Model This model is a fine tuned version of facebook/wav2vec2 xls r 300m on the Timit dataset. Check this notebook for training detail. Usage Approach 1: Using HuggingFace's pipeline, this will cover everything end to end from raw audio input to text output. Approach 2: More custom way to predict phonemes. Training and evaluation data We use DARPA TIMIT dataset for this model. We split into 80/10/10 for training, validation, and testing respectively. That roughly corresponds to about 137/17/17 minutes. The model obtained 7.996% on this test set. Training procedure Training hyperparameters The following hyperparameters were used during training: learning rate: 3e 05 train batch size: 8 eval batch size: 8 seed: 42 gradient accumulation steps: 4 total train batch size: 32 optimizer: Adam with betas=(0.9,0.999) and epsilon=1e 08 lr scheduler type: linear lr scheduler warmup steps: 2000 training steps: 10000 mixed precision training: Native AMP Framework versions Transformers 4.17.0.dev0 Pytorch 1.10.2+cu102 Datasets 1.18.2.dev0 Tokenizers 0.11.0 Citation
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