badrex
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w2v-bert-2.0-malagasy-asr

@@ -1,199 +1,114 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+license: cc-by-4.0
+datasets:
+- badrex/malagasy-speech-full
+language:
+- mg
+metrics:
+- wer
+- cer
+base_model:
+- facebook/w2v-bert-2.0
+pipeline_tag: automatic-speech-recognition
 ---
+<div align="center" style="line-height: 1;">
+  <h1>Automatic Speech Recognition for Malagasy</h1>
+  <a href="https://huggingface.co/datasets/badrex/malagasy-speech-full" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-ffc107?color=ffca28&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  <a href="https://huggingface.co/spaces/badrex/Malagasy-ASR" target="_blank" style="margin: 2px;">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Space-ffc107?color=c62828&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  <a href="https://github.com/deepseek-ai/DeepSeek-R1/blob/main/LICENSE" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+### Model Description 🍋‍🟩
+This model is a fine-tuned version of Wav2Vec2-BERT 2.0 for Malagasy automatic speech recognition (ASR). It was trained on 150 hours of transcribed Malagasy speech. The ASR model is robust and the in-domain WER is below 11.7%.
+- **Developed by:** Badr al-Absi
+- **Model type:** Speech Recognition (ASR)
+- **Language:** Malagasy (mg)
+- **License:** CC-BY-4.0
+- **Finetuned from:** facebook/w2v-bert-2.0
+<!-- ### Examples 🚀
+|  | Audio | Human Transcription |  ASR Transcription |
+|----------|--------|----------------|----------------|
+| 1 | <audio controls src="https://huggingface.co/badrex/w2v-bert-2.0-zulu-asr/resolve/main/examples/example_2.wav"></audio> | Yenza isinqumo ngezilimo uzozitshala kumaphi amasimu uphinde idwebe imephu njengereferensi yakho. |  yenza isinqumo ngezilimo ozozitshala kumaphi amasimu uphinde igwebe imephu njengereference yakho |
+| 2 | <audio controls src="https://huggingface.co/badrex/w2v-bert-2.0-zulu-asr/resolve/main/examples/example_3.wav"></audio> | Emdlalweni wokugcina ngokumelene IFrance, wayengumuntu ongasetshenziswanga esikhundleni njengoba i-Argentina inqobe ngo-4-2 nge-penalty ukuze ithole isiqu sayo sesithathu seNdebe Yomhlaba. | emdlalweni wokugqina ngokumelene i-france wayengumuntu ongasetshenziswanga esikhundleni njengoba i-argentina incobe ngo-4-2 ngephelnathi ukuze ithole isiqu sayo sesithathu sendebe yomhlaba |
+| 3 | <audio controls src="https://huggingface.co/badrex/w2v-bert-2.0-zulu-asr/resolve/main/examples/example_1.wav"></audio> | Amadolobhana angaphandle angaphezu kwamamitha ambalwa, Reneging cishe 140m, amamitha angu-459.3, ngaphezu kogu lolwandle. Le ndawo iningi emahlathini ama-dune asogwini, ikakhulukazi eceleni kwe-zindunduma zasogwini nasedolobheni lase-Meerensee. | amadolobhana angaphandle angaphezu kwamamitha ambalwa reneging cishe 140m amamitha angu 4593 ngaphezu kogulolwandle le ndawo iningi emahlabathini amedum esogwini ikakhulukazi eceleni kwezindunduma zasogwini nasedolobheni lasemerins |
+ -->
 ### Direct Use
+The model can be used directly for automatic speech recognition of a Malagasy audio:
+```python
+from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC
+import torch
+import torchaudio
+# load model and processor
+processor = Wav2Vec2BertProcessor.from_pretrained("badrex/w2v-bert-2.0-malagasy-asr")
+model = Wav2Vec2BertForCTC.from_pretrained("badrex/w2v-bert-2.0-malagasy-asr")
+# load audio
+audio_input, sample_rate = torchaudio.load("path/to/audio.wav")
+# preprocess
+inputs = processor(audio_input.squeeze(), sampling_rate=sample_rate, return_tensors="pt")
+# inference
+with torch.no_grad():
+    logits = model(**inputs).logits
+# decode
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = processor.batch_decode(predicted_ids)[0]
+print(transcription)
+```
+### Downstream Use
+This model can be used as a foundation for:
+- building voice assistants for Malagasy speakers
+- transcription services for Malagasy content
+- accessibility tools for Malagasy-speaking communities
+- research in low-resource speech recognition
+### Model Architecture
+- **Base model:** Wav2Vec2-BERT 2.0
+- **Architecture:** transformer-based with convolutional feature extractor
+- **Parameters:** ~600M (inherited from base model)
+- **Objective:** connectionist temporal classification (CTC)
+### Funding
+The development of this model was supported by [CLEAR Global](https://clearglobal.org/) and [Gates Foundation](https://www.gatesfoundation.org/).
+### Citation
+```bibtex
+@misc{w2v_bert_malagasy_asr,
+  author = {Badr M. Abdullah},
+  title = {Adapting Wav2Vec2-BERT 2.0 for Malagasy ASR},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/badrex/w2v-bert-2.0-malagasy-asr}
+}
+```
+### Model Card Contact
+For questions or issues, please contact via the Hugging Face model repository in the community discussion section.