Update README.md

README.md

@@ -1,3 +1,43 @@
+---
+datasets:
+- asierhv/composite_corpus_eu_v2.1
+---
+# mHubert Basque Discrete Units (k=1000, L9)
+
+## Model Summary
+This repository provides a fine-tuned **mHubert** (Multilingual HuBERT) model specifically optimized for the **Basque language**. It is designed to transform raw audio signals into discrete unit sequences, which serve as a compact, symbolic representation of speech.
+
+The model extracts high-level acoustic and phonetic features from the **9th transformer layer** (Layer 9). These features are then quantized using a KMeans model with **1000 clusters**. This representation is widely used in generative speech research, including unit-based Vocoders.
+
+## Key Features
+* **Base Model**: mHubert (Multilingual HuBERT) fine-tuned for Basque.
+* **Quantization**: KMeans with $k=1000$ clusters.
+* **Extraction Layer**: Layer 9 (L9).
+* **Input**: 16 kHz Basque speech audio.
+* **Output**: 1D sequence of discrete unit IDs (indices 0–999).
+* **Primary Use Case**: Speech discretization for generative modeling and unit-based synthesis.
+
+## Technical Specifications
+| Feature | Detail |
+| :--- | :--- |
+| **Sampling Rate** | 16,000 Hz |
+| **Transformer Layers** | 12 |
+| **Feature Layer** | 9 |
+| **Vocabulary Size** | 1000 units |
+| **Language** | Basque (Euskara) |
+
+## How to Use
+
+To extract discrete units from an audio file, you will need `transformers`, `torch`, `torchaudio`, and `joblib`.
+
+### Installation
+```bash
+pip install torch torchaudio transformers joblib huggingface_hub
+
+```
+
+### Inference
+
 ```
 
 from huggingface_hub import hf_hub_download