FireRedPunc (GGUF)
GGUF conversion of FireRedTeam/FireRedPunc for use with CrispASR.
Adds punctuation to unpunctuated ASR output using a BERT-based token classifier.
Model Details
- Architecture: BERT-base (LERT) — 12L, d=768, 12 heads, d_ffn=3072, GELU
- Parameters: ~102M
- Classifier: Linear(768, 5) — 5 punctuation classes
- Labels: space, ，(comma), 。(period), ？(question), ！(exclamation)
- Vocabulary: chinese-bert-wwm-ext (21,128 tokens, WordPiece)
- Max sequence: 512 tokens (longer texts are automatically chunked)
- Languages: Chinese + English
- License: Apache 2.0
Usage with CrispASR
# Add punctuation to any ASR backend's output
crispasr --backend wav2vec2 -m wav2vec2.gguf --punc-model fireredpunc-q8_0.gguf -f audio.wav
# Works with all backends
crispasr --backend omniasr -m omniasr-ctc-1b-q4_k.gguf --punc-model fireredpunc-q8_0.gguf -f audio.wav
Available Files
| File | Quant | Size | Description |
|---|---|---|---|
fireredpunc.gguf | F16 | 195 MB | Full precision |
fireredpunc-q8_0.gguf | Q8_0 | 104 MB | Recommended — lossless quality |
fireredpunc-q4_k.gguf | Q4_K | 56 MB | Smaller but may miss some punctuation |
Recommendation: Use Q8_0. Token classification is more sensitive to quantization than language modeling — Q4_K occasionally drops commas that Q8_0 and F16 correctly predict.
Example
Input (from CTC ASR):
and so my fellow americans ask not what your country can do for you ask what you can do for your country
Output (with FireRedPunc):
And so my fellow americans， ask not what your country can do for you， ask what you can do for your country？
Conversion
python models/convert-fireredpunc-to-gguf.py \
--input /path/to/FireRedPunc \
--output fireredpunc.gguf
Original Model
- Source: FireRedTeam/FireRedPunc
- Paper: FireRedASR2S
- Base: LERT (Linguistically-motivated BERT)
- Training: 18.57B Chinese characters + 2.20B English words
Provenance and EU AI Act Art. 53 note
- Upstream model: FireRedTeam/FireRedPunc — published by
FireRedTeam. - Upstream licence:
apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.