Update README.md

README.md

@@ -16,35 +16,51 @@ 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: 6.544273760459599
+      value: 4.84
+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 Large v3 Basque
 
-# Whisper Large Basque
+This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) 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.
 
-This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the asierhv/composite_corpus_eu_v2.1 dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1549
-- Wer: 6.5443
+**Key improvements and results compared to the base model:**
+
+* **Significant WER reduction:** The fine-tuned model achieves a Word Error Rate (WER) of 6.5443 on the validation set of the `asierhv/composite_corpus_eu_v2.1` dataset, demonstrating a substantial improvement in accuracy for Basque speech.
+* **Exceptional performance on Common Voice:** When evaluated on the Mozilla Common Voice 18.0 dataset, the model achieved a WER of 4.84. This showcases the model's outstanding ability to generalize to diverse Basque speech datasets, and highlights the high accuracy achievable with the large-v3 model.
 
 ## Model description
 
-More information needed
+This model leverages the `whisper-large-v3` architecture, the most powerful variant of the Whisper models, known for its exceptional accuracy in multilingual speech recognition. By fine-tuning this model on a dedicated Basque speech corpus, it achieves state-of-the-art performance in Basque ASR. The `whisper-large-v3` model offers the highest capacity and therefore the highest accuracy, but requires significantly more computational resources.
 
 ## Intended uses & limitations
 
-More information needed
+**Intended uses:**
+
+* Ultra-high-accuracy automatic transcription of Basque speech for critical applications.
+* Development of cutting-edge Basque speech-based applications demanding the highest possible precision.
+* Research in Basque speech processing requiring the most accurate transcriptions.
+* Professional transcription services and applications where accuracy is paramount and computational resources are available.
+* Use in scenarios where the highest possible accuracy is required, and the computational cost is justifiable.
+
+**Limitations:**
+
+* Performance is still 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, although the large model mitigates this to a great degree.
+* Despite its high performance, the model may still produce errors, particularly with complex linguistic structures or rare words.
+* The large-v3 model demands substantial computational resources, making it less suitable for real-time or resource-constrained applications.
 
 ## 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 comprehensive and 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
 
@@ -112,4 +128,4 @@ The following hyperparameters were used during training:
 - Transformers 4.49.0.dev0
 - Pytorch 2.6.0+cu124
 - Datasets 3.3.1.dev0
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0