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ASR_SWAHILI – AI Model by DicksonMassawe | AlphaNeural AI

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DicksonMassawe/
ASR_SWAHILI

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automatic-speech-recognition

generated_from_trainer

endpoints_compatible

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wav2vec_large_xlsr_swahili

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 32
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 1

Training results

Framework versions

Transformers 4.30.2
Pytorch 1.10.0
Datasets 2.13.0
Tokenizers 0.13.3