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-1500 /training_args.bin
Download mms-1b-all-chichewa-1h/checkpoint-1500/training_args.bin from dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript/resolve/main/mms-1b-all-chichewa-1h/checkpoint-1500/training_args.bin
- Command line
-
hf download hf://dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript/mms-1b-all-chichewa-1h/checkpoint-1500/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dmatekenya/whisper-large-v3-chichewa-variant-b-normalized-transcript/resolve/main/mms-1b-all-chichewa-1h/checkpoint-1500/training_args.bin
4.98 kB
- Xet hash:
- 8a3c9691c229343c0c92349976a903256e794c470e205a97898d7c2486a59def
- Size of remote file:
- 4.98 kB
- SHA256:
- dd5850f6a99a2c974ed9fab7ef4100f89baaa8f9f5ace404ec183064b6c92424
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