Update README.md

README.md

@@ -102,15 +102,18 @@ The **Simba** family consists of state-of-the-art models fine-tuned using SimbaB
   - **Simba-M** (MMS-1b-all)
   - **Simba-H** (AfriHuBERT)
       
-| **ASR Models**   | **Architecture**  | **🤗 Hugging Face Model Card** | **Status** |
-|---------|:------------------:| :------------------:| :------------------:|    
-| 🔥**Simba-S**🔥|    SeamlessM4T-v2  |  🤗 [https://huggingface.co/UBC-NLP/Simba-S](https://huggingface.co/UBC-NLP/Simba-S) | ✅ Released |
-| 🔥**Simba-W**🔥|    Whisper         |  🤗 [https://huggingface.co/UBC-NLP/Simba-W](https://huggingface.co/UBC-NLP/Simba-W) | ✅ Released | 
-| 🔥**Simba-X**🔥|    Wav2Vec2        |  🤗 [https://huggingface.co/UBC-NLP/Simba-X](https://huggingface.co/UBC-NLP/Simba-X) | ✅ Released |   
-| 🔥**Simba-M**🔥|    MMS             |  🤗 [https://huggingface.co/UBC-NLP/Simba-M](https://huggingface.co/UBC-NLP/Simba-M) | ✅ Released |   
-| 🔥**Simba-H**🔥|    HuBERT          |  🤗 [https://huggingface.co/UBC-NLP/Simba-H](https://huggingface.co/UBC-NLP/Simba-H) | ✅ Released |   
+🌐 Explore the Frontier
+
+| **ASR Models**   | **Architecture**  | **#Parameters** | **🤗 Hugging Face Model Card** | **Status** |
+|---------|:------------------:| :------------------:| :------------------:|:------------------:|    
+| 🔥**Simba-S**🔥|    SeamlessM4T-v2  |  2.3B | 🤗 [https://huggingface.co/UBC-NLP/Simba-S](https://huggingface.co/UBC-NLP/Simba-S) | ✅ Released |
+| 🔥**Simba-W**🔥|    Whisper         |  1.5B | 🤗 [https://huggingface.co/UBC-NLP/Simba-W](https://huggingface.co/UBC-NLP/Simba-W) | ✅ Released | 
+| 🔥**Simba-X**🔥|    Wav2Vec2        |  1B | 🤗 [https://huggingface.co/UBC-NLP/Simba-X](https://huggingface.co/UBC-NLP/Simba-X) | ✅ Released |   
+| 🔥**Simba-M**🔥|    MMS             |  1B | 🤗 [https://huggingface.co/UBC-NLP/Simba-M](https://huggingface.co/UBC-NLP/Simba-M) | ✅ Released |   
+| 🔥**Simba-H**🔥|    HuBERT          |  94M | 🤗 [https://huggingface.co/UBC-NLP/Simba-H](https://huggingface.co/UBC-NLP/Simba-H) | ✅ Released |   
+
+* **Simba-S** emerged as the best-performing ASR model overall.
 
-* **Simba-S** (based on SeamlessM4T-v2-MT) emerged as the best-performing ASR model overall.
 
 **🧩 Usage Example**
 
@@ -125,7 +128,9 @@ asr_pipeline = pipeline(
     model="UBC-NLP/Simba-S" #Simba mdoels `UBC-NLP/Simba-S`, `UBC-NLP/Simba-W`, `UBC-NLP/Simba-X`, `UBC-NLP/Simba-H`, `UBC-NLP/Simba-M`
 )
 
+##### Load the multilingual African adapter (Only for  `UBC-NLP/Simba-M`)
 asr_pipeline.model.load_adapter("multilingual_african")  # Only for  `UBC-NLP/Simba-M`
+###########################
 
 # Transcribe audio from file
 result = asr_pipeline("https://africa.dlnlp.ai/simba/audio/afr_Lwazi_afr_test_idx3889.wav")
@@ -140,122 +145,36 @@ result = asr_pipeline({
 print(result["text"])
 
 ```
-Get started with Simba models in minutes using our interactive Colab notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/UBC-NLP/simba/edit/main/simba_models.ipynb)
-
-
-## Citation
-
-If you use the Simba models or SimbaBench  benchmark for your scientific publication, or if you find the resources in this website useful, please cite our paper.
 
-```bibtex
+#### Example Outputs
 
-@inproceedings{elmadany-etal-2025-voice,
-    title = "Voice of a Continent: Mapping {A}frica{'}s Speech Technology Frontier",
-    author = "Elmadany, AbdelRahim A.  and
-      Kwon, Sang Yun  and
-      Toyin, Hawau Olamide  and
-      Alcoba Inciarte, Alcides  and
-      Aldarmaki, Hanan  and
-      Abdul-Mageed, Muhammad",
-    editor = "Christodoulopoulos, Christos  and
-      Chakraborty, Tanmoy  and
-      Rose, Carolyn  and
-      Peng, Violet",
-    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
-    month = nov,
-    year = "2025",
-    address = "Suzhou, China",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2025.emnlp-main.559/",
-    doi = "10.18653/v1/2025.emnlp-main.559",
-    pages = "11039--11061",
-    ISBN = "979-8-89176-332-6",
-}
+Using the same audio file with different Simba models:
 
+```python
+# Simba-S
+{'text': 'watter verontwaardiging sou daar, in ons binneste gewees het.'}
 ```
 
-<div align="center">
-
-<img src="https://africa.dlnlp.ai/simba/images/VoC_simba" alt="VoC Simba Models Logo">
-
-
-[![EMNLP 2025 Paper](https://img.shields.io/badge/EMNLP_2025-Paper-B31B1B?style=for-the-badge&logo=arxiv&logoColor=B31B1B&labelColor=FFCDD2)](https://aclanthology.org/2025.emnlp-main.559/)
-[![Official Website](https://img.shields.io/badge/Official-Website-2EA44F?style=for-the-badge&logo=googlechrome&logoColor=2EA44F&labelColor=C8E6C9)](https://africa.dlnlp.ai/simba/)
-[![SimbaBench](https://img.shields.io/badge/SimbaBench-Benchmark-8A2BE2?style=for-the-badge&logo=googlecharts&logoColor=8A2BE2&labelColor=E1BEE7)](#simbabench)
-[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-FFD21E?style=for-the-badge&logoColor=black&labelColor=FFF9C4)](https://huggingface.co/collections/UBC-NLP/simba-speech-series)
-[![YouTube Video](https://img.shields.io/badge/YouTube-Video-FF0000?style=for-the-badge&logo=youtube&logoColor=FF0000&labelColor=FFCCBC)](#demo)
-
-</div>
-
-## *Bridging the Digital Divide for African AI*
-
-**Voice of a Continent** is a comprehensive open-source ecosystem designed to bring African languages to the forefront of artificial intelligence. By providing a unified suite of benchmarking tools and state-of-the-art models, we ensure that the future of speech technology is inclusive, representative, and accessible to over a billion people.
-
-## Best-in-Class Multilingual Models
-
-Introduced in our EMNLP 2025 paper *[Voice of a Continent](https://aclanthology.org/2025.emnlp-main.559/)*, the **Simba Series** represents the current state-of-the-art for African speech AI.
-
-- **Unified Suite:** Models optimized for African languages.
-- **Superior Accuracy:** Outperforms generic multilingual models by leveraging SimbaBench's high-quality, domain-diverse datasets.
-- **Multitask Capability:** Designed for high performance in ASR (Automatic Speech Recognition) and TTS (Text-to-Speech).
-- **Inclusion-First:** Specifically built to mitigate the "digital divide" by empowering speakers of underrepresented languages.
-
-The **Simba** family consists of state-of-the-art models fine-tuned using SimbaBench. These models achieve superior performance by leveraging dataset quality, domain diversity, and language family relationships.
-
-### 🗣️✍️ Simba-ASR
-> **The New Standard for African Speech-to-Text**
-
-**🎯 Task** `Automatic Speech Recognition` — Powering high-accuracy transcription across the continent.
-
-**🌍 Language Coverage (43 African languages)**
->  **Amharic** (`amh`), **Arabic** (`ara`), **Asante Twi** (`asanti`), **Bambara** (`bam`), **Baoulé** (`bau`), **Bemba** (`bem`), **Ewe** (`ewe`), **Fanti** (`fat`), **Fon** (`fon`), **French** (`fra`), **Ganda** (`lug`), **Hausa** (`hau`), **Igbo** (`ibo`), **Kabiye** (`kab`), **Kinyarwanda** (`kin`), **Kongo** (`kon`), **Lingala** (`lin`), **Luba-Katanga** (`lub`), **Luo** (`luo`), **Malagasy** (`mlg`), **Mossi** (`mos`), **Northern Sotho** (`nso`), **Nyanja** (`nya`), **Oromo** (`orm`), **Portuguese** (`por`), **Shona** (`sna`), **Somali** (`som`), **Southern Sotho** (`sot`), **Swahili** (`swa`), **Swati** (`ssw`), **Tigrinya** (`tir`), **Tsonga** (`tso`), **Tswana** (`tsn`), **Twi** (`twi`), **Umbundu** (`umb`), **Venda** (`ven`), **Wolof** (`wol`), **Xhosa** (`xho`), **Yoruba** (`yor`), **Zulu** (`zul`), **Tamazight** (`tzm`), **Sango** (`sag`), **Dinka** (`din`).
-
-**🏗️ Base Architectures**
-
-  -  **Simba-S** (SeamlessM4T-v2-MT) — *Top Performer*
-  - **Simba-W** (Whisper-v3-large)
-  - **Simba-X** (Wav2Vec2-XLS-R-2b)
-  - **Simba-M** (MMS-1b-all)
-  - **Simba-H** (AfriHuBERT)
-      
-| **ASR Models**   | **Architecture**  | **🤗 Hugging Face Model Card** | **Status** |
-|---------|:------------------:| :------------------:| :------------------:|    
-| 🔥**Simba-S**🔥|    SeamlessM4T-v2  |  🤗 [https://huggingface.co/UBC-NLP/Simba-S](https://huggingface.co/UBC-NLP/Simba-S) | ✅ Released |
-| 🔥**Simba-W**🔥|    Whisper         |  🤗 [https://huggingface.co/UBC-NLP/Simba-W](https://huggingface.co/UBC-NLP/Simba-W) | ✅ Released | 
-| 🔥**Simba-X**🔥|    Wav2Vec2        |  🤗 [https://huggingface.co/UBC-NLP/Simba-X](https://huggingface.co/UBC-NLP/Simba-X) | ✅ Released |   
-| 🔥**Simba-M**🔥|    MMS             |  🤗 [https://huggingface.co/UBC-NLP/Simba-M](https://huggingface.co/UBC-NLP/Simba-M) | ✅ Released |   
-| 🔥**Simba-H**🔥|    HuBERT          |  🤗 [https://huggingface.co/UBC-NLP/Simba-H](https://huggingface.co/UBC-NLP/Simba-H) | ✅ Released |   
-
-* **Simba-S** (based on SeamlessM4T-v2-MT) emerged as the best-performing ASR model overall.
-
-**🧩 Usage Example**
-
-You can easily run inference using the Hugging Face `transformers` library.
-
 ```python
-from transformers import pipeline
-
-# Load Simba-S for ASR
-asr_pipeline = pipeline(
-    "automatic-speech-recognition",
-    model="UBC-NLP/Simba-S" #Simba mdoels `UBC-NLP/Simba-S`, `UBC-NLP/Simba-W`, `UBC-NLP/Simba-X`, `UBC-NLP/Simba-H`, `UBC-NLP/Simba-M`
-)
-
-asr_pipeline.model.load_adapter("multilingual_african")  # Only for  `UBC-NLP/Simba-M`
-
-# Transcribe audio from file
-result = asr_pipeline("https://africa.dlnlp.ai/simba/audio/afr_Lwazi_afr_test_idx3889.wav")
-print(result["text"])
+# Simba-W
+{'text': 'watter veronwaardigingsel daar, in ons binneste gewees het.'}
+```
 
+```python
+# Simba-X
+{'text': 'fator fr on ar taamsodr is'}
+```
 
-# Transcribe audio from audio array
-result = asr_pipeline({
-    "array": audio_array,
-    "sampling_rate": 16_000
-})
-print(result["text"])
+```python
+# Simba-M
+{'text': 'watter veronwaardiging sodaar in ons binniste gewees het'}
+```
 
+```python
+# Simba-H
+{'text': 'watter vironwaardiging so daar in ons binneste geweeshet'}
 ```
+
 Get started with Simba models in minutes using our interactive Colab notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/UBC-NLP/simba/edit/main/simba_models.ipynb)