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---
language:
- sv
license: apache-2.0
tags:
- i-dont-know-what-im-doing
- generated_from_trainer
datasets:
- fimster/NST_small_whisper
metrics:
- wer
model-index:
- name: Whisper Small sv-SE NST - Lab 2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: NST Swedish ASR
type: fimster/NST_small_whisper
config: speech
split: None
args: 'config: speech, split: test'
metrics:
- name: Wer
type: wer
value: 10.167794316644112
---
<!-- 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 sv-SE NST - Lab 2
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the NST Swedish ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1305
- Wer: 10.1678
## 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: 16
- eval_batch_size: 8
- 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: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1635 | 0.67 | 1000 | 0.1694 | 13.4993 |
| 0.07 | 1.33 | 2000 | 0.1431 | 11.3802 |
| 0.0597 | 2.0 | 3000 | 0.1302 | 10.4682 |
| 0.0193 | 2.67 | 4000 | 0.1305 | 10.1678 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
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