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openai-whisper-cpu reviews and mentions
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How to run Llama 13B with a 6GB graphics card
I feel the same.
For example some stats from Whisper [0] (audio transcoding) show the following for the medium model (see other models in the link):
---
GPU medium fp32 Linear 1.7s
CPU medium fp32 nn.Linear 60.7
CPU medium qint8 (quant) nn.Linear 23.1
---
So the same model runs 35.7 times faster on GPU, and compared to an CPU-optimized model still 13.6.
I was expecting around an order or magnitude of improvement. Then again, I do not know if in the case of this article the entire model was in the GPU, or just a fraction of it (22 layers), which might explain the result.
[0] https://github.com/MiscellaneousStuff/openai-whisper-cpu
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Whispers AI Modular Future
According to https://github.com/MiscellaneousStuff/openai-whisper-cpu the medium model needs 1.7 seconds to transcribe 30 seconds of audio when run on a GPU.
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[P] Transcribe any podcast episode in just 1 minute with optimized OpenAI/whisper
There is a very simple method built-in to PyTorch which can give you over 3x speed improvement for the large model, which you could also combine with the method proposed in this post. https://github.com/MiscellaneousStuff/openai-whisper-cpu
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[D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
For CPU inference, model quantization is a very easy to apply method with great average speedups which is already built-in to PyTorch. For example, I applied dynamic quantization to the OpenAI Whisper model (speech recognition) across a range of model sizes (ranging from tiny which had 39M params to large which had 1.5B params). Refer to the below table for performance increases:
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www.saashub.com | 17 Apr 2024
Stats
MiscellaneousStuff/openai-whisper-cpu is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of openai-whisper-cpu is Jupyter Notebook.
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