Instructions to use mlengineer-ai/whisper-small-fa-specaug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlengineer-ai/whisper-small-fa-specaug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mlengineer-ai/whisper-small-fa-specaug")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mlengineer-ai/whisper-small-fa-specaug") model = AutoModelForSpeechSeq2Seq.from_pretrained("mlengineer-ai/whisper-small-fa-specaug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-20000/pytorch_model.bin from mlengineer-ai/whisper-small-fa-specaug: direct link, hf CLI and curl.
- Browser
- Download file 967 MB
-
https://huggingface.co/mlengineer-ai/whisper-small-fa-specaug/resolve/main/checkpoint-20000/pytorch_model.bin
- Command line
-
hf download hf://mlengineer-ai/whisper-small-fa-specaug/checkpoint-20000/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mlengineer-ai/whisper-small-fa-specaug/resolve/main/checkpoint-20000/pytorch_model.bin
967 MB
- Xet hash:
- bfd478f1c5f572902b3bd0577ec868db9589078859dda1b498488dbb37d05608
- Size of remote file:
- 967 MB
- SHA256:
- b2ab1a2a68392bffcc5d2d3116a000063762f6be218ff4fe70522cc1b014e723
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