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Automatic Speech Recognition
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metadata 
license: apache-2.0
base_model: openai/whisper-small
pipeline_tag: automatic-speech-recognition
library_name: openasr
tags:
  - automatic-speech-recognition
  - speech-to-text
  - openasr
  - oasr
  - whisper-small

Whisper Small · OpenASR

Compact multilingual Whisper for local transcription

License Format Runtime Base model

Native speech-to-text in the OpenASR runtime — engineered for peak performance on CPU & GPU, no Python at inference time.

✨ Highlights

  • 🎧 Multilingual ASR — transcribes many languages and can translate speech to English
  • 🧠 244M parameters — the small Whisper checkpoint balances accuracy, footprint, and speed
  • 🌐 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 peak performance on CPU & GPU

🚀 Quickstart 

# 1. Install the OpenASR CLI  ·  https://openasr.org
# 2. Pull a build (pick a quant — see the table below)
openasr pull whisper-small:q8

# 3. Transcribe
openasr transcribe audio.wav --model whisper-small

All builds for this model:

openasr pull whisper-small:fp16
openasr pull whisper-small:q8
openasr pull whisper-small:q4

📦 Available builds

Quant File (.oasr) Size RAM peak RTF · M1 CPU RTF · M1 GPU JFK ΔWER vs fp16
fp16 whisper-small-fp16.oasr 489 MB 1.57 GB 0.13× 0.08× 0.0%
q8_0 whisper-small-q8_0.oasr 303 MB 881 MB 0.11× 0.07× 0.0%
q4_k whisper-small-q4_k.oasr 204 MB 665 MB 0.10× 0.07× 0.0%

RTF = real-time factor on the fixed 11s JFK clip (lower is faster); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. q8_0 is the recommended default — near-reference quality at a fraction of the footprint.

🧠 About Whisper Small

Whisper Small is OpenAI's 244M-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. Compared with larger Whisper checkpoints, the small model is easier to run locally while retaining the broad zero-shot behavior that makes Whisper useful across noisy datasets and domains. This OpenASR repo repackages the original openai/whisper-small 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.

⚙️ How these packs were made

Converted from openai/whisper-small with the OpenASR importer:

openasr model-pack import-whisper-local <src> <out>.oasr \
  --package-id whisper-small --quantization {fp16,q8-0,q4-k}

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

⚖️ License

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.

🙏 Acknowledgements

This pack is a redistribution of Whisper Small, released by OpenAI (openai/whisper-small). 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.

🔗 Links