Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
Eval Results (legacy)
Instructions to use Scrya/whisper-medium-ms-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scrya/whisper-medium-ms-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Scrya/whisper-medium-ms-augmented")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Scrya/whisper-medium-ms-augmented") model = AutoModelForSpeechSeq2Seq.from_pretrained("Scrya/whisper-medium-ms-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Scrya/whisper-medium-ms-augmented: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Scrya/whisper-medium-ms-augmented/resolve/main/training_args.bin
- Command line
-
hf download hf://Scrya/whisper-medium-ms-augmented/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Scrya/whisper-medium-ms-augmented/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 958d9a669b5eb932478136d08dacace606759bb878f5146b5df720e3ed079f35
- Size of remote file:
- 3.58 kB
- SHA256:
- 9d9743840194dceeb7ba4fb40ee973faf8fea19b3a9d4eb54f604bce1515f6cc
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