---
language:
- sv
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-large-v2
model-index:
- name: Whisper Large Swedish
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: mozilla-foundation/common_voice_11_0 sv-SE
      type: mozilla-foundation/common_voice_11_0
      config: sv-SE
      split: test
      args: sv-SE
    metrics:
    - type: wer
      value: 9.220639613007256
      name: Wer
---

<!-- 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 Swedish

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) trained on NST Swedish ASR and evaluated on Common Voice 11 testset. 
It achieves the following results on the evaluation set

- Loss: 0.2337
- Wer: 9.2206

## Model description

openai/whisper-large-v2 had a WER of 10.6 on Common Voice 9 testset. 

## Intended uses & limitations

More information needed

## Training and evaluation data

The training dataset contains 276 000 examples and with a batch size of 64 and training 5000 it is 1.14 epochs. 
More training data or more epochs would probably improve the result even further.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0695        | 0.2   | 1000 | 0.2695          | 12.4671 |
| 0.0524        | 0.4   | 2000 | 0.2659          | 11.6367 |
| 0.046         | 0.6   | 3000 | 0.2402          | 10.6557 |
| 0.0342        | 0.8   | 4000 | 0.2339          | 10.1774 |
| 0.0224        | 1.14  | 5000 | 0.2337          | 9.2206  |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2