All builds for this model:

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.

Converted from openai/whisper-large-v3 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: Apache-2.0 (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, released by OpenAI (openai/whisper-large-v3). All credit for the original model, training recipe, and weights belongs to OpenAI. The upstream Hugging Face model card declares Apache-2.0 licensing; OpenASR only converts the weights into .oasr packages and adds quantized builds for local runtime use.