Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Polish
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use M2LabOrg/whisper-small-pl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use M2LabOrg/whisper-small-pl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="M2LabOrg/whisper-small-pl")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("M2LabOrg/whisper-small-pl") model = AutoModelForSpeechSeq2Seq.from_pretrained("M2LabOrg/whisper-small-pl") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee0b931ae8ef27343e032ee239d4ff09f79593554c1ddafc4a0f04e9176f66a1
- Size of remote file:
- 967 MB
- SHA256:
- 5c598fdf05fa195f30e0c4350fe3e2d846abbd03257be6a0a25bac56f0e96b46
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.