Whisper Tiny (English)
The smallest English-only Whisper, fastest for English speech
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let's line up this week's release plan,
the beta build can ship on Friday,
then loop in design and QA,
Why it fits
Whisper Tiny.en is OpenAI's 39M-parameter English-only Whisper checkpoint. It uses the standard
Whisper encoder-decoder architecture for automatic speech recognition, trained with large-scale
weak supervision on 680k hours of labelled speech. As an English-specialized model it tends to
outperform the same-size multilingual Whisper on English audio, at the lowest footprint and
fastest inference in the family. This OpenASR repo repackages the original openai/whisper-tiny.en
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
- 🇬🇧 English-only — specialized for English, typically more accurate on English than the same-size multilingual model
- ⚡ 39M parameters — the smallest, fastest, and lightest Whisper checkpoint
- 🌐 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.en:q8 ↓ whisper-tiny.en.oasr 60.3 MB ✓ verified sha256 $ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/whisper-tiny.en/q8_0/whisper-tiny.en-q8_0.oasr ✓ local transcript · 0 bytes sent
$ openasr serve --backend native --model-pack ~/.openasr/models/whisper-tiny.en/q8_0/whisper-tiny.en-q8_0.oasr --addr 127.0.0.1:8080 ▶ http://127.0.0.1:8080 · model=whisper-tiny.en · 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.en", file=audio)