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-15000/training_args.bin from mlengineer-ai/whisper-small-fa-specaug: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/mlengineer-ai/whisper-small-fa-specaug/resolve/main/checkpoint-15000/training_args.bin
- Command line
-
hf download hf://mlengineer-ai/whisper-small-fa-specaug/checkpoint-15000/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/mlengineer-ai/whisper-small-fa-specaug/resolve/main/checkpoint-15000/training_args.bin
4.09 kB
- Xet hash:
- 26ff1bf43349078eaef626a665d288f5f8b16419f1d278ae18ded4ea660592d5
- Size of remote file:
- 4.09 kB
- SHA256:
- 0289e1bf8fd1a33f7c3c9d3e15cc9bdd13d10e9774e0ada934c0b9d71501a993
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