transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_13_0 base_model:AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
base_model: AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: finetuning-wav2vec-large-swahili-asr-model_v9
  results: []
---

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# finetuning-wav2vec-large-swahili-asr-model_v9

This model is a fine-tuned version of [AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw](https://huggingface.co/AntonyG/fine-tune-wav2vec2-large-xls-r-1b-sw) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2818
- eval_wer: 0.1945
- eval_runtime: 657.4969
- eval_samples_per_second: 17.142
- eval_steps_per_second: 2.143
- epoch: 9.69
- step: 14000

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 15
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
