asafu-art/deepspeech-kabyle

Automatic Speech Recognition (ASR) - Kabyle # Automatic Speech Recognition (ASR) - DeepSpeech - Kabyle This project develops a working Speech-To-Text module for Kabyle language using Mozilla DeepSpeech, that can be used for any audio processing pipeline. Mozilla DeepSpeech is an open-source automatic speech recognition (ASR) toolkit based on Baidu's Deep Speech research paper. The DeepSpeech project uses Google's TensorFlow to make the implementation easier. This Readme is written for DeepSpeech v0.8.2. Refer to Mozillla DeepSpeech for lastest updates. ## Get Started ### Prerequisites - A running setup of `NVIDIA Docker` - A host directory with 100 GB at least for training and producing intermediate data - Host directory must be writable by `trainer` user (uid 999) (User defined in the Dockerfile) - Kabyle Common Voice dataset `kab.tar.gz` from inside `sources/`subdirectory of your host directory ### Build the image ``` docker build -t dskabyle . ``` ### Parameters Parameters for the model: - `batch_size` : ( default 96 ) to specify the number of elements un a batch for training, dev and test dataset - `n_hidden` : ( default 2048 ) to specify the layer width to use when initializing layers - `epochs` : ( default 75 ) to specify the number of epochs to run training for - `learning_rate` : ( default 0.001 ) to define the learning rate of Adam optimizer - `dropout` : ( default 0.05 ) to define the applied dropout rate for feedforward layers - `lm_alpha`: ( default 0.66 ) defines the alpha hyperparameters of the CTC decoder. Language Model weight. Word insertion weight. - `lm_beta` : ( default 1.45 ) define the beta hyperparameters of the CTC decoder - `beam_width` : ( default 500 ) to define the beam width used in the CTC decoder when building candidate transcriptions - `early_stop` : ( default 1 ) to indicate early stop during training to avoid overfitting - `duplicate_sentence_count` : ( default 1 ) to specify the maximum number of times a sentence can appear in the common-voice corpus These trainin …