wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
Paper • 2006.11477 • Published • 10
# Load model directly
from transformers import AutoProcessor, AutoModelForPreTraining
processor = AutoProcessor.from_pretrained("flax-community/wav2vec2-spanish")
model = AutoModelForPreTraining.from_pretrained("flax-community/wav2vec2-spanish", device_map="auto")Quick Links
Wav2Vec2 Spanish
Wav2Vec2 model pre-trained using the Spanish portion of the Common Voice dataset. The model is trained with Flax and using TPUs sponsored by Google since this is part of the Flax/Jax Community Week organised by HuggingFace.
Model description
The model used for training is Wav2Vec2 by FacebookAI. It was introduced in the paper "wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations" by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli (https://arxiv.org/abs/2006.11477).
This model is available in the 🤗 Model Hub.
Training data
Spanish portion of Common Voice. Common Voice is an open source, multi-language dataset of voices part of Mozilla's initiative to help teach machines how real people speak.
The dataset is also available in the 🤗 Datasets library.
Team members
- María Grandury (@mariagrandury)
- Manuel Romero (@mrm8488)
- Eduardo González Ponferrada (@edugp)
- pcuenq (@pcuenq)
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="flax-community/wav2vec2-spanish")