Update README.md

README.md

@@ -16,70 +16,84 @@ model-index:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: asierhv/composite_corpus_eu_v2.1
-      type: asierhv/composite_corpus_eu_v2.1
+      name: Mozilla Common Voice 18.0
+      type: mozilla-foundation/common_voice_18_0
     metrics:
     - name: Wer
       type: wer
-      value: 14.849506681653555
+      value: 13.56
+language:
+- eu
 ---
 
-<!-- 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 Tiny Basque
 
-This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the asierhv/composite_corpus_eu_v2.1 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3719
-- Wer: 14.8495
+This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) specifically for Basque (eu) language Automatic Speech Recognition (ASR). It was trained on the [asierhv/composite_corpus_eu_v2.1](https://huggingface.co/datasets/asierhv/composite_corpus_eu_v2.1) dataset, which is a composite corpus designed to improve Basque ASR performance.
+
+**Key improvements and results compared to the base model:**
+
+* **Significant WER reduction:** The fine-tuned model achieves a Word Error Rate (WER) of 14.8495 on the validation set of the `asierhv/composite_corpus_eu_v2.1` dataset, demonstrating improved accuracy compared to the base `whisper-tiny` model for Basque.
+* **Performance on Common Voice:** When evaluated on the Mozilla Common Voice 18.0 dataset, the model achieved a WER of 13.56. This demonstrates the model's ability to generalize to other Basque speech datasets.
 
 ## Model description
 
-More information needed
+This model leverages the power of the Whisper architecture, originally developed by OpenAI, and adapts it to the specific nuances of the Basque language. By fine-tuning the `whisper-tiny` model on a comprehensive Basque speech corpus, it learns to accurately transcribe spoken Basque. The `whisper-tiny` model is the smallest of the whisper models, providing a good balance between speed and accuracy.
 
 ## Intended uses & limitations
 
-More information needed
+**Intended uses:**
+
+* Automatic transcription of Basque speech.
+* Development of Basque speech-based applications.
+* Research on Basque speech processing.
+* Accessibility tools for Basque speakers.
+
+**Limitations:**
+
+* Performance may vary depending on the quality of the audio input (e.g., background noise, recording quality).
+* The model might struggle with highly dialectal or informal speech.
+* While the model shows improved performance, it may still produce errors, especially with complex sentences or uncommon words.
+* The model is based on the small version of whisper, and thus, accuracy may be improved with larger models.
 
 ## Training and evaluation data
 
-More information needed
+* **Training dataset:** [asierhv/composite_corpus_eu_v2.1](https://huggingface.co/datasets/asierhv/composite_corpus_eu_v2.1). This dataset is a composite corpus of Basque speech data, designed to improve the performance of Basque ASR systems.
+* **Evaluation Dataset:** The `test` portion of `asierhv/composite_corpus_eu_v2.1`.
 
 ## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 3.75e-05
-- train_batch_size: 32
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 1000
-- training_steps: 10000
-- mixed_precision_training: Native AMP
 
-### Training results
+* **learning_rate:** 3.75e-05
+* **train_batch_size:** 32
+* **eval_batch_size:** 16
+* **seed:** 42
+* **optimizer:** AdamW with betas=(0.9, 0.999) and epsilon=1e-08
+* **lr_scheduler_type:** linear
+* **lr_scheduler_warmup_steps:** 1000
+* **training_steps:** 10000
+* **mixed_precision_training:** Native AMP
 
-| Training Loss | Epoch | Step  | Validation Loss | Wer     |
-|:-------------:|:-----:|:-----:|:---------------:|:-------:|
-| 0.586         | 0.1   | 1000  | 0.6249          | 34.1639 |
-| 0.3145        | 0.2   | 2000  | 0.5048          | 25.2591 |
-| 0.225         | 0.3   | 3000  | 0.4839          | 22.0557 |
-| 0.3003        | 0.4   | 4000  | 0.4540          | 20.3072 |
-| 0.132         | 0.5   | 5000  | 0.4574          | 19.0146 |
-| 0.1588        | 0.6   | 6000  | 0.4380          | 17.8219 |
-| 0.1841        | 0.7   | 7000  | 0.4395          | 16.6667 |
-| 0.143         | 0.8   | 8000  | 0.3719          | 15.4490 |
-| 0.0967        | 0.9   | 9000  | 0.3685          | 15.1368 |
-| 0.1059        | 1.0   | 10000 | 0.3719          | 14.8495 |
+### Training results
 
+| Training Loss | Epoch | Step  | Validation Loss | WER      |
+|---------------|-------|-------|-----------------|----------|
+| 0.586         | 0.1   | 1000  | 0.6249          | 34.1639  |
+| 0.3145        | 0.2   | 2000  | 0.5048          | 25.2591  |
+| 0.225         | 0.3   | 3000  | 0.4839          | 22.0557  |
+| 0.3003        | 0.4   | 4000  | 0.4540          | 20.3072  |
+| 0.132         | 0.5   | 5000  | 0.4574          | 19.0146  |
+| 0.1588        | 0.6   | 6000  | 0.4380          | 17.8219  |
+| 0.1841        | 0.7   | 7000  | 0.4395          | 16.6667  |
+| 0.143         | 0.8   | 8000  | 0.3719          | 15.4490  |
+| 0.0967        | 0.9   | 9000  | 0.3685          | 15.1368  |
+| 0.1059        | 1.0   | 10000 | 0.3719          | 14.8495  |
 
 ### Framework versions
 
-- Transformers 4.49.0.dev0
-- Pytorch 2.6.0+cu124
-- Datasets 3.3.1.dev0
-- Tokenizers 0.21.0
+* Transformers 4.49.0.dev0
+* Pytorch 2.6.0+cu124
+* Datasets 3.3.1.dev0
+* Tokenizers 0.21.0