WhisperJAV

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).

ChronosJAV

Some of the best ASR models for this material (anime-whisper, Qwen3-ASR and its Japanese fine-tunes) do not give reliable times on their own. ChronosJAV therefore takes the text from the ASR model and the times from a separate step. By default each subtitle takes the start and end of the piece of speech (VAD group) the speech segmenter found, and no aligner is loaded — as in 1.9.x, and again since 1.10.1 (1.10.0 timed the text with an aligner and a timing model by default). Both remain choices in the settings: an aligner mode (CrispASR's aligners such as wav2vec2-ja, or the PyTorch Qwen3 forced aligner) and the Whisper small timing model.

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.

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Two-pass ensemble

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

The v1.9.5 default pairing: pass 1 = Qwen3-ASR with FireRedVAD, pass 2 = anime-whisper with TEN VAD, both on Aggressive — two different recognizers and two different VADs, so their blind spots don't overlap. In the GUI, pass 2 runs once you tick it.

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 ("Finish each file", --ensemble-serial, on by default since 1.10.1; --no-ensemble-serial turns it off): finish each file completely before starting the next, so results appear as they're done
• Bring your own pass 1, pass 2 or both: PurfView's Faster-Whisper XXL (--pass1-pipeline xxl and/or --pass2-pipeline xxl, with --xxl-exe ...; the model with --pass1-model / --pass2-model, default large-v2)

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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.

Pipeline	Strength	Watch out
balanced	The workhorse: full pipeline, good speed/accuracy, every component swappable	Jack of all trades — specialists beat it on their home turf
fidelity	Most thorough classic pipeline; strong on quiet/ASMR content	Slowest option
fast	Decent middle ground on mixed-quality audio	Fewer defences than balanced
faster	Speed; fine for clean, dialogue-forward audio	Minimal preprocessing = less hallucination protection
qwen (ChronosJAV)	Modern text-first recognizer; robust on messy audio	Timestamps come from the VAD, so the segmenter choice matters doubly
anime-whisper (ChronosJAV)	Best-in-class on anime-style/JAV dialogue; heavily benchmark-tuned here	Can miss very faint, isolated utterances
transformers	Runs any HF Whisper model; best GPU path on Apple Silicon	Uses HF's own chunking — scene/segmenter choices don't apply
The seven GGUF pipelines (*-crispasr)	GGUF models through CrispASR, installed with WhisperJAV; scene detection, enhancement and VAD work as for the other passes	Experimental; Japanese only
xxl	Bring your own external engine as a pass	Self-contained: WhisperJAV's knobs don't reach inside

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.

Method	Strength	Watch out
Semantic	Groups acoustically similar audio; best for full-length features. The default everywhere since v1.9.2 (it was auditok)	Occasionally cuts inside speech on very uniform audio; the flip has not been proven better on a feature-length film
Auditok	Energy-based: fast, simple, dependable. The pre-1.9.2 default — --scene-detection-method auditok	Constant background music can mask the pauses it needs
Silero	Neural; holds up on noisy audio	Slower than auditok
None	No cutting at all	Only sensible for short clips

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.

Backend	Strength	Watch out
none	No artefacts, no surprises — the right default	Won't rescue genuinely bad audio
ffmpeg-dsp	Transparent classic filters (loudnorm, denoise, compress…); loudnorm genuinely helps very quiet recordings	Aggressive settings dull consonants
zipenhancer	Lightweight neural denoise; good against hiss	16 kHz processing; can soften detail
htdemucs	Vocal isolation — separates voice from loud BGM	The biggest intervention of all; reserve for music-dominated content

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.

Not on Balanced, since v1.9.2. The Balanced pipeline uses Faster-Whisper's own voice detection and accepts none of the backends below — --speech-segmenter with --mode balanced stops the run with an error, as do --max-group-duration and --chunk-threshold. What you choose there instead is which Silero build that detection runs: --vad-version 3.1 | 4.0 | 6.2 (default 4.0), or the Speech Segmenter column of a Balanced pass in the Ensemble Mode tab. Every other pipeline takes the table below unchanged.

Backend	Strength	Watch out
WhisperSeg	Trained on Japanese ASMR-style audio; tuned against our ground truth; the JA default	Japanese-specialised — switch it for other languages
TEN VAD	Light and quick; good general performer; pass-2 default for diversity	Less JA-specialised than WhisperSeg
Silero v3.1 / v4.0	Solid general-purpose; the recommendation for non-Japanese audio	Tends to miss very quiet Japanese speech
Silero v6.2	Adds max-duration splitting and finer control	Same quiet-speech caveat
Faster-Whisper native (Balanced — and the only option there)	Fastest — one recognizer call per scene; pick the Silero build with --vad-version	Coarser timing than a dedicated VAD; no independent speech detector for the run-outcome check
FireRedVAD (Fidelity default)	Tiny multilingual model, cheap on CPU; lowest false-alarm rate of the bundled VADs; installed with WhisperJAV since v1.9.2	Its ~2 MB model downloads from Hugging Face once — a machine that cannot reach it stops the run and says so; --speech-segmenter silero-v3.1 needs no download
None	The model hears everything	Maximum hallucination exposure on non-speech

ASR engine and model

Model	Pipeline	Strength	Watch out
Whisper large-v2	classic	The most predictable performer on this domain — that's why it's the default	Not the newest
Whisper large-v3	classic	Newer training	More hallucination-prone on JAV audio
Whisper turbo	classic	Fastest Whisper	Some accuracy cost
whisper-ja-1.5B (CT2) (new)	balanced	JA finetune, word timestamps intact; strongest results in our scene-length benchmarks	Community model; occasional repetitions (our filters catch most)
anime-whisper	ChronosJAV	Excellent anime/JAV dialogue quality	No native timestamps — VAD-timed
Qwen3-ASR 1.7B / 0.6B	ChronosJAV	Robust on messy audio; 0.6B fits 4 GB VRAM	No native timestamps — VAD-timed
JA Anime-Galgame 1.7B (new)	ChronosJAV	Qwen finetune with published gains on anime speech (CER −27% rel.); recovers lines the base drops	Slightly more junk insertions (post-processing handles most)
JA-tuned 1.7B (neosophie) (new)	ChronosJAV	Qwen finetune aimed at proper nouns and kanji-heavy phrasing	No published benchmarks
Kotoba family	transformers	Japanese-optimized, light; bilingual variant; good on Apple Silicon	Smaller models — ceiling below the 1.5B+ class

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:

Goal	Pass 1	Pass 2	Merge
The v1.9.5 default	Qwen3-ASR · semantic · FireRedVAD · aggressive	anime-whisper · semantic · TEN · aggressive	pass1_primary
Classic + modern	balanced · large-v2	qwen (or the Anime-Galgame finetune)	pass1_primary
Quiet/ASMR recall	fidelity · aggressive	anime-whisper · aggressive	longest
Second opinion on the model only	your usual recipe	same recipe, different ASR model	pass1_primary

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

Strategy	What it does
pass1_primary	Keeps pass 1 as primary and fills missing parts from pass 2
pass2_primary	The mirror image — pass 2 primary, filled from pass 1
smart_merge	Automatically picks the better line from each pass
full_merge	Includes every line from both passes — most recall, most duplicates to clean up
longest	For each overlapping pair, picks the subtitle with more text content
pass1_overlap / pass2_overlap	Same as the _primary pair, but tolerates up to 30% overlap when filling — use when the two passes disagree slightly on boundaries

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

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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

Provider	Cost	Notes
Ollama	free, local	Recommended local option; auto-starts the server and picks a model for your VRAM
DeepSeek	cheap	Good quality/price for this content
Gemini	free tier
Claude / GPT / OpenRouter / GLM / Groq	paid API

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

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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.

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Which mode for which content

Content	Suggestion	Sensitivity
Dialogue-heavy drama	balanced	aggressive
Anime-style / clear JAV dialogue	anime-whisper	aggressive
ASMR / whispering / VR	fidelity or anime-whisper	aggressive
Heavy background music	balanced	conservative
Amateur / variable audio	fast	conservative
Group scenes	faster	conservative
Best possible result	ensemble (anime-whisper + qwen)	per-pass defaults

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

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Which pipeline for which computer

You choose your kind of computer once, when you install WhisperJAV.

Your computer	What uses the graphics card
NVIDIA graphics card	Every pipeline.
AMD or Intel graphics card, or an older NVIDIA card (Windows)	The GGUF pipelines (Ensemble tab, Pipeline menu groups "GGUF via crispasr" and "Experimental, open to feedback"), through Vulkan. Experimental, not yet tested. The other pipelines run on the processor, several times slower.
Mac with Apple silicon	transformers asks for the Apple GPU; the GGUF pipelines and fidelity ask for it too (not yet tested on a Mac). balanced, fast, faster and ChronosJAV run on the processor.
No graphics card	None: every pipeline runs on the processor, several times slower than on a graphics card.

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Installation

Already on 1.10? Windows installer: close WhisperJAV and run "%LOCALAPPDATA%\WhisperJAV\python.exe" -m whisperjav.upgrade in Command Prompt (or your install folder's python.exe), not whisperjav-upgrade, which can leave an install on another drive than Windows' temporary folder (normally C:) unable to start. It keeps the accelerator of your install. The GUI only tells you that a new version is out. If WhisperJAV no longer starts after an upgrade, uninstall it and install the newest release with the Windows installer; your settings and downloaded models stay. Source install: git fetch --tags, git checkout the new release tag, and run the installer again with the same Python environment active. On 1.9.x? Install 1.10 fresh: it needs Python 3.13, so whisperjav-upgrade cannot take 1.9.x to it.

Each install is made for one accelerator, chosen when you install: NVIDIA GPU (CUDA), GPU through Vulkan (AMD, Intel or older NVIDIA card, Windows), Apple GPU (Metal), your own PyTorch, or CPU only. See Accelerated or not.

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 10–30 minutes while it sets up Python, PyTorch, FFmpeg and dependencies. With a supported NVIDIA GPU (GTX 16 / RTX 20 or newer, driver 580+) it installs for the GPU by itself; otherwise a window asks what the install should be made for
4. Launch from the desktop shortcut. First transcription downloads models (~3 GB)

Google Colab / Kaggle

Not supported by 1.10, which is made and tested for installs on your own computer only. The Colab expert notebook (badge at the top) keeps installing 1.9.5.

Prerequisites: Python 3.13, Git, FFmpeg in PATH.

git clone --branch v1.10.1 https://github.com/meizhong986/whisperjav.git
cd whisperjav
installer\install_windows.bat                    :: a supported NVIDIA GPU is used; otherwise it asks
installer\install_windows.bat --accelerator cpu  :: or choose: nvidia, vulkan, own-pytorch, cpu

Full guide: docs/en/guides/installation_windows_python.md

xcode-select --install
brew install [email protected] ffmpeg portaudio git

git clone --branch v1.10.1 https://github.com/meizhong986/whisperjav.git
cd whisperjav
python3.13 -m venv ~/venvs/whisperjav && source ~/venvs/whisperjav/bin/activate
chmod +x installer/install_mac.sh && ./installer/install_mac.sh

Apple silicon, macOS 14 or newer; the install is made for the Apple GPU (Metal). The transformers pipeline uses the Apple GPU and is recommended on a Mac; fidelity and the GGUF pipelines ask for it (not yet tested on a Mac); balanced, fast, faster and ChronosJAV run on the CPU. Intel Macs are not supported by 1.10.

Full guide: docs/en/guides/installation_mac_apple_silicon.md

Install system packages first (Ubuntu example; see the guide for Fedora/Arch):

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

Then:

git clone --branch v1.10.1 https://github.com/meizhong986/whisperjav.git
cd whisperjav
chmod +x installer/install_linux.sh && ./installer/install_linux.sh

Linux with glibc 2.35 or newer (Ubuntu 22.04+, Debian 12+, Fedora 36+) and Python 3.13. For the GPU you need the NVIDIA driver (570+), but not the CUDA Toolkit — PyTorch bundles its own runtime. On distros with externally-managed Python (Ubuntu 24.04+), use a venv; the script detects this and tells you what to do.

The GUI needs a pywebview backend inside your environment — the distro's WebKit2GTK packages install system-wide, where a plain virtual environment cannot see them, which is why the GUI can still stop with ModuleNotFoundError: No module named 'gi'. Either pip install "pywebview[qt]" (the Qt backend, no system GTK bindings needed), or create the venv with python3 -m venv --system-site-packages so it can see the distro's bindings.

Full guide: docs/en/guides/installation_linux.md

Every 1.10 install gets the package versions its release was tested with: install.py installs them from the lock file with uv sync. To keep a PyTorch build you already have in your environment (for example ROCm for an AMD GPU), install with:

python install.py --accelerator own-pytorch

WhisperJAV then installs everything except PyTorch and never replaces it (experimental).

System requirements

	Minimum	Recommended
OS	Windows 10 (x86-64) / macOS 14 on Apple silicon / Linux with glibc 2.35 (Ubuntu 22.04)	Windows 11 / macOS 14 or newer / Ubuntu 24.04
Python (source installs)	3.13	3.13
RAM	8 GB	16 GB
Disk	8 GB	15 GB with models
GPU	none (CPU works, slowly)	NVIDIA GTX 16 / RTX 20 or newer, or Apple silicon

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

Without a supported NVIDIA GPU

Choose what the install is made for when you install — GPU through Vulkan (Windows: AMD, Intel or older NVIDIA card; the GGUF pipelines use it, experimental), your own PyTorch, or CPU only. Every run then follows that choice; nothing needs ticking. At the start of each run WhisperJAV says where each part runs. --device cpu runs one job on the CPU. Details: Accelerated or not.

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Troubleshooting

• The Windows installer stopped and said the installation failed. Since v1.9.2 it checks that what WhisperJAV needs to run actually imports, and stops rather than leaving you a shortcut to a broken install. It names what failed and writes INSTALLATION_FAILED_v<version>.txt next to install_log_v<version>.txt in the installation folder. Run the installer again first — the usual cause is a download that did not finish. If it fails twice, attach both files to an issue.
• "FFmpeg not found" — install FFmpeg and add it to PATH.
• Very slow — run whisperjav --check, or read the box at the start of a run: they say where each part runs. On a CPU-only install everything runs on the CPU, several times slower than on a GPU.
• 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.
• An empty or very short subtitle file, with the run reporting success. v1.9.2 fixes one cause of this on Balanced and now reports the outcome per file rather than always claiming success. If it still happens, that is worth a report.
• Anything else: open a GitHub issue with your system info and the console log. tools/whisperjav_env_report.py in this repository prints your package versions, GPU, driver and CUDA in one go — run it with the same Python that runs WhisperJAV (Windows users can right-click whisperjav_env_report.ps1). Logs and reproduction details make fixes much faster.

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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/

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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 · CrispASR — and the testing community, whose feedback and bug reports shape every release.

Special thanks to CrispASR, which runs the GGUF pipelines and ChronosJAV's aligner and timing model, for its rapid response and support.

Disclaimer

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