Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+metrics:
+- wer
+model-index:
+- name: openai/whisper-medium-en
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: myst-test
+      type: asr
+      config: en
+      split: test
+    metrics:
+    - type: wer
+      value: 8.85 
+      name: WER
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: cslu_scripted
+      type: asr
+      config: en
+      split: test
+    metrics:
+    - type: wer
+      value: 2.38 
+      name: WER
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: cslu_spontaneous
+      type: asr
+      config: en
+      split: test
+    metrics:
+    - type: wer
+      value: 16.53 
+      name: WER
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: librispeech
+      type: asr
+      config: en
+      split: testclean
+    metrics:
+    - type: wer
+      value: 3.52
+      name: WER
+  
+---
+
+
+# openai/whisper-medium-en
+
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.22987066209316254
+- Wer: 7.945455976651671`
+
+
+## Training and evaluation data
+
+Training data: Myst Train (125 hours)
+Evaluation data: Myst Dev (20.9 hours)
+
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- 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_steps: 500
+- training_steps: 10000
+