# Spec for the HF README of handy-computer/moonshine-streaming-medium-gguf.
# Consumed by scripts/hf_cards/generate.py.

hf_repo: UsefulSensors/moonshine-streaming-medium
target_repo: handy-computer/moonshine-streaming-medium-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/moonshine-streaming-medium.md

upstream_commit: 57b8436
pin_date: 2026-05-06

validation:
  reference: HF Transformers v5.7.0
  commit: 0d312ce
  date: 2026-05-06

license: mit
license_display: MIT
pipeline_tag: automatic-speech-recognition
languages:
  - en
tags:
  - gguf
  - transcribe.cpp
  - asr
  - speech-to-text
  - moonshine
  - moonshine-streaming
  - useful-sensors
  - encoder-decoder

summary: |
  Offline English speech-to-text. A 245M-parameter encoder-decoder ASR model
  designed for streaming use (ergodic encoder + sliding-window attention,
  50 Hz time-domain frontend). Same family as moonshine-streaming-tiny and
  moonshine-streaming-small; deepest of the three (14 / 14 layers) and widest
  hidden dims (encoder 768 / decoder 640). Takes a 16 kHz mono WAV and
  produces a transcript. No translation, no multilingual capability,
  no timestamps.

default_quant_index: 2  # Q8_0

# Capability flags for the transcribe_cpp metadata block.
capabilities:
  streaming: true
  translate: false
  lang_detect: false
  timestamps: none  # none | segment | word | token

# Speedup-over-realtime (×RT) per rig/backend, from docs/models; published
# raw as rtf_<machine> in the metadata block.
perf:
  m4-max:
    metal: 63
    cpu: 35.5
  ryzen-4750u:
    vulkan: 14
    cpu: 7.5

wer:
  source: LibriSpeech test-clean
  notes: |
    WER measured on the full LibriSpeech test-clean split (2620 utterances)
    with greedy decoding (`num_beams=1`, `do_sample=False`). F32 reference
    baseline: 2.16%. Quants are numerically indistinguishable from F32 on
    this manifest. Useful Sensors' self-reported number on this split is
    2.08% from the Open ASR Leaderboard table; the +0.08pp residual matches
    the same scoring / text-normalization difference seen across the tiny
    and small variants (cross-checked against HF Transformers on tiny and
    found to be at 99.6% identical hypotheses to our port), and is not a
    numerical drift. Q6_K / Q5_K_M / Q4_K_M GGUFs are not currently shipped
    for this variant.

quants:
  - name: F32
    filename: moonshine-streaming-medium-F32.gguf
    size: 1015 MB
    wer: 2.16%
  - name: F16
    filename: moonshine-streaming-medium-F16.gguf
    size: 509 MB
    wer: 2.16%
  - name: Q8_0
    filename: moonshine-streaming-medium-Q8_0.gguf
    size: 282 MB
    wer: 2.16%
