Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use michaelsh/whisper-tiny-minds-v5-numproc1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelsh/whisper-tiny-minds-v5-numproc1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="michaelsh/whisper-tiny-minds-v5-numproc1")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("michaelsh/whisper-tiny-minds-v5-numproc1") model = AutoModelForSpeechSeq2Seq.from_pretrained("michaelsh/whisper-tiny-minds-v5-numproc1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from michaelsh/whisper-tiny-minds-v5-numproc1: direct link, hf CLI and curl.
- Browser
- Download file 4.16 kB
-
https://huggingface.co/michaelsh/whisper-tiny-minds-v5-numproc1/resolve/main/training_args.bin
- Command line
-
hf download hf://michaelsh/whisper-tiny-minds-v5-numproc1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/michaelsh/whisper-tiny-minds-v5-numproc1/resolve/main/training_args.bin
4.16 kB
- Xet hash:
- b7e3818c03dd3131a18862f477efca693f3b64ce6c2b38bca47970b26cf8aea4
- Size of remote file:
- 4.16 kB
- SHA256:
- e0713d74be313457c7f5a2595dd84522dbd1a4c8a68de91615ae92a63cac634a
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