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---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-tiny-bg
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: bg
split: None
args: bg
metrics:
- name: Wer
type: wer
value: 58.93870930367281
---
<!-- 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-tiny-bg
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8746
- Wer: 58.9387
## 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 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.3458 | 3.6630 | 1000 | 0.7458 | 60.0684 |
| 0.1146 | 7.3260 | 2000 | 0.7719 | 58.7417 |
| 0.0475 | 10.9890 | 3000 | 0.8278 | 57.8149 |
| 0.0245 | 14.6520 | 4000 | 0.8746 | 58.9387 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1
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