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README.md

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+---
+license: apache-2.0
+base_model: openai/whisper-medium
+tags:
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_16_1
+metrics:
+- wer
+model-index:
+- name: whisper-large-v3-pt-cv16-fleurs
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: mozilla-foundation/common_voice_16_1 pt
+      type: mozilla-foundation/common_voice_16_1
+      config: pt
+      split: test
+      args: pt
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.11905377038591959
+---
+
+<!-- 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-cv16-fleurs
+
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the mozilla-foundation/common_voice_16_1 pt dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1975
+- Wer: 0.1191
+
+## 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-06
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 2
+- total_eval_batch_size: 2
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2000
+- training_steps: 5000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 0.2614        | 0.06  | 1000 | 0.2986          | 0.1466 |
+| 0.2632        | 0.13  | 2000 | 0.2244          | 0.1316 |
+| 0.1694        | 0.19  | 3000 | 0.2086          | 0.1234 |
+| 0.1658        | 0.26  | 4000 | 0.1987          | 0.1205 |
+| 0.1391        | 0.32  | 5000 | 0.1975          | 0.1191 |
+
+
+### Framework versions
+
+- Transformers 4.39.0.dev0
+- Pytorch 2.2.1
+- Datasets 2.16.1
+- Tokenizers 0.15.2

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