Model save

README.md

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+---
+license: apache-2.0
+base_model: openai/whisper-large-v3
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_18_0
+metrics:
+- wer
+model-index:
+- name: whisper-large-v3-pt-3000h-3
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_18_0
+      type: common_voice_18_0
+      config: pt
+      split: None
+      args: pt
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.10174567584881486
+---
+
+<!-- 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-3000h-3
+
+This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the common_voice_18_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1478
+- Wer: 0.1017
+
+## 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: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 1000
+- num_epochs: 2.0
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Wer    |
+|:-------------:|:------:|:----:|:---------------:|:------:|
+| 0.13          | 0.9998 | 691  | 0.1486          | 0.1037 |
+| 0.0844        | 1.9998 | 1382 | 0.1478          | 0.1017 |
+
+
+### Framework versions
+
+- Transformers 4.44.0.dev0
+- Pytorch 2.4.0+cu124
+- Datasets 2.18.1.dev0
+- Tokenizers 0.19.1

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+}

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