Create README.md

README.md

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+---
+library_name: transformers
+language:
+- ug
+license: apache-2.0
+base_model: openai/whisper-small
+tags:
+- generated_from_trainer
+metrics:
+- cer
+- wer
+model-index:
+- name: Whisper Small Fine-tuned with Uyghur Common Voice
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 15
+      type: mozilla-foundation/common_voice_15_0
+    metrics:
+    - name: Wer
+      type: wer
+      value: 28.29947071879802
+    - name: Cer
+      type: cer
+      value: 10.896777936451267
+---
+
+<!-- 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 Small Fine-tuned with Uyghur Common Voice
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Uyghur Common Voice dataset.
+
+This model achieves the following results on the evaluation set:
+- Loss: 1.59201979637146
+- Wer Ortho: 42.97005356986037
+- Wer: 28.29947071879802
+- Cer: 10.896777936451267
+
+## Training and evaluation data
+
+The training was done using the combined train and dev set of common_voice_15_0 (11215 recordings, \~20hrs of audio). 
+
+The testing was done using the test set of THUYG20 as the standard benchmark for Uyghur speech models.
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 300
+- training_steps: 4000
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss |  Epoch  | Step | Validation Loss | Wer Ortho |    Wer    |    Cer    |
+|:-------------:|:-------:|:----:|:---------------:|:---------:|:---------:|:---------:|
+| 0.574400      | 0.7133  | 500  | 1.413890        | 59.765522 | 48.561550 | 17.639905 |
+| 0.299600      | 1.4256  | 1000 | 1.283326        | 52.819004 | 41.377838 | 14.717958 |
+| 0.130600      | 2.1398  | 1500 | 1.379338        | 52.265742 | 38.953389 | 16.260934 |
+| 0.122500      | 2.8531  | 2000 | 1.313730        | 50.245894 | 36.494793 | 14.762585 |
+| 0.060500      | 3.5663  | 2500 | 1.434626        | 47.589356 | 32.998976 | 12.185938 |
+| 0.019500      | 4.2796  | 3000 | 1.526625        | 45.345570 | 30.975756 | 11.307346 |
+| 0.015300      | 4.9929  | 3500 | 1.531676        | 44.120488 | 29.285470 | 11.690021 |
+| 0.003300      | 5.7061  | 4000 | 1.592020        | 42.970054 | 28.299471 | 10.896778 |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3