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---
language:
- sv
- 'no'
- da
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- babelbox/babelbox_voice
- NbAiLab/NST
- NbAiLab/NPSC
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium Nordic
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: sv-SE
split: test
metrics:
- name: Wer
type: wer
value: 11.31
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: da
split: test
metrics:
- name: Wer
type: wer
value: 14.86
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: nn-NO
split: test
metrics:
- name: Wer
type: wer
value: 37.02
---
<!-- 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 Medium Nordic
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the [mozilla-foundation/common_voice_11_0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) (sv-SE, da, nn-NO), the [babelbox/babelbox_voice](https://huggingface.co/datasets/babelbox/babelbox_voice) (Swedish radio), the [NbAiLab/NST](https://huggingface.co/datasets/NbAiLab/NST) (Norwegian radio), the [NbAiLab/NPSC](https://huggingface.co/datasets/NbAiLab/NPSC) (Norwegian parliament) and the [google/fleurs](https://huggingface.co/datasets/google/fleurs) (sv_se, da_dk, nb_no) datasets. The goal is to leverage transfer learning across Nordic languages, which have strong similarities.
It achieves the following results on the common voice Swedish test set:
- Loss: 0.2129
- Wer: 11.3079
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
Please note that a bug during training prevented us from evaluating WER correctly.
Validation loss suggests we started overfitting after 5000/6000 steps.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.3056 | 0.1 | 1000 | 0.2670 | 99.9221 |
| 0.16 | 0.2 | 2000 | 0.2322 | 99.6640 |
| 0.1309 | 0.3 | 3000 | 0.2152 | 98.9759 |
| 0.097 | 0.4 | 4000 | 0.2112 | 100.0 |
| 0.091 | 0.5 | 5000 | 0.2094 | 99.7312 |
| 0.1098 | 0.6 | 6000 | 0.2098 | 98.6077 |
| 0.0637 | 0.7 | 7000 | 0.2148 | 98.4625 |
| 0.0718 | 0.8 | 8000 | 0.2151 | 99.8710 |
| 0.0517 | 0.9 | 9000 | 0.2175 | 97.2342 |
| 0.0465 | 1.0 | 10000 | 0.2129 | 96.3552 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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