# Spec for the HF README of handy-computer/qwen3-asr-0.6b-gguf.
# Consumed by scripts/hf_cards/generate.py.

hf_repo: Qwen/Qwen3-ASR-0.6B
target_repo: handy-computer/Qwen3-ASR-0.6B-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/qwen3-asr-0.6b.md

upstream_commit: 5eb144179a02acc5e5ba31e748d22b0cf3e303b0
pin_date: 2026-04-19

# 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: qwen_asr 0.0.6
  commit: 3f61df7
  date: 2026-04-20

license: apache-2.0
license_display: Apache-2.0
pipeline_tag: automatic-speech-recognition
languages:
  - zh
  - en
  - yue
  - ar
  - de
  - fr
  - es
  - pt
  - id
  - it
  - ko
  - ru
  - th
  - vi
  - ja
  - tr
  - hi
  - ms
  - nl
  - sv
  - da
  - fi
  - pl
  - cs
  - fil
  - fa
  - el
  - ro
  - hu
  - mk
tags:
  - gguf
  - transcribe.cpp
  - asr
  - speech-to-text
  - qwen3
  - qwen3-asr
  - multilingual

summary: |
  Offline multilingual speech-to-text. An 18-layer bidirectional audio encoder
  feeds a 28-layer Qwen3 causal LM with audio-token injection (fused
  audio+text sequence, no cross-attention). Auto-detects the audio's language
  across 30 languages and emits the transcript in that language. Takes a
  16 kHz mono WAV; explicit language hints are not supported at this time.

default_quant_index: 2  # Q8_0

# Capability flags for the transcribe_cpp metadata block.
capabilities:
  streaming: false
  translate: false
  lang_detect: true
  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: 65
    cpu: 16.5
  ryzen-4750u:
    vulkan: 8
    cpu: 4.3

wer:
  source: LibriSpeech test-clean
  notes: |
    WER measured on the full LibriSpeech `test-clean` split (2620 English
    utterances) with the Whisper-style English text normalizer and jiwer 3.x,
    on the metal backend of an Apple M4. Qwen3-ASR is a multilingual model —
    this number characterizes the English case only. BF16 / F16 / Q8_0 / Q6_K
    are all within bootstrap CI of each other; Q5_K_M and Q4_K_M show a small
    but real regression driven by the tied token-embedding / head. Reproduce
    with `scripts/wer/run.py` + `scripts/wer/score.py`.

quants:
  - name: BF16
    filename: Qwen3-ASR-0.6B-BF16.gguf
    size: 1.46 GB
    wer: 2.11%
  - name: F16
    filename: Qwen3-ASR-0.6B-F16.gguf
    size: 1.47 GB
    wer: 2.12%
  - name: Q8_0
    filename: Qwen3-ASR-0.6B-Q8_0.gguf
    size: 811 MB
    wer: 2.11%
  - name: Q6_K
    filename: Qwen3-ASR-0.6B-Q6_K.gguf
    size: 763 MB
    wer: 2.10%
  - name: Q5_K_M
    filename: Qwen3-ASR-0.6B-Q5_K_M.gguf
    size: 707 MB
    wer: 2.21%
  - name: Q4_K_M
    filename: Qwen3-ASR-0.6B-Q4_K_M.gguf
    size: 654 MB
    wer: 2.26%
