Whisper Large-v3 INT8

Mobile CPUvivo X300SME2

INT8-quantized automatic speech recognition model for English speech-to-text, based on an encoder-decoder Transformer architecture with autoregressive decoding and KV caching. It uses 128-bin log-mel audio features and produces text transcriptions via greedy decoding, with weight-only quantization designed to reduce model size while preserving transcription quality.View on Hugging Face

Accuracyvs base model

WER1.97%↓0.06 pp

CER0.81%↓0 pp

RTFx

0.88

Latency

14673.4ms

Peak Memory

5605.54MB

Get Started with this Model

Learning pathRecommended

Run vLLM inference with quantized models and benchmark on Arm servers

Learn how to run Llama 3.1 8B and Whisper inference with BF16 and W8A8 quantized models on Arm servers, then compare throughput and accuracy with vLLM benchmark tools.

Learning pathRecommended

Transcribe speech with models from the Arm AI Portal on Android

Download and run Arm-optimized Whisper speech-to-text models from the Arm AI Portal on an Arm-powered Android device.

Learning path

Build a Sentiment-Aware Voice Assistant with On-Device LLMs

Build an end-to-end, on-device voice assistant that understands both speech and emotion using Whisper, HuBERT, ONNX Runtime, and a local LLM with llama.cpp on Arm.

Learning path

Accelerate multimodal Voice Assistant performance with KleidiAI and SME2

Build and benchmark a multimodal Android voice-assistant pipeline, then use KleidiAI and SME2 to accelerate its speech recognition and language-model components.

Model Evaluation

Base model: whisper-large-v3Tested on: vivo X300 (arm64, 8-core)

Evaluation Conditions

Automatic Speech Recognition · vivo X300 · INT8 · LibriSpeech (test-clean)

Metric	Resultsvs baseline	Change
End to end latency p50	14673.40ms342812.52ms	−328139.12 ms
End to end latency p90

About this version

Original Model:: whisper-large-v3 by OpenAI — Repository
Optimization/conversion:: Arm-Optimized version for execution on Arm-based platforms.
Converted/optimized by:: Arm
License:: The Original Model and the Optimized Model are subject to Apache-2.0.

This repository contains a converted or optimized version of the Original Model (the “Optimized Model”). The Original Model has been converted or optimized as described above for execution on Arm-based platforms.

No retraining or fine-tuning of the Original Model was performed as part of the conversion or optimization. The conversion or optimization was not intended to change the Original Model’s behavior or intended use.

Original Model and Documentation

For information about the Original Model, including its development, training data, intended uses, limitations and other relevant information, please refer to the Original Model repository. Information in that repository was provided by the original developer or other third parties and, unless expressly stated otherwise, has not been independently verified by Arm.

Licenses and Third-Party Terms

Use of the Original Model and the Optimized Model is subject to the applicable licenses, usage restrictions and other terms identified above and in the relevant repositories. Publication of the Optimized Model does not grant any rights beyond those provided under the applicable license terms.

You are responsible for reviewing those terms and ensuring that your use of the Original Model and the Optimized Model is permitted.

Configuration	Baseline	Arm Optimized
Dataset	LibriSpeech (test-clean)	LibriSpeech (test-clean)
Sample count	2,620	2,620
Batch size	1	1

Whisper Large-v3 INT8

Get Started with this Model

Run vLLM inference with quantized models and benchmark on Arm servers

Transcribe speech with models from the Arm AI Portal on Android

Build a Sentiment-Aware Voice Assistant with On-Device LLMs

Accelerate multimodal Voice Assistant performance with KleidiAI and SME2

Model Evaluation

Whisper Large-v3 INT8

Get Started with this Model

Run vLLM inference with quantized models and benchmark on Arm servers

Transcribe speech with models from the Arm AI Portal on Android

Build a Sentiment-Aware Voice Assistant with On-Device LLMs

Accelerate multimodal Voice Assistant performance with KleidiAI and SME2

Model Evaluation

Configuration

Comparability

Purpose of this Release