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

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.

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).

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.

Processing modes

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).

ChronosJAV

Some of the best recognizers for this domain (anime-whisper, Qwen3-ASR and its Japanese finetunes) don't produce reliable timestamps on their own. ChronosJAV runs text generation and timing as separate stages: the VAD provides the time skeleton, the model provides the words. Since v1.9, timestamps come from the VAD frames by default (no aligner model loaded, ~1 GB less VRAM); a Qwen forced-aligner mode remains available in the settings for word-level alignment.

The same decoupled design is why new models can be added without rebuilding the pipeline — anything that turns audio into text can be slotted in.

Two-pass ensemble

Different pipelines miss different lines. Ensemble mode runs your file through two pipelines and merges the results.

The v1.9 default pairing: pass 1 = anime-whisper with WhisperSeg VAD, pass 2 = Qwen3-ASR with TEN VAD — two different recognizers and two different VADs, so their blind spots don't overlap.

whisperjav video.mp4 --ensemble \
    --pass1-pipeline qwen --pass2-pipeline balanced \
    --merge-strategy pass1_primary

• Merge strategies: pass1_primary / pass2_primary (one pass leads, the other fills gaps), smart_merge, full_merge, pass1_overlap / pass2_overlap, longest
• Presets: save and reload named ensemble configurations from the GUI
• Serial mode (--ensemble-serial): finish each file completely before starting the next, so results appear as they're done
• Bring your own pass 2: PurfView's Faster-Whisper XXL (--pass2-pipeline xxl --xxl-exe ...) or an external CrispASR build

Mix-and-match strategies

The Ensemble tab is a mixing desk. Each pass is a free combination of five choices — pipeline × scene detection × audio pre-processing × speech segmentation × ASR model — and the two-pass design is the sixth dimension. The defaults are benchmark-tuned, so you never have to touch any of this; but audio varies a lot, and one deliberate substitution is often worth the experiment. The golden rule: change one thing at a time, so you know what caused the difference.

Pipeline

The recipe that ties the other choices together.

Naming note (CLI users). ChronosJAV is one pipeline with interchangeable recognizers, so qwen and anime-whisper are two backends of it rather than two separate pipelines. The GUI lists them side by side in the pipeline dropdown; on the command line both live under qwen, and the backend is chosen inside the params:

# Qwen3-ASR (the default backend)
--pass1-pipeline qwen

# anime-whisper, same pipeline, different recognizer
--pass1-pipeline qwen --pass1-qwen-params '{"generator_backend": "anime-whisper"}'

The usable values for generator_backend are qwen3 and anime-whisper (a third, cohere, exists in the code but is not currently enabled). Note also that litagin/anime-whisper can be loaded through the transformers pipeline via --hf-model-id — but that route uses HF's own chunking and does not produce usable per-line timing, so prefer the ChronosJAV route above.

Scene detection

Where the long file gets cut into workable pieces.

Audio pre-processing (speech enhancement)

Off by default — remember the pre-processing paradox. The "Enhance for VAD only" checkbox is the safest way to use these: the cleaned audio guides speech detection while the model still hears the original.

Speech segmentation (VAD)

Decides what the model hears — and in ChronosJAV pipelines, where your timestamps come from. Probably the highest-leverage swap on this list.

ASR engine and model

The two-pass dimension

Everything above multiplies: two passes means two full recipes, then a merge. What makes a good pair is diversity — different recognizers and different VADs, so the passes fail in different places and the merge covers both.

A few known-good recipes:

Merge strategies. Seven are available (--merge-strategy):

Save anything that works as a preset so it's one click next time.

AI translation

Generate and translate in one go, or translate subtitles you already have:

whisperjav video.mp4 --translate                      # transcribe + translate
whisperjav-translate -i subtitles.srt --provider ollama

Interrupted translations resume where they left off — just run the same command again.

The GUI

Four tabs:

1. Transcribe — files, mode, sensitivity, language
2. Advanced options — output format, scene detection, model override, debug
3. Ensemble — the two-pass grid: per-pass pipeline, sensitivity, scene detector, enhancer, VAD, and model, plus a Customize dialog exposing each backend's tunable parameters, and preset save/load
4. AI SRT Translate — translate existing subtitle files

Sensible defaults everywhere: if you never open a Customize dialog, you get the benchmark-tuned configuration.

Which mode for which content

These are starting points, not rules — see Mix-and-match strategies for how to adapt them.

Installation

Already installed? Upgrade with whisperjav-upgrade (all platforms). Rollback is available: whisperjav-upgrade --rollback.

Windows — standalone installer (recommended)

No Python knowledge needed.

1. Download the .exe from Releases
2. Run it — no admin rights required (installs to %LOCALAPPDATA%\WhisperJAV)
3. Wait 10–20 minutes while it sets up Python, PyTorch, FFmpeg and dependencies. It detects your NVIDIA driver and installs the matching CUDA build automatically (or CPU-only if no GPU)
4. Launch from the desktop shortcut. First transcription downloads models (~3 GB)

Google Colab / Kaggle

No local install at all — use the badges at the top of this page. Maintained notebooks for both platforms.

git clone https://github.com/meizhong986/whisperjav.git
cd whisperjav
installer\install_windows.bat            :: auto-detects GPU
installer\install_windows.bat --cpu-only :: or force CPU

xcode-select --install
brew install python@3.12 ffmpeg portaudio git

git clone https://github.com/meizhong986/whisperjav.git
cd whisperjav
python3 -m venv ~/venvs/whisperjav && source ~/venvs/whisperjav/bin/activate
chmod +x installer/install_mac.sh && ./installer/install_mac.sh

sudo apt-get install -y python3 python3-pip python3-venv python3-dev \
    build-essential ffmpeg git libsndfile1 libsndfile1-dev

git clone https://github.com/meizhong986/whisperjav.git
cd whisperjav
chmod +x installer/install_linux.sh && ./installer/install_linux.sh

pip install torch torchaudio --index-url https://download.pytorch.org/whl/cu128  # NVIDIA
pip install torch torchaudio                                                     # Apple Silicon
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu    # CPU

pip install "whisperjav[all] @ git+https://github.com/meizhong986/whisperjav.git"

System requirements

Rough speed per hour of video: RTX GPU 5–10 min · Apple Silicon 8–15 min · CPU 30–60 min.

Troubleshooting

• "FFmpeg not found" — install FFmpeg and add it to PATH.
• Very slow, GPU warning in log — your PyTorch is CPU-only. Reinstall it with the CUDA index URL shown above.
• model.bin error in faster mode — enable Windows Developer Mode (or run once as admin), then delete the cached model folder under %USERPROFILE%\.cache\huggingface\hub.
• Anything else: open a GitHub issue with your system info and the console log. Logs and reproduction details make fixes much faster.

Contributing

Contributions are welcome — see CONTRIBUTING.md. Development setup:

git clone https://github.com/meizhong986/whisperjav.git
cd whisperjav
pip install -e ".[dev]"
python -m pytest tests/

License

MIT. See LICENSE.

References

1. Chen, Y., et al. (2025). "ChronusOmni: Improving Time Awareness of Omni Large Language Models." arXiv:2512.09841. (Inspiration for the ChronosJAV pipeline)
2. Bain, M., et al. (2023). "WhisperX: Time-Accurate Speech Transcription of Long-Form Audio." arXiv:2303.00747.
3. Wang, Y., et al. (2025). "Calm-Whisper: Reduce Whisper Hallucination On Non-Speech By Calming Crazy Heads Down." Interspeech 2025. arXiv:2505.12969.
4. Barański, M., et al. (2025). "Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio." arXiv:2501.11378.
5. Koenecke, A., et al. (2024). "Careless Whisper: Speech-to-Text Hallucination Harms." ACM FAccT 2024.
6. Roll, N., et al. (2025). "In-Context Learning Boosts Speech Recognition via Human-like Adaptation to Speakers and Language Varieties." arXiv:2505.14887.
7. Yang, X., et al. (2024). "PromptASR for Contextualized ASR with Controllable Style." ICASSP 2024. arXiv:2309.07414.

Acknowledgments

Built on the shoulders of: OpenAI Whisper · faster-whisper · stable-ts · Qwen3-ASR · anime-whisper · Kotoba-Whisper · HuggingFace Transformers · PySubtrans — and the testing community, whose feedback and bug reports shape every release.

Disclaimer

This tool generates accessibility subtitles. Users are responsible for compliance with applicable laws regarding the content they process.