Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Swahili
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use mn720/inctraining5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mn720/inctraining5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mn720/inctraining5")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mn720/inctraining5") model = AutoModelForSpeechSeq2Seq.from_pretrained("mn720/inctraining5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mn720/inctraining5: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/mn720/inctraining5/resolve/main/training_args.bin
- Command line
-
hf download hf://mn720/inctraining5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mn720/inctraining5/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 916362d6650e5f9e22962b165ddb5786000a232efbb5e6d9b8aff4921b71f15d
- Size of remote file:
- 5.11 kB
- SHA256:
- 477c017fd6508e1e2c40d09e84ba33315005389e6117d4ce3a1fd5d01f61d40b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.