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---
language:
- en
license: cc-by-sa-4.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- sage-bergerson/edacc_processed
model-index:
- name: Whisper Large EdAcc
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. -->
# Whisper Large EdAcc
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the EdAcc dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0862
## 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: 5e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- 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 |
|:-------------:|:-------:|:----:|:---------------:|
| 0.8893 | 0.6494 | 200 | 0.6173 |
| 0.4959 | 1.2987 | 400 | 0.5871 |
| 0.4654 | 1.9481 | 600 | 0.5799 |
| 0.308 | 2.5974 | 800 | 0.6095 |
| 0.2504 | 3.2468 | 1000 | 0.6823 |
| 0.1877 | 3.8961 | 1200 | 0.6828 |
| 0.1028 | 4.5455 | 1400 | 0.7804 |
| 0.0896 | 5.1948 | 1600 | 0.8240 |
| 0.0516 | 5.8442 | 1800 | 0.8491 |
| 0.0291 | 6.4935 | 2000 | 0.9035 |
| 0.0276 | 7.1429 | 2200 | 0.9402 |
| 0.0141 | 7.7922 | 2400 | 0.9443 |
| 0.0098 | 8.4416 | 2600 | 0.9972 |
| 0.0073 | 9.0909 | 2800 | 1.0118 |
| 0.0056 | 9.7403 | 3000 | 1.0176 |
| 0.0027 | 10.3896 | 3200 | 1.0468 |
| 0.0021 | 11.0390 | 3400 | 1.0564 |
| 0.0016 | 11.6883 | 3600 | 1.0703 |
| 0.0009 | 12.3377 | 3800 | 1.0840 |
| 0.0011 | 12.9870 | 4000 | 1.0862 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1 |