From a81f1c55eb491e330d2391c3298d5481405fa12c Mon Sep 17 00:00:00 2001 From: Chris Abraham Date: Tue, 8 Apr 2025 15:45:42 +0700 Subject: [PATCH] Add blog post "Accelerating Whisper on Arm with PyTorch and Hugging Face Transformers" Signed-off-by: Chris Abraham --- ...accelerating-whipser-arm-w-transformers.md | 39 +++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 _posts/2025-04-08-accelerating-whipser-arm-w-transformers.md diff --git a/_posts/2025-04-08-accelerating-whipser-arm-w-transformers.md b/_posts/2025-04-08-accelerating-whipser-arm-w-transformers.md new file mode 100644 index 000000000000..10db0cabc270 --- /dev/null +++ b/_posts/2025-04-08-accelerating-whipser-arm-w-transformers.md @@ -0,0 +1,39 @@ +--- +layout: blog_detail +title: "Accelerating Whisper on Arm with PyTorch and Hugging Face Transformers" +author: Pareena Verma, Arm +--- + +Automatic speech recognition (ASR) has revolutionized how we interact with technology, clearing the way for applications like real-time audio transcription, voice assistants, and accessibility tools. OpenAI Whisper is a powerful model for ASR, capable of multilingual speech recognition and translation. + +A new Arm Learning Path is now available that explains how to accelerate Whisper on Arm-based cloud instances using PyTorch and Hugging Face transformers. + +**Why Run Whisper on Arm?** + +Arm processors are popular in cloud infrastructure for their efficiency, performance, and cost-effectiveness. With major cloud providers such as AWS, Azure, and Google Cloud offering Arm-based instances, running machine learning workloads on this architecture is becoming increasingly attractive. + +**What You’ll Learn** + +The [Arm Learning Path](https://learn.arm.com/learning-paths/servers-and-cloud-computing/whisper/) provides a structured approach to setting up and accelerating Whisper on Arm-based cloud instances. Here’s what you cover: + +**1. Set Up Your Environment** + +Before running Whisper, you must set up your development environment. The learning path walks you through setting up an Arm-based cloud instance and installing all dependencies, such as PyTorch, Transformers, and ffmpeg. + +**2. Run Whisper with PyTorch and Hugging Face Transformers** + +Once the environment is ready, you will use the Hugging Face transformer library with PyTorch to load and execute Whisper for speech-to-text conversion. The tutorial provides a step-by-step approach for processing audio files and generating audio transcripts. + +**3. Measure and Evaluate Performance** + +To ensure efficient execution, you learn how to measure transcription speeds and compare different optimization techniques. The guide provides insights into interpreting performance metrics and making informed decisions on your deployment. + +**Try it Yourself** + +Upon completion of this tutorial, you know how to: + +* Deploy Whisper on an Arm-based cloud instance. +* Implement performance optimizations for efficient execution. +* Evaluate transcription speeds and optimize further based on results. + +**Try the live demo today** and see audio transcription in action on Arm: [Whisper on Arm Demo](https://learn.arm.com/learning-paths/servers-and-cloud-computing/whisper/_demo/). \ No newline at end of file