Instructions to use FastFlowLM/Whisper-V3-Turbo-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FastFlowLM/Whisper-V3-Turbo-NPU2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="FastFlowLM/Whisper-V3-Turbo-NPU2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("FastFlowLM/Whisper-V3-Turbo-NPU2") model = AutoModelForSpeechSeq2Seq.from_pretrained("FastFlowLM/Whisper-V3-Turbo-NPU2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download decoder_mv.xclbin from FastFlowLM/Whisper-V3-Turbo-NPU2: direct link, hf CLI and curl.
- Browser
- Download file 137 kB
-
https://huggingface.co/FastFlowLM/Whisper-V3-Turbo-NPU2/resolve/main/decoder_mv.xclbin
- Command line
-
hf download hf://FastFlowLM/Whisper-V3-Turbo-NPU2/decoder_mv.xclbin
-
curl -L -o decoder_mv.xclbin https://huggingface.co/FastFlowLM/Whisper-V3-Turbo-NPU2/resolve/main/decoder_mv.xclbin
137 kB
- Xet hash:
- 7b6dbc9b7981f0a7f7513d0d67cdf0f5036a5f65f1a2f07dae94c7e05f010f89
- Size of remote file:
- 137 kB
- SHA256:
- 5f711363051bcd6a688031ce917a53b8f4e088b1f4eda53bf352e9581443d749
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