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Audio8-ASR-0.1B
Audio8-ASR-0.1B is a compact speech-recognition model for English, Chinese, Cantonese, French, German, Japanese, and Korean, with separate packages for Transformers, CPU ONNX, and local iPhone transcription.
Its name refers to the roughly 0.1B-parameter language-model component. The full model has about 0.324B unique parameters and uses a Qwen3-ASR audio encoder, an adapter, and a small causal decoder. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A small multilingual ASR model
The base Hugging Face repository provides a BF16 Transformers checkpoint and custom model code for turning short audio clips into text across seven named languages.
Why it stands out
Three practical local paths
Alongside the checkpoint, the publisher provides a self-contained ONNX Runtime package with a browser UI and local API, plus an iOS package with a Swift SDK, demo app, and prebuilt model assets.
Availability
Downloadable from Hugging Face
The model and deployment packages are available on Hugging Face. Each repository states CC BY-NC 4.0; review those terms for your intended use. The base model is not currently served by a Hugging Face Inference Provider.
Why it matters
What makes it useful
Audio8 makes a small speech model usable in more than one setting. Builders can start with the Transformers checkpoint, move to a CPU-friendly ONNX package, or test a prebuilt iPhone path without treating a large GPU server as the only option.
What to know
Where it fits
Use it as an early local transcription building block rather than a finished meeting assistant or streaming speech service. It is most relevant when model size, device memory, language coverage, and repository-stated license terms matter as much as raw recognition scores.
Notable points
What stands out
Audio8 reports a 7.03 average word error rate across seven English Open ASR Leaderboard splits under its stated H200 evaluation setup, plus separate Chinese character error rates. Those project-reported results do not establish the same accuracy on every language, accent, microphone, noise level, or device.
Before using
What to review
The default Transformers and ONNX examples are built for short-form ASR and truncate audio after 30 seconds. Longer recordings need a separate chunking workflow.
The Transformers path loads custom repository code with trust_remote_code=True. Review that code and pin the model revision before using it in an important workflow.
The ONNX package documents roughly 1.1 GB peak memory depending on the device and configuration. The iPhone package is designed for roughly 200 MB runtime memory, but the actual footprint varies by device, iOS version, and workload.
The iOS package requires Apple development tools and device signing for physical-device testing. It combines an Apple Neural Engine audio tower with an int4 decoder running through ONNX Runtime on the CPU.
The model repositories identify the weights as CC BY-NC 4.0. Review the current terms at the source to decide whether they suit your intended use.
Use recordings you have permission to transcribe, and check which app or service receives the audio before treating a local model package as an entirely local workflow.
Reader fit
Who may find it relevant
Builders comparing compact local ASR models across desktop, CPU, and iPhone deployment paths.
People testing short multilingual transcription or hotword handling on their own recordings.
Less relevant for readers who need long recordings without chunking, live streaming, speaker diarization, or a finished transcription app.
Editorial note
Why LifeHubber lists it
For a local transcription workflow whose names or terminology change, Audio8 offers a decoding adjustment without retraining the model. For a short clip containing a known name or specialist term, compare ONNX transcriptions with and without that term in the hotword list. Hotwords bias decoding rather than train the model; inspect the changed word because excessive bias can introduce incorrect words or repetition.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
What to explore next
Compare how transcription reaches the device.
Audio8 covers compact short-form transcription. Continue with a wider speech map, a different CPU model, or a true streaming design when those boundaries matter more.
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