# Spec for the HF README of handy-computer/cohere-transcribe-03-2026-gguf.
# Consumed by scripts/hf_cards/generate.py.

hf_repo: CohereLabs/cohere-transcribe-03-2026
target_repo: handy-computer/cohere-transcribe-03-2026-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/cohere-transcribe-03-2026.md

upstream_commit: 76b8b23
pin_date: 2026-04-16

# 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: Transformers
  commit: bf0d0b7
  date: 2026-04-18

license: apache-2.0
license_display: Apache-2.0
pipeline_tag: automatic-speech-recognition
languages:
  - en
  - fr
  - de
  - es
  - it
  - pt
  - nl
  - pl
  - el
  - ar
  - ja
  - zh
  - vi
  - ko
tags:
  - gguf
  - transcribe.cpp
  - asr
  - speech-to-text
  - cohere
  - conformer
  - encoder-decoder
  - multilingual

summary: |
  Offline multilingual speech-to-text covering 14 languages (English, French,
  German, Spanish, Italian, Portuguese, Dutch, Polish, Greek, Arabic, Japanese,
  Chinese, Vietnamese, Korean). A Conformer encoder with a Transformer
  encoder-decoder head (cross-attention, tied token embedding). Takes a 16 kHz
  mono WAV and produces a transcript. Decoding is autoregressive.

default_quant_index: 2  # Q8_0

# Capability flags for the transcribe_cpp metadata block.
capabilities:
  streaming: false
  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: 73
    cpu: 9
  ryzen-4750u:
    vulkan: 8
    cpu: 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. BF16 reference baseline: 1.26%.
    Cohere's self-reported number on the same split is 1.25%
    (Open ASR Leaderboard, as of 2026-03-26). Both ours and Cohere's numbers
    use the Whisper EnglishTextNormalizer, so the comparison is apples-to-apples
    and our port matches the upstream reference within rounding.

quants:
  - name: BF16
    filename: cohere-transcribe-03-2026-BF16.gguf
    size: 4.10 GB
    wer: 1.26%
  - name: F16
    filename: cohere-transcribe-03-2026-F16.gguf
    size: 4.11 GB
    wer: 1.26%
  - name: Q8_0
    filename: cohere-transcribe-03-2026-Q8_0.gguf
    size: 2.41 GB
    wer: 1.27%
  - name: Q6_K
    filename: cohere-transcribe-03-2026-Q6_K.gguf
    size: 1.97 GB
    wer: 1.27%
  - name: Q5_K_M
    filename: cohere-transcribe-03-2026-Q5_K_M.gguf
    size: 1.76 GB
    wer: 1.25%
  - name: Q4_K_M
    filename: cohere-transcribe-03-2026-Q4_K_M.gguf
    size: 1.55 GB
    wer: 1.25%
