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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
ASR/STT subtitle generator. Uses Qwen3-ASR, local LLM, Whisper, TEN-VAD. Noise-robust for JAV
| Date | Stars |
|---|---|
| 2026-07-24 | 2046 |
| 2026-07-25 | 2051 |
| 2026-07-28 | 2059 |
| 2026-07-30 | 2066 |
| 2026-07-31 | 2067 |
| 2026-08-06 | 2067 |
Today
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Momentum
1.0
growth rate 0.05%/day
# WhisperJAV
<p align="center">
<a href="https://colab.research.google.com/github/meizhong986/WhisperJAV/blob/main/notebook/WhisperJAV_colab_edition_expert.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
<a href="https://kaggle.com/kernels/welcome?src=https://github.com/meizhong986/WhisperJAV/blob/main/notebook/WhisperJAV_kaggle_parallel_edition.ipynb">
<img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"/>
</a>
<a href="https://buymeacoffee.com/meizhong">
<img src="https://img.shields.io/badge/Buy%20Me%20a%20Coffee-ffdd00?style=flat&logo=buy-me-a-coffee&logoColor=black" alt="Buy Me a Coffee">
</a>
</p>
A subtitle generator for Japanese Adult Videos.
**Documentation:** [English](https://meizhong986.github.io/WhisperJAV/) | [简体中文](https://meizhong986.github.io/WhisperJAV/zh/)
---
### What is the idea:
Transformer-based ASR architectures like Whisper suffer significant performance degradation when applied to the **spontaneous and noisy domain of JAV**. This degradation is driven by specific acoustic and temporal characteristics that defy the statistical distributions of standard training data.
#### 1. The Acoustic Profile
JAV audio is defined by "acoustic hell" and a low Signal-to-Noise Ratio (SNR), characterized by:
* **Non-Verbal Vocalisations (NVVs):** A high density of physiological sounds (heavy breathing, gasps, sighs) and "obscene sounds" that lack clear harmonic structure.
* **Spectral Mimicry:** These vocalizations often possess "curve-like spectrum features" that mimic the formants of fricative consonants or Japanese syllables (e.g., *fu*), acting as accidental adversarial examples that trick the model into recognizing words where none exist.
* **Extreme Dynamics:** Volatile shifts in audio intensity, ranging from faint whispers (*sasayaki*) to high-decibel screams, which confuse standard gain control and attention mechanisms.
* **Linguistic Variance:** The prevalence of theatrical onomatopoeia and *Role Language* (*Yakuwarigo*) containing exaggerated intonations and slang absent from standard corpora.
#### 2. Temporal Drift and Hallucination
While standard ASR models are typically trained on short, curated clips, JAV content comprises long-form media often exceeding 120 minutes. Research indicates that processing such extended inputs causes **contextual drift** and error accumulation. Specifically, extended periods of "ambiguous audio" (silence or rhythmic breathing) cause the Transformer's attention mechanism to collapse, triggering repetitive **hallucination loops** where the model generates unrelated text to fill the acoustic void.
#### 3. The Pre-processing Paradox & Fine-Tuning Risks
Standard audio engineering intuition—such as aggressive denoising or vocal separation—often fails in this domain. Because Whisper relies on specific **log-Mel spectrogram** features, generic normalization tools can inadvertently strip high-frequency transients essential for distinguishing consonants, resulting in "domain shift" and erroneous transcriptions. Consequently, audio processing requires a "surgical," multi-stage approach (like VAD clamping) rather than blanket filtering.
Furthermore, while fine-tuning models on domain-specific data can be effective, it presents a high risk of **overfitting**. Due to the scarcity of high-quality, ethically sourced JAV datasets, fine-tuned models often become brittle, losing their generalization capabilities and leading to inconsistent "hit or miss" quality outputs.
**WhisperJAV** is an attempt to address above failure points. The inference pipelines do:
1. **Acoustic Filtering:** Deploys **scene-based segmentation** and VAD clamping under the hypothesis that distinct scenes possess uniform acoustic characteristics, ensuring the model processes coherent audio environments rather than mixed streams [1-3].
2. **Linguistic Adaptation:** Normalizes **domain-Excerpt of 27,547 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fcb81d706b895525, topic:whisper, topic:speech-to-text
matched fp:fcb81d706b895525, topic:llm