update model card README.md

README.md

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+---
+language:
+- de
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- facebook/voxpopuli
+metrics:
+- wer
+model-index:
+- name: whisper-large-v2-german
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: VOXPOPULI
+      type: facebook/voxpopuli
+      config: de
+      split: test
+      args: de
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.12201852946974177
+---
+
+<!-- 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-large-v2-german
+
+This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the VOXPOPULI dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2841
+- Wer Ortho: 0.1517
+- Wer: 0.1220
+
+## 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: 3e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: constant_with_warmup
+- lr_scheduler_warmup_steps: 50
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
+| 0.2616        | 1.0   | 1679 | 0.2695          | 0.1601    | 0.1303 |
+| 0.1801        | 2.0   | 3358 | 0.2690          | 0.1554    | 0.1235 |
+| 0.1185        | 3.0   | 5037 | 0.2841          | 0.1517    | 0.1220 |
+
+
+### Framework versions
+
+- Transformers 4.30.0.dev0
+- Pytorch 2.0.1+cu117
+- Datasets 2.13.1
+- Tokenizers 0.13.3