Instructions to use artificialguybr/whisper-small-pt-cv13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use artificialguybr/whisper-small-pt-cv13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="artificialguybr/whisper-small-pt-cv13")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("artificialguybr/whisper-small-pt-cv13") model = AutoModelForSpeechSeq2Seq.from_pretrained("artificialguybr/whisper-small-pt-cv13", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9522a579cb9efe0cd43a209d9a947732f3422bbb865c7e78dfc536687a355c0
- Size of remote file:
- 1.93 GB
- SHA256:
- 63dc6d732b63d6fc66b41d4f5259bf7779693e80361d8696915b13cdae741c0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.