Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Swedish
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use SamuelHarner/whisper_augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SamuelHarner/whisper_augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="SamuelHarner/whisper_augmented")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("SamuelHarner/whisper_augmented") model = AutoModelForSpeechSeq2Seq.from_pretrained("SamuelHarner/whisper_augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SamuelHarner/whisper_augmented: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/SamuelHarner/whisper_augmented/resolve/main/training_args.bin
- Command line
-
hf download hf://SamuelHarner/whisper_augmented/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SamuelHarner/whisper_augmented/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- f2dac0ba0f9275d414a92e7f9296cbf7d3f79740813bd5b716d2f2c7c72eaf87
- Size of remote file:
- 4.86 kB
- SHA256:
- 842d72fb6b802dd5256f42eccce75f5205c91e08de1f64a6a33aa1cc3dd222e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.