https://github.com/coolzhao/kerasdeepspeech
A Keras CTC implementation of Baidu's DeepSpeech for model experimentation
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A Keras CTC implementation of Baidu's DeepSpeech for model experimentation
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Fork of robmsmt/KerasDeepSpeech
Created over 6 years ago
· Last pushed over 8 years ago
https://github.com/coolzhao/KerasDeepSpeech/blob/master/
# Keras DeepSpeech [](https://travis-ci.org/robmsmt/KerasDeepSpeech) Repository for experimenting with different CTC based model designs for ASR. Supports [live recording and testing](data/live-rec-test.py) of speech and quickly creates customised datasets using [own-voice dataset creation scripts](data/data-recorder.py)! ## OVERVIEW## SETUP 1. Recommended > use virtualenv installed with python2.7 (3.x untested and will not work with Core ML) 2. `git clone https://github.com/robmsmt/KerasDeepSpeech` 3. `pip install -r requirements.txt` 4. Get the data using the import/download scripts in the  folder, LibriSpeech is a good example. 5. Download the language model (large file) run `./lm/get_lm.sh` ## RUN 1. To Train, simply run `python run-train.py` In order to specify training/validation files use `python run-train.py --train_files
--valid_files ` (see run-train for complete arguments list) 2. To Test, run `python run-test.py --test_files ` ## CREDIT 1. Mozilla [DeepSpeech](https://github.com/mozilla/DeepSpeech) 2. Baidu [DS1](https://arxiv.org/abs/1412.5567) & [DS2](https://arxiv.org/abs/1512.02595) papers ## Licence The content of this project itself is licensed under the GNU General Public License. Copyright 2018 ## Contributing ## Have a question? Like the tool? Don't like it? Open an issue and let's talk about it! Pull requests are appreciated!
Owner
- Login: coolzhao
- Kind: user
- Repositories: 7
- Profile: https://github.com/coolzhao
## SETUP
1. Recommended > use virtualenv installed with python2.7 (3.x untested and will not work with Core ML)
2. `git clone https://github.com/robmsmt/KerasDeepSpeech`
3. `pip install -r requirements.txt`
4. Get the data using the import/download scripts in the  folder, LibriSpeech is a good example.
5. Download the language model (large file) run `./lm/get_lm.sh`
## RUN
1. To Train, simply run `python run-train.py` In order to specify training/validation files use `python run-train.py --train_files