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:
- 3eb1f4ba624fd59f5ca233ad46cd68d04d41bf4b623b452d5ec752dc9d45d078
- Size of remote file:
- 417 kB
- SHA256:
- 052a168787a9160b4b2ba54e4995e9600298812c34191ca3f70cea51cd4f5c1e
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