Live transcription · English

Whisper Base (English)

Compact English-only Whisper for fast English transcription

English · 18.5× · 102.7 MB

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On-deviceOn this computer
interview.m4a12:04

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 Base.en is OpenAI's 74M-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, while staying small and fast. This OpenASR repo repackages the original openai/whisper-base.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 tighter 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
  • 🪶 74M parameters — small and fast, a clear step up from tiny.en
  • 🌐 Weak-supervision scale — trained with Whisper's 680k-hour labelled speech corpus
  • 🦀 Native in OpenASR — .oasr packs run with no Python at inference, engineered for CPU and Apple Silicon

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Developer details CLI commands, file hashes, and per-quant downloads — for scripting and verification.

Downloads by quantization

Pull stringSizeQuant
whisper-base.en:fp16 142.1 MB fp16
open .oasr
whisper-base.en:q8default 102.7 MB q8_0
open .oasr
whisper-base.en:q4 90.7 MB q4_k
open .oasr

Model facts

Size102.7 MB
Speed18.5× real-time
LanguageEnglish
Familywhisper
Vendoropenai
Released2022-09-21
Peak memory383.3 MB
Quantizationq8_0
LicenseApache-2.0
HF repoOpenASR/whisper-base.en
Revisiond80b948ee82890c5b18d2e12efadd697529cc559
sha256e62c3ae3478dd36c2b4c74a8d798355b1d16d5533083acd24cc90088e6525d05
Download .oasr CLI reference →

Usage

These are CLI / local-server examples. The desktop app runs this model without typing a command — use Install above.

bash · transcribe a file
$ openasr pull whisper-base.en:q8
↓ whisper-base.en.oasr  102.7 MB  ✓ verified sha256
$ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/whisper-base.en/q8_0/whisper-base.en-q8_0.oasr
✓ local transcript · 0 bytes sent
bash · serve a local API
$ openasr serve --backend native --model-pack ~/.openasr/models/whisper-base.en/q8_0/whisper-base.en-q8_0.oasr --addr 127.0.0.1:8080
▶ http://127.0.0.1:8080 · model=whisper-base.en · 0 bytes will leave this host
python · client.py
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-base.en", file=audio)