Whisper Small
Compact multilingual Whisper for local transcription
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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 Small is OpenAI's 244M-parameter multilingual Whisper checkpoint. 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.
Compared with larger Whisper checkpoints, the small model is easier to run locally while
retaining the broad zero-shot behavior that makes Whisper useful across noisy datasets and
domains. This OpenASR repo repackages the original openai/whisper-small 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 tighter 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
- 🧠 244M parameters — the small Whisper checkpoint balances accuracy, footprint, and speed
- 🌐 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-small:q8 ↓ whisper-small.oasr 288.9 MB ✓ verified sha256 $ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/whisper-small/q8_0/whisper-small-q8_0.oasr ✓ local transcript · 0 bytes sent
$ openasr serve --backend native --model-pack ~/.openasr/models/whisper-small/q8_0/whisper-small-q8_0.oasr --addr 127.0.0.1:8080 ▶ http://127.0.0.1:8080 · model=whisper-small · 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-small", file=audio)