate of 8% and Word Error Rate of 20% on the LibriSpeech test corpus.

Installation

Prerequisites

Python3, portaudio19-dev and ffmpeg are required.

On Ubuntu install via

sudo apt install python3-pip portaudio19-dev ffmpeg

Install via pip3

pip3 install git+https://github.com/timediv/speechT

Architecture

Currently speechT is based on the Wav2Letter paper and the CTC loss function.

The speech corpus from http://www.openslr.org/12/ is automatically downloaded.
Note: The corpus is about 30GB!

Training

The data must be preprocessed before training

speecht-cli preprocess

Then, to run the training, execute

speecht-cli train

Use --help for more details.

You can monitor the training and see other logs in tensorboard

tensorboard --logdir log/

Testing

To evaluate on the whole test set run

speecht-cli evaluate

To evaluate on a single batch

speecht-cli evaluate --step-count 1

By default greedy decoding is used. See section Using a language model on how to use KenLM for decoding.

Use --help for more details.

Live usage

To record using your microphone and then print the prediction run

speecht-cli record

Use --help for more details.

Trained weights

You don't have the resources to train on your own? Download the weights from here

mkdir train
tar xf speechT-weights.tgz -C train/

Then you can use the model with e.g. evaluate

speecht-cli evaluate --run-name best_run

Using a language model

If you'd like to use KenLM as a language model for decoding you need to compile and install tensorflow-with-kenlm. If you only require the CPU version of tensorflow for linux you can also download it here instead.

Download all the necessary files from here, then

tar xf kenlm-english.tgz
speecht-cli evaluate --language-model kenlm-english/

Results

With the default parameters trained for about 5 to 6 days on a Nvidia Titan X.

Loss curve for speech recognizer training

Overall statistics

Average Letter Edit Distance: 7.7125
Average Letter Error Rate: 8%
Average Word Edit Distance: 3.801953125
Average Word Error Rate: 20%

LER, WER and predictions on a few examples

expected: but that is kaffar's knife
decoded: but that is caffr's klife 
LED: 4 LER: 0.15 WED: 2 WER: 0.40

expected: he moved uneasily and his chair creaked
decoded: he moved uneasily in his chair creet
LED: 5 LER: 0.13 WED: 2 WER: 0.29

expected: it is indeed true that the importance of tact and skill in the training of the young and of cultivating their reason and securing their affection can not be overrated
decoded: it is indeed true that the importance of tact and skill in the training of the young and of cultivating their reason and so carrying their affection can not be o rated
LED: 8 LER: 0.05 WED: 4 WER: 0.13

expected: she pressed his hand gently in gratitude
decoded: she pressed his hand gently in gratitude
LED: 0 LER: 0.00 WED: 0 WER: 0.00

expected: don't worry sizzle dear it'll all come right pretty soon
decoded: don't worry i l dear it all come riprety soon 
LED: 13 LER: 0.23 WED: 5 WER: 0.50

expected: may we see gates at once asked kenneth
decoded: may we see gates at once asked keneth 
LED: 2 LER: 0.05 WED: 1 WER: 0.12

The whole evaluation log can be found here.