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:
- 4a697920226da1f5247cb946a5bef500595190afd49bd299bd683eb7085e8bfc
- Size of remote file:
- 151 MB
- SHA256:
- 8cc56c2923427cc0ed3f79a48ee52a6f890cf8e458723046c227d5dfb84b0015
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.