Create README.md

README.md

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+---
+license: apache-2.0
+base_model: openai/whisper-large-v3
+tags:
+- generated_from_trainer
+datasets:
+- fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
+metrics:
+- wer
+model-index:
+- name: whisper-large-v3-pt-1000h-ct2
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
+        default
+      type: fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba
+      args: default
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.11132023872721715
+---
+
+<!-- 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-v3-pt-1000h-ct2
+
+This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset. It was converted to the CTranslate2 format.
+It achieves the following results on the evaluation set:
+- Loss: 0.5576
+- Wer: 0.1113
+
+## 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: 5e-06
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 32
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 10000
+- training_steps: 82000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Wer    |
+|:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 0.2717        | 0.39  | 10000 | 0.4143          | 0.1341 |
+| 0.2646        | 0.79  | 20000 | 0.4141          | 0.1284 |
+| 0.2244        | 1.18  | 30000 | 0.5361          | 0.1253 |
+| 0.2056        | 1.57  | 40000 | 0.4714          | 0.1223 |
+| 0.2034        | 1.97  | 50000 | 0.4937          | 0.1195 |
+| 0.1717        | 2.36  | 60000 | 0.5127          | 0.1178 |
+| 0.1692        | 2.75  | 70000 | 0.6040          | 0.1146 |
+| 0.121         | 3.15  | 80000 | 0.5361          | 0.1130 |
+
+
+### Framework versions
+
+- Transformers 4.39.0.dev0
+- Pytorch 2.2.1
+- Datasets 2.18.1.dev0
+- Tokenizers 0.15.2