Arm/whisper-small-int8-xnnpack-executorch

Whisper Small optimized for Arm-based mobile CPUs with SME2

Whisper Small, an encoder-decoder Transformer for automatic speech recognition, quantized to INT8 and exported to ExecuTorch for inference on Arm-based mobile CPUs with SME2.

Summary

This repository contains an Arm-optimized version of openai/whisper-small for automatic speech recognition. The model is provided in ExecuTorch (.pte) format, targeting Mobile CPU systems.

This version is intended to demonstrate efficient inference on Arm-based platforms while preserving the original model's intended behavior. Arm has evaluated this model on LibriSpeech ASR and measured performance on a representative evaluation target.

Key results

Area	Result
Model format	ExecuTorch (.pte)
Target device class	Mobile CPU
Reference device	vivo X300 (C1-Ultra, C1-Premium, C1-Pro; Android 16 / OriginOS 6)
Primary performance result	7802.5 ms p50 latency, RTFx 0.90
Accuracy result	Normalised WER 3.41%, CER 1.29%
Size / memory result	395.05 MB, 2.72 x smaller than the baseline (1074.76 MB)

Original model

Field	Value
Original model	openai/whisper-small
Original source	Hugging Face
Original developer	OpenAI
Original model card	openai/whisper-small
Original license	Apache 2.0

Model files

File	Description
whisper_small_vivo_executorch_optimized.pte	Arm-optimized model for deployment
whisper_preprocessor.pte	ExecuTorch module that computes the log-mel spectrogram from raw audio; loaded by example.py when present, with a Python-side fallback otherwise
tokenizer.json	Fast Whisper tokenizer vocabulary and tokenisation rules
tokenizer_config.json	Whisper tokenizer configuration
special_tokens_map.json	Whisper special-token definitions
pte_original/	Original baseline model and tokenizer artifacts; not used by the optimized example
example.py	Minimal inference example
pyproject.toml	Pinned runtime dependencies for example.py, resolved with uv
uv.lock	Locked dependency resolution for pyproject.toml
config.yaml	Model I/O contract used by the example
benchmarks/	FP32 baseline and Arm-optimized benchmark records

Performance

Performance was measured on the reference configuration below. Results are intended to make the optimization reproducible but do not guarantee identical performance on every Arm-based system.

Reference configuration

Field	Value
Device / platform	vivo X300
CPU / accelerator	C1-Ultra, C1-Premium, C1-Pro (aarch64, 8 cores), CPU execution backend
OS	Android 16 / OriginOS 6
Runtime	ExecuTorch 1.1.0
Backend / delegate	XNNPACK, KleidiAI
Batch size	1
Precision	INT8 weights (per-channel symmetric) and INT8 dynamic activations (PTQ-dynamic, no calibration required)
Runs	10 warmup, 50 measured

Performance results

Metric	Original / baseline	Arm-optimized	Improvement
p50 latency	10927.0 ms	7802.5 ms	1.40 x faster
p90 latency	11742.0 ms	8027.0 ms	1.46 x faster
p99 latency	11742.0 ms	8027.0 ms	1.46 x faster
Model size	1074.76 MB	395.05 MB	2.72 x smaller
Peak memory	5226.97 MB	4662.29 MB	1.12 x less
RTFx	0.64	0.90	1.40 x higher
Model load time	1327.0 ms	1015.0 ms	1.31 x faster

Accuracy

Accuracy was evaluated using the same preprocessing, input resolution, and evaluation protocol described below. Where possible, the optimized model is compared against the original model under the same evaluation conditions.

Evaluation setup

Field	Value
Dataset	LibriSpeech ASR (librispeech_asr)
Split	test-clean
Number of samples	2620
Metric(s)	Normalised WER (Whisper-style), CER
Evaluation runtime	ExecuTorch

Accuracy results

Metric	Original / baseline	Arm-optimized	Change
Normalised WER	3.45%	3.41%	-0.04 pp
CER	1.33%	1.29%	-0.04 pp

Accuracy was measured using the evaluation setup described above. Users should re-evaluate the model on their own data before production use.

Arm optimization approach

Arm optimized this model for efficient inference on Arm-based platforms using a hardware-aware conversion and validation flow.

For this release, Arm used:

Optimization area	Applied?	Notes
Model conversion	Yes	Converted to ExecuTorch .pte via an Optimum ExecuTorch seq2seq export
Quantization	Yes	PTQ-dynamic INT8: 8-bit weights (per-channel symmetric), 8-bit dynamic activations; no calibration required
Runtime/backend selection	Yes	XNNPACK and KleidiAI optimisations
Graph/runtime compatibility updates	Yes	Performed as part of the ExecuTorch export pipeline
Accuracy validation	Yes	Compared against the original model or published baseline
Performance validation	Yes	Measured on the reference Arm platform

The goal of this process is to improve deployment characteristics such as latency, memory use, model size, and runtime compatibility while preserving the model's intended behavior. Detailed conversion scripts, calibration configuration, or backend-specific implementation details may be provided separately where appropriate.

Using this model

Install dependencies

Dependencies are declared in pyproject.toml, which ships with this repository. Resolve and install them into a local virtual environment with uv:

uv python install
uv sync --frozen

Run the example

uv run example.py

Note: The Python/uv example runs on AWS Graviton (Ubuntu arm64) to confirm runtime compatibility only, and is intended as a guideline for building an equivalent run script on Mobile CPU systems.

Expected input

Property	Value
Input shape	[1, 80, 3000]
Input type	float32
Input range	N/A (log-mel spectrogram magnitude, not a fixed bounded range)
Preprocessing	Load audio as a 16 kHz mono waveform; compute a log-mel spectrogram (mel bins 80, hop length 160, n_fft 400, sample rate 16000, duration 30 seconds); pad or trim to 3000 time frames

Expected output

Property	Value
Output shape	N/A (variable-length token ID sequence, autoregressive generation)
Output type	Token ID sequence
Postprocessing	Greedy decoding with a fixed token-suppression list and a repetition-guard heuristic that trims repeated trailing token patterns; decode with the Whisper tokenizer, skipping special tokens

Intended use

This model is intended for developers evaluating automatic speech recognition workloads on Arm-based platforms. It is suitable as a reference implementation for benchmarking, prototyping, and integration exploration.

Limitations

• Performance depends on the target device, runtime version, backend/delegate support, memory configuration, and system load.
• Accuracy was evaluated on LibriSpeech ASR and may not generalize to all domains.
• This release preserves the original model's intended task and behavior, but users should validate it for their own application, data, and deployment environment.
• This repository is not a replacement for the original model documentation.

Additional notes

Quantization keeps a small set of layers in FP32 to preserve accuracy: proj_out/lm_head, the encoder and decoder positional embeddings, and encoder.conv1/encoder.conv2. Audio inputs are limited to 30 seconds (3000 mel time frames) per chunk; longer audio must be chunked externally before inference.

example.py forces English transcription by hardcoding the decoder prefix to <|en|>, <|transcribe|>, <|notimestamps|>. This is an example-level default, not a model restriction: the bundled tokenizer and decoder support the full multilingual Whisper vocabulary (98 language tokens) and the <|translate|> task, so other languages or the translate task can be enabled by changing the forced-prefix token IDs in example.py, with no re-export required.

• Sample input: sample_input.flac is utterance 5338-24615-0014 of the LibriSpeech ASR corpus (dev-clean split) by Panayotov et al., via OpenSLR (CC BY 4.0).

About this version

Original Model: openai/whisper-small 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.

Purpose of this Release

The Optimized Model is provided as a reference implementation to demonstrate and evaluate execution and performance on Arm-based systems. It is not a production-ready or supported solution.

Arm’s publication of the Optimized Model does not constitute an endorsement or certification of the Original Model or a representation that the Optimized Model is suitable for production use or any particular purpose.

To the fullest extent permitted by applicable law (i) the Optimized Model is provided “as is.” Arm makes no representations or warranties that the Original Model, the Optimized Model or their outputs are accurate, safe, secure, non-infringing, legally compliant, suitable for production use or fit for any particular purpose; and (ii) Arm will not be liable for any loss or damage arising from or in connection with the Optimized Model, its use or its outputs.

You are responsible for independently evaluating the Optimized Model, its outputs and its suitability for your intended use, including compliance with applicable legal, regulatory, safety and security requirements.

Arm does not commit to provide ongoing support, maintenance or updates for the Optimized Model. Any use of or reliance on the Optimized Model or its outputs is at your own risk.