Update README.md

README.md

@@ -16,80 +16,95 @@ 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: 9.104533533158486
+      value: 7.14
+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 Medium Basque
 
-This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the asierhv/composite_corpus_eu_v2.1 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.2488
-- Wer: 9.1045
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) 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 9.1045 on the validation set of the `asierhv/composite_corpus_eu_v2.1` dataset, demonstrating improved accuracy compared to the base `whisper-medium` model for Basque.
+* **Performance on Common Voice:** When evaluated on the Mozilla Common Voice 18.0 dataset, the model achieved a WER of 7.14. This indicates strong generalization capabilities and highlights the benefits of the medium-sized model for enhanced accuracy.
 
 ## Model description
 
-More information needed
+This model utilizes the `whisper-medium` architecture, which offers a substantial increase in capacity compared to smaller variants, leading to improved accuracy. By fine-tuning this model on a dedicated Basque speech corpus, it specializes in accurately transcribing Basque speech. The `whisper-medium` model strikes a balance between high accuracy and manageable computational requirements.
 
 ## Intended uses & limitations
 
-More information needed
+**Intended uses:**
+
+* High-precision automatic transcription of Basque speech for professional and research applications.
+* Development of advanced Basque speech-based applications requiring very high accuracy.
+* Research in Basque speech processing where the highest possible accuracy is crucial.
+* Professional transcription services and applications where accuracy is paramount.
+* Use in scenarios where the computational cost is acceptable for the significant improvement in accuracy.
+
+**Limitations:**
+
+* Performance remains influenced by audio quality, with challenges arising from background noise and poor recording conditions.
+* Accuracy may be affected by highly dialectal or informal Basque speech.
+* Despite improved performance, the model may still produce errors, particularly with complex linguistic structures or rare words.
+* The medium model is larger than the small, base, and tiny models, so inference will be slower and require more resources.
 
 ## 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 meticulously curated collection of Basque speech data, designed to maximize the performance of Basque ASR systems.
+* **Evaluation Dataset:** The `test` split of `asierhv/composite_corpus_eu_v2.1`.
 
 ## Training procedure
 
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 6.25e-06
-- train_batch_size: 16
-- eval_batch_size: 8
-- 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: 500
-- training_steps: 10000
-- mixed_precision_training: Native AMP
 
-### Training results
+* **learning_rate:** 6.25e-06
+* **train_batch_size:** 16
+* **eval_batch_size:** 8
+* **seed:** 42
+* **optimizer:** AdamW with betas=(0.9, 0.999) and epsilon=1e-08
+* **lr_scheduler_type:** linear
+* **lr_scheduler_warmup_steps:** 500
+* **training_steps:** 10000
+* **mixed_precision_training:** Native AMP
 
-| Training Loss | Epoch | Step  | Validation Loss | Wer     |
-|:-------------:|:-----:|:-----:|:---------------:|:-------:|
-| 0.3412        | 0.05  | 500   | 0.5112          | 28.8685 |
-| 0.1464        | 0.1   | 1000  | 0.4178          | 20.5570 |
-| 0.2504        | 0.15  | 1500  | 0.3625          | 18.1279 |
-| 0.2615        | 0.2   | 2000  | 0.3236          | 15.5364 |
-| 0.1648        | 0.25  | 2500  | 0.3209          | 13.8129 |
-| 0.0933        | 0.3   | 3000  | 0.2991          | 12.8887 |
-| 0.1016        | 0.35  | 3500  | 0.2823          | 12.4329 |
-| 0.1449        | 0.4   | 4000  | 0.2741          | 11.7460 |
-| 0.151         | 0.45  | 4500  | 0.2791          | 11.5774 |
-| 0.0917        | 0.5   | 5000  | 0.2744          | 11.2402 |
-| 0.0913        | 0.55  | 5500  | 0.2901          | 11.1340 |
-| 0.1085        | 0.6   | 6000  | 0.2663          | 10.3285 |
-| 0.0928        | 0.65  | 6500  | 0.2705          | 10.2910 |
-| 0.0725        | 0.7   | 7000  | 0.2506          | 10.3035 |
-| 0.1216        | 0.75  | 7500  | 0.2758          | 9.7103  |
-| 0.131         | 0.8   | 8000  | 0.2519          | 9.4292  |
-| 0.0525        | 0.85  | 8500  | 0.2602          | 9.3106  |
-| 0.0729        | 0.9   | 9000  | 0.2549          | 9.3606  |
-| 0.0939        | 0.95  | 9500  | 0.2470          | 9.1920  |
-| 0.0639        | 1.0   | 10000 | 0.2488          | 9.1045  |
+### Training results
 
+| Training Loss | Epoch | Step  | Validation Loss | WER      |
+|---------------|-------|-------|-----------------|----------|
+| 0.3412        | 0.05  | 500   | 0.5112          | 28.8685  |
+| 0.1464        | 0.1   | 1000  | 0.4178          | 20.5570  |
+| 0.2504        | 0.15  | 1500  | 0.3625          | 18.1279  |
+| 0.2615        | 0.2   | 2000  | 0.3236          | 15.5364  |
+| 0.1648        | 0.25  | 2500  | 0.3209          | 13.8129  |
+| 0.0933        | 0.3   | 3000  | 0.2991          | 12.8887  |
+| 0.1016        | 0.35  | 3500  | 0.2823          | 12.4329  |
+| 0.1449        | 0.4   | 4000  | 0.2741          | 11.7460  |
+| 0.151         | 0.45  | 4500  | 0.2791          | 11.5774  |
+| 0.0917        | 0.5   | 5000  | 0.2744          | 11.2402  |
+| 0.0913        | 0.55  | 5500  | 0.2901          | 11.1340  |
+| 0.1085        | 0.6   | 6000  | 0.2663          | 10.3285  |
+| 0.0928        | 0.65  | 6500  | 0.2705          | 10.2910  |
+| 0.0725        | 0.7   | 7000  | 0.2506          | 10.3035  |
+| 0.1216        | 0.75  | 7500  | 0.2758          | 9.7103   |
+| 0.131         | 0.8   | 8000  | 0.2519          | 9.4292   |
+| 0.0525        | 0.85  | 8500  | 0.2602          | 9.3106   |
+| 0.0729        | 0.9   | 9000  | 0.2549          | 9.3606   |
+| 0.0939        | 0.95  | 9500  | 0.2470          | 9.1920   |
+| 0.0639        | 1.0   | 10000 | 0.2488          | 9.1045   |
 
 ### 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