Add blog post "Accelerating Whisper on Arm with PyTorch and Hugging Face Transformers" (#1975)

Signed-off-by: Chris Abraham <[email protected]>
-- Squash and Merge by Andrew Bringaze at the request of Bazil Sterling & Jennifer Bly

Author: cjyabraham

Diff for: _posts/2025-04-08-accelerating-whipser-arm-w-transformers.md

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+---
+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/).