Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
English
speech_to_text
speech
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use Classroom-workshop/assignment1-jack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Classroom-workshop/assignment1-jack with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Classroom-workshop/assignment1-jack")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Classroom-workshop/assignment1-jack") model = AutoModelForSpeechSeq2Seq.from_pretrained("Classroom-workshop/assignment1-jack") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 15a1f5cdc4c798dbab16872295215025a409896056da058dfe31842f880ba85c
- Size of remote file:
- 129 MB
- SHA256:
- 2faac8e98aaa3808196dab18955801120c7aab1c6d4d17ea788fefd1cd37aaa8
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