w2v-bert-2.0-malagasy-asr

<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://creativecommons.org/licenses/by/4.0/deed.en" 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:

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 and Gates Foundation.

Citation

@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.