Upload README.md

README.md

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+---
+language:
+- sv
+license: apache-2.0
+tags:
+- hf-asr-leaderboard
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_11_0
+metrics:
+- wer
+model-index:
+- name: Whisper Small Swedish
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 11.0
+      type: mozilla-foundation/common_voice_11_0
+      config: sv-SE
+      split: test
+      args: 'config: sv, split: test'
+    metrics:
+    - name: Wer
+      type: wer
+      value: 19.942996961630502
+---
+
+<!-- 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 Swedish
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3442
+- Wer: 19.9430
+
+Check [here](https://drive.google.com/file/d/10Nd0rMnLM5yEpMhI26sHVpQGcSmv2t2X/view?usp=sharing) for the result of checkpoint-4000
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- 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
+
+### Framework versions
+- Transformers 4.26.0.dev0
+- Pytorch 1.13.0+cu116
+- Datasets 2.7.1
+- Tokenizers 0.13.2