# Spec for the HF README of handy-computer/Fun-ASR-MLT-Nano-2512-gguf.
# Consumed by scripts/hf_cards/generate.py.

hf_repo: FunAudioLLM/Fun-ASR-MLT-Nano-2512
target_repo: handy-computer/Fun-ASR-MLT-Nano-2512-gguf
transcribe_docs_url: https://github.com/handy-computer/transcribe.cpp/blob/main/docs/models/fun-asr-mlt-nano-2512.md

# Pinned at intake (Stage 1).
upstream_commit: cf67a938bf2829959d08fdfb84e186eff02a67ff
pin_date: 2026-05-06

# 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: FunASR
  commit: f094d28
  date: 2026-05-06

# Upstream license is "FunASR-Model-License-1.1" — not a standard SPDX id.
# The HF YAML field accepts a free-form `other` plus a license_name /
# license_link override on the upstream card; the renderer emits
# `license: other` and surfaces the human-facing form via `license_display`.
license: other
license_display: FunASR Model Open Source License Agreement v1.1
pipeline_tag: automatic-speech-recognition
languages:
  - zh
  - en
  - yue
  - ja
  - ko
  - vi
  - id
  - th
  - ms
  - tl
  - ar
  - hi
  - bg
  - hr
  - cs
  - da
  - nl
  - et
  - fi
  - el
  - hu
  - ga
  - lv
  - lt
  - mt
  - pl
  - pt
  - ro
  - sk
  - sl
  - sv
tags:
  - gguf
  - transcribe.cpp
  - asr
  - speech-to-text
  - fun-asr-nano
  - fun-asr-mlt-nano
  - sense-voice-encoder
  - qwen3
  - audio-llm
  - multilingual

summary: |
  Offline speech-to-text covering 31 languages, with focused optimization
  on East and Southeast Asian languages: Chinese, English, Cantonese,
  Japanese, Korean, Vietnamese, Indonesian, Thai, Malay, Filipino, plus
  Arabic, Hindi, and 19 European languages (Bulgarian, Croatian, Czech,
  Danish, Dutch, Estonian, Finnish, Greek, Hungarian, Irish, Latvian,
  Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian,
  Swedish). Same architecture as Fun-ASR-Nano-2512 (~800M trainable
  parameters: frozen SenseVoiceEncoderSmall + 2-layer audio adaptor +
  bundled Qwen3-0.6B LLM); trained on a smaller multilingual corpus
  ("hundreds of thousands of hours" per the model card, vs Nano's
  "tens of millions"). Takes a 16 kHz mono WAV and emits text. Not
  streaming, no translation, no timestamps. ITN (inverse text
  normalization) is supported by the model and exposed via the
  `--itn` CLI flag and `transcribe_funasr_nano_params { use_itn }`
  in the library API.

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: 68
    cpu: 16
  ryzen-4750u:
    vulkan: 9
    cpu: 4.5

wer:
  source: LibriSpeech test-clean
  notes: |
    WER measured on the full LibriSpeech test-clean split (2620 utterances)
    with greedy LLM decoding via the bundled Qwen3-0.6B head. The publisher
    does not report a numerical LibriSpeech WER for the MLT variant
    specifically (the shared README's per-model table covers Fun-ASR-Nano
    only). Gate baseline is our own FunASR 1.3.1 reference run on the same
    manifest: 1.76% (95% CI [1.60%, 1.93%]). transcribe.cpp's BF16 port
    matches that baseline within -0.02 percentage-points. LibriSpeech is
    English only; the strength of the MLT variant is multilingual coverage,
    not English accuracy. For the other 30 languages, run your own
    representative manifest.

quants:
  - name: BF16
    filename: Fun-ASR-MLT-Nano-2512-BF16.gguf
    size: 1590 MB
    wer: 1.74%
  - name: F16
    filename: Fun-ASR-MLT-Nano-2512-F16.gguf
    size: 1590 MB
    wer: 1.74%
  - name: Q8_0
    filename: Fun-ASR-MLT-Nano-2512-Q8_0.gguf
    size: 850 MB
    wer: 1.74%
  - name: Q6_K
    filename: Fun-ASR-MLT-Nano-2512-Q6_K.gguf
    size: 659 MB
    wer: 1.69%
  - name: Q5_K_M
    filename: Fun-ASR-MLT-Nano-2512-Q5_K_M.gguf
    size: 602 MB
    wer: 1.77%
  - name: Q4_K_M
    filename: Fun-ASR-MLT-Nano-2512-Q4_K_M.gguf
    size: 531 MB
    wer: 1.89%
