# Spec for the HF README of handy-computer/canary-1b-gguf.
# Consumed by scripts/hf_cards/generate.py.
#
# canary-1b is the only family member under a non-commercial license.
# The converter writes general.license="CC-BY-NC-4.0" and
# general.license.link=https://creativecommons.org/licenses/by-nc/4.0/
# into every preset; the YAML below mirrors that distinction so the HF
# card surfaces the constraint to anyone clicking through to download.

hf_repo: nvidia/canary-1b
target_repo: handy-computer/canary-1b-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/canary-1b.md

upstream_commit: 1698acf
pin_date: 2026-05-08

validation:
  reference: NeMo
  commit: db53eda
  date: 2026-05-08

license: cc-by-nc-4.0
license_display: CC-BY-NC-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
  - non-commercial

summary: |
  Offline multilingual speech-to-text and translation. A 1B-parameter
  multitask AED with a 24-layer FastConformer encoder and a 24-layer
  Transformer decoder — the original canary release. Supports automatic
  speech recognition in English, German, Spanish, and French, and
  translation between supported pairs. Takes a 16 kHz mono WAV and
  produces a transcript. Not a streaming model.
  **License: CC-BY-NC-4.0 (non-commercial only)** — the only canary
  variant under a non-commercial license.

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: 42.5
    cpu: 19.4
  ryzen-4750u:
    vulkan: 8.9
    cpu: 5.6

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: 1.55%.
    NVIDIA's self-reported number on the upstream model card is 1.48%;
    likely this is due to differences in how we score WER, based on the
    results we have from canary-180m-flash.

quants:
  - name: F32
    filename: canary-1b-F32.gguf
    size: 3.8 GB
    wer: 1.55%
  - name: F16
    filename: canary-1b-F16.gguf
    size: 1.9 GB
    wer: 1.55%
  - name: Q8_0
    filename: canary-1b-Q8_0.gguf
    size: 1.1 GB
    wer: 1.55%
  - name: Q6_K
    filename: canary-1b-Q6_K.gguf
    size: 891 MB
    wer: 1.57%
  - name: Q5_K_M
    filename: canary-1b-Q5_K_M.gguf
    size: 799 MB
    wer: 1.57%
  - name: Q4_K_M
    filename: canary-1b-Q4_K_M.gguf
    size: 696 MB
    wer: 1.55%
