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---
base_model: distil-whisper/distil-large-v3
datasets:
- audiofolder
library_name: peft
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: distil_whisper-v3-LoRA-en_students_test_2
results: []
---
<!-- 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. -->
# distil_whisper-v3-LoRA-en_students_test_2
This model is a fine-tuned version of [distil-whisper/distil-large-v3](https://huggingface.co/distil-whisper/distil-large-v3) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6839
- Wer: 18.4361
## 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: 28
- eval_batch_size: 28
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 100000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.5189 | 0.4444 | 500 | 1.1913 | 25.9108 |
| 1.1727 | 0.8889 | 1000 | 0.9531 | 24.5396 |
| 1.1341 | 1.3333 | 1500 | 0.8688 | 22.2761 |
| 1.0152 | 1.7778 | 2000 | 0.8174 | 20.8792 |
| 1.0589 | 2.2222 | 2500 | 0.7855 | 20.7595 |
| 0.9793 | 2.6667 | 3000 | 0.7611 | 22.2846 |
| 0.9594 | 3.1111 | 3500 | 0.7442 | 20.3860 |
| 1.0031 | 3.5556 | 4000 | 0.7303 | 18.5045 |
| 0.9525 | 4.0 | 4500 | 0.7199 | 18.1054 |
| 0.8729 | 4.4444 | 5000 | 0.7105 | 19.3170 |
| 1.0031 | 4.8889 | 5500 | 0.7028 | 19.7446 |
| 0.9273 | 5.3333 | 6000 | 0.6966 | 19.7189 |
| 0.9174 | 5.7778 | 6500 | 0.6896 | 18.4475 |
| 0.8842 | 6.2222 | 7000 | 0.6839 | 18.4361 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.4
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1 |