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