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

hf_repo: openai/whisper-tiny.en
target_repo: handy-computer/whisper-tiny.en-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/whisper-tiny.en.md

upstream_commit: 87c7102
pin_date: 2026-04-25

validation:
  reference: transformers
  commit: 5.6.1
  date: 2026-04-26

license: apache-2.0
license_display: Apache-2.0
pipeline_tag: automatic-speech-recognition
languages:
  - "en"
tags:
  - gguf
  - transcribe.cpp
  - asr
  - speech-to-text
  - whisper
  - openai
  - en

summary: |
  OpenAI Whisper tiny.en — converted to GGUF for transcribe.cpp. English-only; faster than the multilingual model at the same size. Encoder-decoder transformer; 30-second windows with chunked long-form decoding.

default_quant_index: 2

# Capability flags for the transcribe_cpp metadata block.
capabilities:
  streaming: false
  translate: false
  lang_detect: false
  timestamps: segment  # 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: 279.8
    cpu: 78.7
  ryzen-4750u:
    vulkan: 60.7
    cpu: 26.1

wer:
  source: LibriSpeech test-clean
  notes: |
    WER measured on the full LibriSpeech test-clean split (2620 utterances) with the transcribe.cpp default decode (greedy, suppress_tokens, temperature fallback, segment timestamps enabled). OpenAI's self-reported number on the same split is 5.66%. We don't know upstream's exact eval config, but the most likely cause of any divergence is that OpenAI's `model.generate()` defaults to `<|notimestamps|>` while transcribe.cpp's pipeline runs with timestamps enabled. Numbers come from a single Metal-backed run; Metal's non-deterministic parallel reductions can shift corpus WER by ~0.1pp between runs, mostly driven by short-clip hallucination outcomes on the noise floor.

quants:
  - name: F32
    filename: whisper-tiny.en-F32.gguf
    size: 146 MB
    wer: 5.77%
  - name: F16
    filename: whisper-tiny.en-F16.gguf
    size: 76 MB
    wer: 5.77%
  - name: Q8_0
    filename: whisper-tiny.en-Q8_0.gguf
    size: 44 MB
    wer: 5.72%
  - name: Q6_K
    filename: whisper-tiny.en-Q6_K.gguf
    size: 43 MB
    wer: 5.80%
  - name: Q5_K_M
    filename: whisper-tiny.en-Q5_K_M.gguf
    size: 42 MB
    wer: 5.89%
  - name: Q4_K_M
    filename: whisper-tiny.en-Q4_K_M.gguf
    size: 42 MB
    wer: 5.99%
