Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
English
wav2vec2
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Classroom-workshop/assignment1-omar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Classroom-workshop/assignment1-omar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Classroom-workshop/assignment1-omar")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Classroom-workshop/assignment1-omar") model = AutoModelForCTC.from_pretrained("Classroom-workshop/assignment1-omar") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f96b8d3a9a16cb730bc2a797e5d595c4c92b6d1438d67b50fc598027d79e1eed
- Size of remote file:
- 378 MB
- SHA256:
- c34f9827b034a1b9141dbf6f652f8a60eda61cdf5771c9e05bfa99033c92cd96
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