Automatic Speech Recognition
Transformers
Safetensors
Malagasy
wav2vec2-bert
speech
malagasy
madagascar
w2v-bert-2.0
ctc
low-resource
african-languages
Eval Results (legacy)
Instructions to use Flo976/w2v-bert-malagasy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Flo976/w2v-bert-malagasy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Flo976/w2v-bert-malagasy")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Flo976/w2v-bert-malagasy") model = AutoModelForCTC.from_pretrained("Flo976/w2v-bert-malagasy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Yesterday
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Note sur la vitesse de la version int8 selon le processeur
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Banniere : 12,7 % avec le modele de langue v2
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Decodage sur log-probabilites, LM v2, ponctuation, version int8 : chiffres et code mis a jour
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2 days ago
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Resume en francais en paragraphe
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Fiche illustree : banniere, badges et graphique par duree
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Publication initiale
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initial commit
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