Speech recognition using google's tensorflow deep learning framework, sequence-to-sequence neural networks.
Replaces caffe-speech-recognition, see there for some background.
Update Mozilla released DeepSpeech
They achieve good error rates. Free Speech is in good hands, go there if you are an end user. For now this project is only maintained for educational purposes.
Create a decent standalone speech recognition for Linux etc. Some people say we have the models but not enough training data. We disagree: There is plenty of training data (100GB here and 21GB here on openslr.org , synthetic Text to Speech snippets, Movies with transcripts, Gutenberg, YouTube with captions etc etc) we just need a simple yet powerful model. It's only a question of time...
Sample spectrogram, Karen uttering 'zero' with 160 words per minute.
Toy examples:
./number_classifier_tflearn.py
./speaker_classifier_tflearn.py
Some less trivial architectures:
./densenet_layer.py
Later:
./train.sh
./record.py
We are in the process of tackling this project in seriousness. If you want to join the party just drop us an email at [email protected].
Update: Nervana demonstrated that it is possible for 'independents' to build speech recognizers that are state of the art. Update: Mozilla is working on DeepSpeech and just achieved 0% error rate ... on the training set;) Free Speech is in good hands.
- Watch video : https://www.youtube.com/watch?v=u9FPqkuoEJ8
- Understand and correct the corresponding code: lstm-tflearn.py
- Data Augmentation : create on-the-fly modulation of the data: increase the speech frequency, add background noise, alter the pitch etc,...
Extensions to current tensorflow which are probably needed:
- WarpCTC on the GPU see issue
- Incremental collaborative snapshots ('P2P learning') !
- Modular graphs/models + persistance
Even though this project is far from finished we hope it gives you some starting points.
Looking for a tensorflow collaboration / consultant / deep learning contractor? Reach out to [email protected]