Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Tatar
whisper
whisper-event
Eval Results (legacy)
Instructions to use 501Good/whisper-small-tt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 501Good/whisper-small-tt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="501Good/whisper-small-tt")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("501Good/whisper-small-tt") model = AutoModelForSpeechSeq2Seq.from_pretrained("501Good/whisper-small-tt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fbf052548070f386700278353b82ac96244b787386f3eea93be449800874582
- Size of remote file:
- 3.64 kB
- SHA256:
- 633b70b2a16ea50f03f5181dc218eb614b3c7cd63bfd7213910e78e13489e2e4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.