OpenASR/whisper-small.en

Whisper Small (English) · OpenASR

Balanced English-only Whisper for accurate local transcription

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

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✨ Highlights

• 🇬🇧 English-only — specialized for English, typically more accurate on English than the same-size multilingual model
• 🧠 244M parameters — the small checkpoint balances English 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.en:q8

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

All builds for this model:

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

📦 Available builds

Quant	File (.oasr)	Size	RAM peak	RTF · M1 CPU	RTF · M1 GPU	JFK ΔWER vs fp16
fp16	whisper-small.en-fp16.oasr	489 MB	1.59 GB	0.13×	0.10×	0.0%
q8_0	whisper-small.en-q8_0.oasr	303 MB	902 MB	0.12×	0.09×	0.0%
q4_k	whisper-small.en-q4_k.oasr	204 MB	687 MB	0.11×	0.08×	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 (English)

Whisper Small.en is OpenAI's 244M-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, making it a strong default for English-only workloads that want accuracy without a large footprint. This OpenASR repo repackages the original openai/whisper-small.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.

⚙️ How these packs were made

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

openasr model-pack import-whisper-local <src> <out>.oasr \
  --package-id whisper-small.en --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.en, released by OpenAI (openai/whisper-small.en). 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

• 🦀 OpenASR — https://github.com/QuintinShaw/openasr
• 🌐 Website — https://openasr.org
• 🤗 Upstream model — openai/whisper-small.en