All builds for this model:

Whisper Large v3 Turbo is OpenAI's faster variant of Whisper large-v3: it keeps the same Whisper architecture and multilingual speech-recognition/translation interface, but reduces the decoder depth from 32 layers to 4. The upstream card describes the result as much faster with only a minor quality trade-off, while retaining Whisper's broad zero-shot behavior from training on more than five million hours of labeled audio. This OpenASR repo repackages the original openai/whisper-large-v3-turbo 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.

Converted from openai/whisper-large-v3-turbo with the OpenASR importer:

The .oasr container is GGUF-backed; packs use zero-copy mmap weight binding and graph buffer reuse to keep peak memory low.

These packs inherit the upstream model's license: MIT (source). OpenASR packaging retains the upstream copyright and NOTICE; the only modifications are format conversion and quantization.

This pack is a redistribution of Whisper Large v3 Turbo, released by OpenAI (openai/whisper-large-v3-turbo). All credit for the original model, training recipe, and weights belongs to OpenAI. The packs inherit the upstream MIT license; OpenASR only converts the weights into .oasr packages and adds quantized builds for local runtime use.