OpenAI's most accurate Whisper, the v3 large checkpoint
Whisper Large v3 is OpenAI's 1.55B-parameter multilingual Whisper checkpoint, the most accurate
member of the family. It uses the standard Whisper encoder-decoder architecture for automatic
speech recognition and speech translation; v3 was trained on a larger and more diverse labelled
corpus and uses 128 mel-frequency bins, improving robustness across languages and conditions
over earlier large checkpoints. This OpenASR repo repackages the original
openai/whisper-large-v3 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. For a faster
large-grade option, see the distilled whisper-large-v3-turbo.
.oasr packs run with no Python at inference, engineered for CPU and Apple SiliconThese are CLI / local-server examples. The desktop app runs this model without typing a command — see the desktop install path above.
$ openasr pull whisper-large-v3:q8 ↓ whisper-large-v3.oasr 1.6 GB ✓ verified sha256 $ openasr transcribe meeting.wav --backend native --model-pack ~/.openasr/models/whisper-large-v3/q8_0/whisper-large-v3-q8_0.oasr ✓ local transcript · 0 bytes sent
$ openasr serve --backend native --model-pack ~/.openasr/models/whisper-large-v3/q8_0/whisper-large-v3-q8_0.oasr --addr 127.0.0.1:8080 ▶ http://127.0.0.1:8080 · model=whisper-large-v3 · 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-large-v3", file=audio)