Automatic Speech Recognition
Transformers
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use VoicesColeby/whisper-tiny-minds14-en-us with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VoicesColeby/whisper-tiny-minds14-en-us with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="VoicesColeby/whisper-tiny-minds14-en-us")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("VoicesColeby/whisper-tiny-minds14-en-us") model = AutoModelForSpeechSeq2Seq.from_pretrained("VoicesColeby/whisper-tiny-minds14-en-us") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 87753986c9fd76e2073ca4a848ab4baab532f9183de30155383ae2bb9ba5cf3b
- Size of remote file:
- 5.97 kB
- SHA256:
- 18582eedb2722d4ffa5360a6bb6ba33e75595fc83c5fa0ecd0a09d49f01019f9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.