Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
whisper
whisper-event
Generated from Trainer
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use MMars/whisper-small-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MMars/whisper-small-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MMars/whisper-small-ar")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("MMars/whisper-small-ar") model = AutoModelForSpeechSeq2Seq.from_pretrained("MMars/whisper-small-ar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from MMars/whisper-small-ar: direct link, hf CLI and curl.
- Browser
- Download file 1.93 GB
-
https://huggingface.co/MMars/whisper-small-ar/resolve/main/optimizer.pt
- Command line
-
hf download hf://MMars/whisper-small-ar/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/MMars/whisper-small-ar/resolve/main/optimizer.pt
1.93 GB
- Xet hash:
- 5841c86f065d19f25caa6fee10c72f34b66f8def0dfc7971977de4965de08029
- Size of remote file:
- 1.93 GB
- SHA256:
- 683d1e94d46b9bffdbd9a93f5dbf0ea4e9976c795713573b6b190587820c8f5d
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