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---
license: apache-2.0
tags:
- generated_from_trainer
- whisper-event
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Medium Sv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: sv-SE
split: test
args: sv-SE
metrics:
- name: Wer
type: wer
value: 10.712174146734748
---
<!-- 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. -->
# openai/whisper-medium
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) trained on
NST Swedish ASR and evaluated on Common Voice 11 testset. It achieves the following results on the evaluation set:
- Loss: 0.2636
- Wer: 10.7122
## 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
- gradient_accumulation_steps: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0746 | 0.2 | 1000 | 0.2904 | 13.4695 |
| 0.0564 | 0.4 | 2000 | 0.3121 | 13.2384 |
| 0.0532 | 0.6 | 3000 | 0.2862 | 11.9726 |
| 0.0387 | 0.8 | 4000 | 0.2629 | 11.6931 |
| 0.0279 | 1.14 | 5000 | 0.2636 | 10.7122 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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