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Moonshine: Speech Recognition for Live Transcription and Voice Commands

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arxiv 2410.15608 v2 pith:TRHQAPVM submitted 2024-10-21 cs.SD cs.CLcs.LGeess.AS

Nat Jeffries , Evan King , Manjunath Kudlur , Guy Nicholson , James Wang , Pete Warden This is my paper · ORCID
classification cs.SDcs.CLcs.LGeess.AS
keywords moonshinespeechlivepositionrecognitiontranscriptionvoiceabsolute
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This paper introduces Moonshine, a family of speech recognition models optimized for live transcription and voice command processing. Moonshine is based on an encoder-decoder transformer architecture and employs Rotary Position Embedding (RoPE) instead of traditional absolute position embeddings. The model is trained on speech segments of various lengths, but without using zero-padding, leading to greater efficiency for the encoder during inference time. When benchmarked against OpenAI's Whisper tiny-en, Moonshine Tiny demonstrates a 5x reduction in compute requirements for transcribing a 10-second speech segment while incurring no increase in word error rates across standard evaluation datasets. These results highlight Moonshine's potential for real-time and resource-constrained applications.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models

    cs.SD 2025-07 conditional novelty 6.0 of 10

    A benchmark of eight post-training quantization methods on Whisper and Moonshine edge speech models across seven datasets, finding 8-bit is safe and 3-bit weights are viable for larger models with advanced methods like SpQR.

  2. WhisperKit: On-device Real-time ASR with Billion-Scale Transformers

    cs.SD 2025-07 conditional novelty 5.0 of 10

    WhisperKit's optimized on-device Whisper Large v3 Turbo streaming system reportedly achieves 0.46 s per-word latency and 2.2% WER, beating cloud baselines in its benchmark.

  3. VibeVoice-ASR-BitNet Technical Report

    cs.SD 2026-07 conditional novelty 4.0 of 10

    Heterogeneous quantization (INT8 tokenizer + 2-bit ternary LM) makes a 1.5B-parameter LLM-based ASR system run at real-time speed on CPUs with 2.9x compression and modest measured WER increases.

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