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---
library_name: transformers
language:
- eu
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Medium Basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_17_0 eu
type: mozilla-foundation/common_voice_17_0
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 8.8020814247499
---
<!-- 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 Medium Basque
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_17_0 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1787
- Wer: 8.8021
## 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: 6.25e-06
- 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: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3171 | 0.0625 | 500 | 0.3369 | 25.5304 |
| 0.1852 | 0.125 | 1000 | 0.2409 | 17.3110 |
| 0.2353 | 0.1875 | 1500 | 0.2050 | 14.2228 |
| 0.1569 | 1.037 | 2000 | 0.1815 | 12.2861 |
| 0.125 | 1.0995 | 2500 | 0.1692 | 11.1144 |
| 0.12 | 1.162 | 3000 | 0.1600 | 10.6975 |
| 0.069 | 2.0115 | 3500 | 0.1540 | 9.7649 |
| 0.0606 | 2.074 | 4000 | 0.1550 | 9.8199 |
| 0.0434 | 2.1365 | 4500 | 0.1580 | 9.4571 |
| 0.0455 | 2.199 | 5000 | 0.1533 | 9.1410 |
| 0.0216 | 3.0485 | 5500 | 0.1620 | 9.0842 |
| 0.017 | 3.111 | 6000 | 0.1704 | 9.0980 |
| 0.0174 | 3.1735 | 6500 | 0.1681 | 9.0723 |
| 0.0098 | 4.023 | 7000 | 0.1725 | 8.8625 |
| 0.0076 | 4.0855 | 7500 | 0.1765 | 8.8351 |
| 0.007 | 4.148 | 8000 | 0.1787 | 8.8021 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.2.dev0
- Tokenizers 0.20.0
|