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fireredpunc-GGUF

Token Classification
GGUF
Chinese
English
ggml
punctuation
text-processing
bert
nlp
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metadata 
license: apache-2.0
language:
  - zh
  - en
tags:
  - gguf
  - ggml
  - punctuation
  - text-processing
  - bert
  - nlp
base_model: FireRedTeam/FireRedPunc
pipeline_tag: token-classification

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