README.md

metadata

language:
  - ko
license: mit
library_name: transformers
tags:
  - whisper
  - automatic-speech-recognition
  - korean
  - disfluency-detection
  - speech
  - asr
base_model: openai/whisper-large-v3-turbo
pipeline_tag: automatic-speech-recognition
model-index:
  - name: SeloWhisper-ko-disfluency
    results:
      - task:
          type: automatic-speech-recognition
          name: Korean ASR with Disfluency Detection
        dataset:
          type: korean-conversational-speech
          name: Held-out clean set
        metrics:
          - type: cer
            value: 0.1653
          - type: wer
            value: 0.33
          - type: f1
            value: 0.8792
            name: Disfluency F1
      - task:
          type: automatic-speech-recognition
          name: Korean ASR with Disfluency Detection
        dataset:
          type: korean-conversational-speech
          name: Held-out noisy set
        metrics:
          - type: cer
            value: 0.1529
          - type: wer
            value: 0.3558
          - type: f1
            value: 0.9226
            name: Disfluency F1

SeloWhisper-ko-disfluency

Korean ASR with inline disfluency detection, fine-tuned from openai/whisper-large-v3-turbo.

Transcribes Korean speech while emitting 10 special tokens for fillers, repetitions, and laughter directly inside the transcript.

Note. Methodology, training data, and analysis are reserved for an upcoming paper. This repository releases only the model weights and the inference recipe.

---

Model Specification

Base model	openai/whisper-large-v3-turbo
Architecture	Whisper encoder–decoder (encoder 32 L / decoder 4 L, d_model 1280)
Parameters	~809 M
Vocabulary	51,867 base + 10 disfluency tokens + dedicated <|pad|>
Language	Korean (ko)
Sampling rate	16 kHz, mono
Max target length	448 tokens
Released checkpoint	step 2,500 (selected by held-out disfluency F1)

---

Disfluency Tokens

Token	Korean cue	Meaning
<ah>	아	filler "ah"
<uh>	어	filler "uh"
<um>	음	filler "um"
<gue>	그	filler "geu"
<jeo>	저	filler "jeo"
<mwo>	뭐	filler "mwo"
<mak>	막	filler "mak"
<repeat>	—	repeated word / syllable
<laugh>	—	laughter
<other>	—	other disfluency

Tokens are registered as additional_special_tokens. A dedicated <|pad|> token is introduced so EOS is preserved during label masking.

---

Usage

import torch, torchaudio
from transformers import WhisperProcessor, WhisperForConditionalGeneration

MODEL_ID = "rearleg/SeloWhisper-ko-disfluency"

processor = WhisperProcessor.from_pretrained(MODEL_ID)
model = WhisperForConditionalGeneration.from_pretrained(MODEL_ID).eval()

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

waveform, sr = torchaudio.load("sample.wav")
if sr != 16000:
    waveform = torchaudio.functional.resample(waveform, sr, 16000)
    sr = 16000
if waveform.shape[0] > 1:
    waveform = waveform.mean(dim=0, keepdim=True)

inputs = processor(
    waveform.squeeze().numpy(),
    sampling_rate=sr,
    return_tensors="pt",
).to(device)

with torch.no_grad():
    generated = model.generate(
        inputs["input_features"],
        max_length=448,
        num_beams=1,
        do_sample=False,
    )

# Keep special tokens so disfluencies remain visible
transcription = processor.batch_decode(generated, skip_special_tokens=False)[0]
print(transcription)

Decode with skip_special_tokens=False to keep disfluency tags visible. Strip Whisper meta tokens (<|ko|>, <|transcribe|>, <|notimestamps|>, <|endoftext|>) manually if you only want the disfluency annotations.

This release ships a generation_config.json with forced_decoder_ids, suppress_tokens, and begin_suppress_tokens already cleared — no extra unsetting is required at inference time.

---

Performance (checkpoint-2500)

Evaluated on held-out Korean conversational speech.

Split	CER	WER	sWER	sCER	Filler P	Filler R	Filler F1
Clean	0.1653	0.3300	0.3785	0.1250	0.9118	0.8488	0.8792
Noisy	0.1529	0.3558	0.4391	0.1224	0.9420	0.9039	0.9226

Metric definitions

• CER — character error rate, whitespace included.
• WER — word error rate. Each disfluency token is treated as a single unit (substituted to a single private-use character during scoring) to avoid penalty inflation.
• sWER — WER after whitespace normalization, anchoring hypothesis spacing to the reference.
• sCER — CER on whitespace-stripped syllable streams (Korean-specific).
• Filler P / R / F1 — counter-based across the 10 disfluency tokens: TP = min(ref_count, hyp_count) per token, summed globally; FP / FN computed symmetrically.

---

Limitations

• Optimized for Korean spontaneous speech; not tuned for broadcast news, code-switching, or non-Korean speech.
• Like all Whisper-family models, hallucination is possible on silent or very short clips — a VAD or RMS-based silence filter upstream is recommended.

---

License

MIT — see LICENSE for the full text.

---

Citation

@misc{cheon2025selowhisper,
  title        = {SeloWhisper-ko-disfluency: Korean ASR with Inline Disfluency Detection},
  author       = {Cheon, Changhyun},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/rearleg/SeloWhisper-ko-disfluency}}
}

Whisper:

@article{radford2022whisper,
  title   = {Robust Speech Recognition via Large-Scale Weak Supervision},
  author  = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg
             and McLeavey, Christine and Sutskever, Ilya},
  journal = {arXiv preprint arXiv:2212.04356},
  year    = {2022}
}