openai-whisper-cpu

Improving transcription performance of OpenAI Whisper for CPU based deployment (by MiscellaneousStuff)

Openai-whisper-cpu Alternatives

Similar projects and alternatives to openai-whisper-cpu

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better openai-whisper-cpu alternative or higher similarity.

openai-whisper-cpu reviews and mentions

Posts with mentions or reviews of openai-whisper-cpu. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-05-14.
  • How to run Llama 13B with a 6GB graphics card
    12 projects | news.ycombinator.com | 14 May 2023
    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

  • Whispers AI Modular Future
    14 projects | news.ycombinator.com | 20 Feb 2023
    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.
  • [P] Transcribe any podcast episode in just 1 minute with optimized OpenAI/whisper
    4 projects | /r/MachineLearning | 6 Nov 2022
    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
  • [D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
    8 projects | /r/MachineLearning | 28 Oct 2022
    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:
  • [P] OpenAI Whisper - 3x CPU Inference Speedup
    1 project | /r/MachineLearning | 27 Oct 2022
    GitHub
  • A note from our sponsor - InfluxDB
    www.influxdata.com | 23 May 2024
    Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →

Stats

Basic openai-whisper-cpu repo stats
5
221
10.0
over 1 year ago

Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com