transformers safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer fi dataset:mozilla-foundation/common_voice_13_0 base_model:openai/whisper-large-v3 license:apache-2.0 model-index endpoints_compatible region:us
---
language:
  - fi
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_13_0
metrics:
  - wer
model-index:
  - name: Whisper Large v3 Fine-Tuned Finnish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 13.0
          type: mozilla-foundation/common_voice_13_0
          config: fi
          split: test
        metrics:
          - name: Wer
            type: wer
            value: 23.707
---

# Whisper Large v3 Fine-Tuned Finnish

<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 13.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2178
- Wer: 23.707

## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- lr_scheduler_kwargs = { 'lr_end': 1e-07 }
- training_steps: 800
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6193        | 0.21  | 50   | 0.2905          | 29.1920 |
| 0.3171        | 0.84  | 200  | 0.3             | 27.02   |
| 0.1224        | 1.68  | 400  | 0.2906          | 28.115  |
| 0.041         | 2.53  | 600  | 0.2477          | 25.179  |
| 0.0098        | 3.37  | 800  | 0.2178          | 23.707  |

### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
