Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- whisper-event
+- generated_from_trainer
+datasets:
+- facebook/multilingual_librispeech
+metrics:
+- wer
+model-index:
+- name: Whisper medium French MLS
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: facebook/multilingual_librispeech French
+      type: facebook/multilingual_librispeech
+      config: french
+      split: test
+      args: french
+    metrics:
+    - name: Wer
+      type: wer
+      value: 6.497245494665325
+---
+
+<!-- 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 French MLS
+
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the facebook/multilingual_librispeech French dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1239
+- Wer: 6.4972
+
+## 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: 64
+- eval_batch_size: 32
+- 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: 1000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 0.1424        | 1.0   | 1000 | 0.1239          | 6.4972 |
+
+
+### Framework versions
+
+- Transformers 4.26.0.dev0
+- Pytorch 1.13.0+cu117
+- Datasets 2.7.1.dev0
+- Tokenizers 0.13.2