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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-swahili-large-v0.1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: sw
split: test
args: sw
metrics:
- name: Wer
type: wer
value: 26.32352799853929
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-swahili-large-v0.1
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4162
- Wer: 26.3235
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.1388 | 0.0681 | 250 | 0.6673 | 65.1304 |
| 0.4408 | 0.1362 | 500 | 0.5600 | 33.9259 |
| 0.3612 | 0.2042 | 750 | 0.4609 | 30.1521 |
| 0.3057 | 0.2723 | 1000 | 0.4162 | 26.3235 |
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
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