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

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

upstream_commit: f8e9dfd
pin_date: 2026-05-06

# Validation pin for the most recent upload. Updated on each release —
# older HF revisions carry whatever value was current at their upload time.
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 34M-parameter encoder-decoder ASR model
  designed for streaming use (ergodic encoder + sliding-window attention,
  50 Hz time-domain frontend). 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: 159
    cpu: 211
  ryzen-4750u:
    vulkan: 59.5
    cpu: 54.5

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: 4.53%. The HF Transformers reference scored on the same manifest
    in the same regime lands at 4.52% with 99.6% byte-identical hypotheses to
    our F32, so the port is at exact parity with the reference. Useful Sensors'
    self-reported number on this split is 4.49% from the Open ASR Leaderboard
    table; the +0.04pp residual is a scoring / text-normalization difference vs
    that methodology, not a numerical drift in the port. Q6_K / Q5_K_M / Q4_K_M
    GGUFs are not currently shipped for this variant.

quants:
  - name: F32
    filename: moonshine-streaming-tiny-F32.gguf
    size: 169 MB
    wer: 4.53%
  - name: F16
    filename: moonshine-streaming-tiny-F16.gguf
    size: 85 MB
    wer: 4.53%
  - name: Q8_0
    filename: moonshine-streaming-tiny-Q8_0.gguf
    size: 48 MB
    wer: 4.52%
