Instructions to use MagicLuke/Wav2Vec2-MyST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MagicLuke/Wav2Vec2-MyST with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForPreTraining processor = AutoProcessor.from_pretrained("MagicLuke/Wav2Vec2-MyST") model = AutoModelForPreTraining.from_pretrained("MagicLuke/Wav2Vec2-MyST") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoProcessor, AutoModelForPreTraining
processor = AutoProcessor.from_pretrained("MagicLuke/Wav2Vec2-MyST")
model = AutoModelForPreTraining.from_pretrained("MagicLuke/Wav2Vec2-MyST")Quick Links
Model Description:
This is the wav2vec2-base model being pre-trained on the My Science Tutor (MyST 470h) dataset (from LDC).
The pertaining is done by using fairseq (wav2vec2_base_librispeech config).
The converge checkpoint is converted from PyTorch model to Hugging Face model by using a modified version of convertor script offered by Huggingface
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