# Spec for the HF README of handy-computer/canary-180m-flash-gguf.
# Consumed by scripts/hf_cards/generate.py.

hf_repo: nvidia/canary-180m-flash
target_repo: handy-computer/canary-180m-flash-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/canary-180m-flash.md

upstream_commit: b12ab41
pin_date: 2026-05-08

# 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: NeMo
  commit: db53eda
  date: 2026-05-08

license: cc-by-4.0
license_display: CC-BY-4.0
pipeline_tag: automatic-speech-recognition
languages:
  - en
  - de
  - es
  - fr
tags:
  - gguf
  - transcribe.cpp
  - asr
  - speech-to-text
  - canary
  - multitask-aed
  - encoder-decoder
  - translation

summary: |
  Offline multilingual speech-to-text and translation. A 182M-parameter
  multitask AED with a 17-layer FastConformer encoder and a 4-layer
  Transformer decoder. Supports automatic speech recognition in English,
  German, Spanish, and French, and bidirectional EN↔{DE, ES, FR}
  translation. Takes a
  16 kHz mono WAV and produces a transcript. Not a streaming model;
  word/segment timestamps are upstream-experimental and not exposed in
  the v1 port.

default_quant_index: 2  # Q8_0

# Capability flags for the transcribe_cpp metadata block.
capabilities:
  streaming: false
  translate: true
  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: 139.6
    cpu: 74
  ryzen-4750u:
    vulkan: 31.9
    cpu: 21.3

wer:
  source: LibriSpeech test-clean
  notes: |
    WER measured on the full LibriSpeech test-clean split (2620 utterances)
    with greedy decoding and no external LM. F32 reference baseline: 1.94%.
    On the same wavs, NeMo's reference run produces 1.93% (one substitution
    difference out of ~27k reference words), so the F32 port matches the
    reference framework at the noise floor. NVIDIA's self-reported number
    on the upstream model card is 1.87%.

quants:
  - name: F32
    filename: canary-180m-flash-F32.gguf
    size: 721 MB
    wer: 1.94%
  - name: F16
    filename: canary-180m-flash-F16.gguf
    size: 364 MB
    wer: 1.94%
  - name: Q8_0
    filename: canary-180m-flash-Q8_0.gguf
    size: 208 MB
    wer: 1.93%
  - name: Q6_K
    filename: canary-180m-flash-Q6_K.gguf
    size: 168 MB
    wer: 1.93%
  - name: Q5_K_M
    filename: canary-180m-flash-Q5_K_M.gguf
    size: 151 MB
    wer: 1.90%
  - name: Q4_K_M
    filename: canary-180m-flash-Q4_K_M.gguf
    size: 133 MB
    wer: 1.93%
