Automatic Speech Recognition
Transformers
Safetensors
English
whisper
FP8
vllm
audio
compressed-tensors
Instructions to use RedHatAI/whisper-large-v3-turbo-FP8-dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/whisper-large-v3-turbo-FP8-dynamic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="RedHatAI/whisper-large-v3-turbo-FP8-dynamic")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("RedHatAI/whisper-large-v3-turbo-FP8-dynamic") model = AutoModelForSpeechSeq2Seq.from_pretrained("RedHatAI/whisper-large-v3-turbo-FP8-dynamic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 015c798476c5ae52554d204b12220f3ab7bf84a1bd707aed1eb0ba07cbc18bed
- Size of remote file:
- 951 MB
- SHA256:
- 4d086cad48e1ddb9c22d38edb088f9c9860505056bbb55b5dd08360c41a2194a
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