Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use model-man/whisper-tiny-dv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use model-man/whisper-tiny-dv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="model-man/whisper-tiny-dv")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("model-man/whisper-tiny-dv") model = AutoModelForSpeechSeq2Seq.from_pretrained("model-man/whisper-tiny-dv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95bfd83612cc6184a24e2b57827c50263d7d21000f1cf26f57e7e31f8d37520e
- Size of remote file:
- 151 MB
- SHA256:
- 5b07f20a7497a1190227fde4c4843e29f910801a8fab0831ab1b3a624c5e8c59
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.