Whisper Medium
High-accuracy multilingual Whisper at 769M parameters
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Why it fits
Whisper Medium is OpenAI's 769M-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. Medium delivers
much of the large model's accuracy at a smaller footprint, a strong choice when quality matters
but the largest checkpoint is too heavy. This OpenASR repo repackages the original
openai/whisper-medium 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
- 🎯 769M parameters — near-large accuracy with a more manageable footprint
- 🌐 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-medium:q8 ↓ whisper-medium.oasr 833.8 MB ✓ verified sha256 $ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/whisper-medium/q8_0/whisper-medium-q8_0.oasr ✓ local transcript · 0 bytes sent
$ openasr serve --backend native --model-pack ~/.openasr/models/whisper-medium/q8_0/whisper-medium-q8_0.oasr --addr 127.0.0.1:8080 ▶ http://127.0.0.1:8080 · model=whisper-medium · 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-medium", file=audio)