WhisperJAV

A subtitle generator for Japanese Adult Videos. Free, runs on your own machine, no cloud upload of your media.

Documentation: English | 简体中文

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The idea

Speech recognition models like Whisper are trained on clean, curated speech. JAV audio is the opposite of that, and the mismatch breaks them in specific, well-understood ways:

1. The acoustic profile. JAV audio has a low signal-to-noise ratio and a high density of non-verbal vocalisations — breathing, gasps, moans — whose spectra often mimic real Japanese syllables (e.g. fu), tricking the model into hearing words where none exist. Add extreme volume swings (whispers to screams) and theatrical role language (yakuwarigo) absent from training corpora, and the model's assumptions stop holding.

2. Long-form drift and hallucination. These are feature-length recordings, not 30-second clips. Over long stretches of ambiguous audio — silence, rhythmic breathing — the model's attention collapses and it fills the void with repeated or invented text. This is a documented failure mode of Whisper-family models [3, 4, 5].

3. The pre-processing paradox. Intuition says "denoise first". In practice, blanket denoising and vocal isolation can strip the high-frequency detail the model needs to tell consonants apart, making things worse. Fine-tuning on JAV data has its own trap: good datasets are scarce, so fine-tuned models tend to overfit and become hit-or-miss.

WhisperJAV is built around these three failure points rather than around any single model:

• Scene-based segmentation — split the audio at natural acoustic boundaries so the model always processes a coherent environment, never a mixed stream [1, 2].
• VAD clamping — detect where speech actually is, and feed the model only that, with measured padding. This is the main defence against hallucination on non-speech.
• Defensive decoding and post-processing — tuned confidence thresholds discard low-quality output, and Japanese-aware filters clean what remains.

None of this is magic; it is careful plumbing around known model weaknesses, and the defaults are tuned against ground-truth benchmarks. Results still vary with source audio quality.

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Quick start

GUI (recommended): launch WhisperJAV from the desktop shortcut (Windows installer) or run:

whisperjav-gui

Add files, pick a mode, click Start. Subtitles land next to your video as .srt.

Command line:

whisperjav video.mp4                                        # defaults
whisperjav video.mp4 --mode balanced --sensitivity aggressive
whisperjav /path/to/folder --output-dir ./subtitles         # whole folder

Any input FFmpeg can read works: MP4, MKV, AVI, WMV, MP3, WAV, FLAC, and so on. Output is SRT (default), WebVTT, or both (--output-format both).

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How a video becomes subtitles

Every pipeline follows the same overall shape; modes differ in which components they use and how aggressively they are tuned.

flowchart LR
    A[Audio<br/>extraction] --> B[Scene<br/>detection]
    B --> C[Speech<br/>enhancement<br/><i>optional</i>]
    C --> D[Speech<br/>segmentation<br/>VAD]
    D --> E[ASR<br/>model]
    E --> F[Post-<br/>processing]
    F --> G[.srt]

• Scene detection uses the media characteristics to cut by predicted scenes. This allows the downstream VAD and ASR to receive chunks which have similar DNA.
• Speech enhancement (off by default) can clean audio per-scene — used surgically, per the pre-processing paradox above.
• Speech segmentation (VAD) finds where speech actually is inside each scene. This choice matters more than most settings: it decides what the model hears and, in the modern pipelines, where your subtitle timestamps come from.
• The ASR model turns speech into text.
• Post-processing is the Japanese-specific cleanup pass:
  • Sentence regrouping aware of ending particles (ね, よ, わ, の), aizuchi (うん, はい), and dialect patterns (Kansai-ben and others)
  • Hallucination and repetition removal
  • Sound-only line removal — subtitle lines that are purely moans/breathing kana are dropped (real dialogue is protected by an evidence check)
  • Timing repair — a subtitle whose duration is absurdly long for its text gets its start pulled in (the end stays put); the console reports how many lines were retimed
  • Scene-boundary overlap resolution

Each stage has several interchangeable providers — the full menu, with strengths and weaknesses, is in Mix-and-match strategies below.

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Processing modes

Mode	Engine	Character
balanced	Faster-Whisper	Default. Full pipeline; good speed/accuracy balance
fidelity	OpenAI Whisper	Slowest, most thorough of the classic pipelines
fast	OpenAI Whisper + scene detection	General use, mixed-quality audio
faster	Faster-Whisper, minimal preprocessing	Speed first, clean audio
qwen (ChronosJAV)	Qwen3-ASR	Modern text-first recognizer
anime-whisper (ChronosJAV)	anime-whisper	Anime/JAV-tuned dialogue
transformers	HuggingFace	Kotoba and other HF Whisper models
parakeet, cohere, reazonspeech, kotoba, whisper-turbo, whisper-large, MOSS (*-crispasr, Ensemble passes)	CrispASR (GGUF models)	Japanese; experimental (parakeet and cohere since 1.10.0, the others new in 1.10.1)

Sensitivity applies to every mode: conservative (fewer false positives, good for noisy content) · balanced · aggressive (catches more quiet dialogue; good for whisper/ASMR content — and the tuning target of most of our benchmark work).

This is the beginning of the README. Read the rest at the original. Read the full README ↗