Whisper Tiny
The smallest multilingual Whisper for fast local transcription
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then loop in design and QA,
Why it fits
Whisper Tiny is OpenAI's 39M-parameter multilingual Whisper checkpoint, the smallest member of
the Whisper family. It uses the standard Whisper encoder-decoder architecture for automatic
speech recognition and speech translation, trained with large-scale weak supervision on 680k
hours of labelled speech. The tiny model trades some accuracy for the lowest footprint and
fastest inference, which suits low-resource devices and latency-sensitive use. This OpenASR
repo repackages the original openai/whisper-tiny weights as .oasr packs that run natively
in the OpenASR runtime with no Python at inference time. For most users the q8_0 build is the
recommended default; q4_k is for the tightest memory budgets and fp16 is for verification or
maximum fidelity.
What you can do with it
- 🎧 Multilingual ASR — transcribes many languages and can translate speech to English
- ⚡ 39M parameters — the smallest Whisper checkpoint, the fastest and lightest to run
- 🌐 Weak-supervision scale — trained with Whisper's 680k-hour labelled speech corpus
- 🦀 Native in OpenASR —
.oasrpacks run with no Python at inference, engineered for CPU and Apple Silicon
Other models
Developer details CLI commands, file hashes, and per-quant downloads — for scripting and verification.
Model facts
Usage
These are CLI / local-server examples. The desktop app runs this model without typing a command — use Install above.
$ openasr pull whisper-tiny:q8 ↓ whisper-tiny.oasr 60.4 MB ✓ verified sha256 $ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/whisper-tiny/q8_0/whisper-tiny-q8_0.oasr ✓ local transcript · 0 bytes sent
$ openasr serve --backend native --model-pack ~/.openasr/models/whisper-tiny/q8_0/whisper-tiny-q8_0.oasr --addr 127.0.0.1:8080 ▶ http://127.0.0.1:8080 · model=whisper-tiny · 0 bytes will leave this host
from openai import OpenAI client = OpenAI(base_url="http://127.0.0.1:8080/v1", api_key="local") audio = open("meeting.wav", "rb") text = client.audio.transcriptions.create(model="whisper-tiny", file=audio)