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---
language:
- sv
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Large Swedish
results:
- task:
name: Automatic Speech Recognition
type: 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:
- name: Wer
type: wer
value: 9.220639613007256
---
<!-- 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
|