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---
language:
- eu
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Small Basque
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_0 eu
type: mozilla-foundation/common_voice_16_0
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 12.012786552211754
---
<!-- 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 Small Basque
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_16_0 eu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1996
- Wer: 12.0128
If you need to use this model with whisper.cpp, you can download the ggml file: [ggml-small-eu.bin](https://huggingface.co/xezpeleta/whisper-small-eu/resolve/main/ggml-small.eu.bin)
## 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: 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2009 | 1.04 | 1000 | 0.2446 | 17.6881 |
| 0.0759 | 2.09 | 2000 | 0.2102 | 14.2584 |
| 0.0264 | 3.13 | 3000 | 0.2200 | 13.6898 |
| 0.0633 | 5.02 | 4000 | 0.1955 | 12.5535 |
| 0.0199 | 6.06 | 5000 | 0.1996 | 12.0128 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|