Instructions to use dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript") model = AutoModelForSpeechSeq2Seq.from_pretrained("dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript", device_map="auto") - Notebooks
- Google Colab
- Kaggle
whisper-large-v3-chichewa-variant-b-normalized-transcript / mms-1b-all-chichewa-1h /checkpoint-2000 /model.safetensors
Download mms-1b-all-chichewa-1h/checkpoint-2000/model.safetensors from dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript: direct link, hf CLI and curl.
- Browser
- Download file 3.86 GB
-
https://huggingface.co/dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript/resolve/main/mms-1b-all-chichewa-1h/checkpoint-2000/model.safetensors
- Command line
-
hf download hf://dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript/mms-1b-all-chichewa-1h/checkpoint-2000/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript/resolve/main/mms-1b-all-chichewa-1h/checkpoint-2000/model.safetensors
3.86 GB
- Xet hash:
- f6a3ebca8872dab950b958f82df4e981839a22430f352f14b6687b6f3d079380
- Size of remote file:
- 3.86 GB
- SHA256:
- f5d298ff4dbba33ed4b5eb29323e4deab7f32d6ffba60b71eda2daf7c556a61d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.