GPU-accelerated subtitle generation for Windows.
Transcribe video to SRT using OpenAI Whisper, translate any-to-any via Google Translate, edit cues in a built-in player.

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What it does

GenSRT generates SRT subtitle files from video using GPU-accelerated speech recognition, with built-in editing and any-to-any translation. The target use cases are serious subtitle work — content creators, fan subtitlers, accessibility teams, researchers working with non-English media.

• Transcribe. Drop a video file, pick a language (or auto-detect), generate an SRT. Built-in support for OpenAI Whisper sizes (tiny through large-v3-turbo) plus any HuggingFace-compatible faster-whisper model — including community fine-tunes for specific languages.
• Translate. Translate the generated SRT to any of 100+ languages via Google Translate. Translation happens cue-by-cue, preserving original timestamps. MyMemory fallback for offline scenarios or rate-limit cases.
• Edit. Built-in player with live subtitle display. Split, merge, delete, and edit cues with immediate feedback in the player. Save to disk as SRT and WebVTT in one operation.

_{Screenshot: Malayalam audio from an Asianet News clip on the US–Iran truce, transcribed with smcproject/vegam-whisper-medium-ml-int8_float16 using v1.2's chunked inference and translated to English. The "Circ Circ Circ…" row in the cue list is a known fine-tuned-Whisper hallucination GenSRT displays as-is — see Known Limitations.}

What's new in v1.2

Chunked inference for fine-tuned Whisper models. Community fine-tunes like SMC's vegam-whisper-medium-ml (for Malayalam) were practically unusable on long-form audio — they would transcribe the first 6-8 seconds and silently drop the rest. v1.2 solves this with silent-boundary chunked inference: audio is sliced along naturally-detected pauses, each chunk is transcribed independently, and the results are assembled into a single SRT with original timestamps preserved.

For Malayalam users with vegam, this produces 2-3× more transcribed content than running the same model without chunking. The chunked path engages automatically — no configuration required.

See the v1.2 release notes for the full changelog.

Quick start

1. Download gensrt-install.exe from the latest release and run it. It's a 7z self-extracting installer — pick a folder, and it'll unpack GenSRT there.
2. Run gensrt.exe from the install folder. The GUI opens.
3. Drop a video file onto the player. The Model selector in the footer defaults to large-v3-turbo (works well for English and most European languages).
4. Click Generate SRT. The model auto-downloads on first use (~1-2 GB depending on the model), then transcription begins.
5. When done, the right-pane cue list populates and subtitles display in the in-player overlay during playback.

First-run note: the first time you generate an SRT with a given model, the model auto-downloads to your HuggingFace cache (~1-2 GB). The download is one-time. On CPU-only machines, transcription itself runs to completion but takes substantially longer than on a CUDA GPU — see Requirements for typical timings.

For Malayalam: select smcproject/vegam-whisper-medium-ml-int8_float16 from the Model dropdown. v1.2's chunked inference runs automatically.

For other Indic or less-common languages: start with large-v3-turbo. If results are poor, search HuggingFace for community fine-tunes and add the repo path via New… in the Model dropdown.

Requirements

• Windows 10 or Windows 11
• Recommended: NVIDIA GPU with CUDA support (~2 GB VRAM is enough for vegam; ~4 GB for large-v3-turbo)
• Also works on CPU (Intel/AMD, including integrated graphics like Intel Arc) — GenSRT falls back automatically when no CUDA GPU is detected
• Internet connection for first-run model download

A note on speed

GPU vs. CPU is a substantial difference for Whisper-class models — observed timings on a 4.5-minute Malayalam news clip using smcproject/vegam-whisper-medium-ml-int8_float16:

Hardware	Time to transcribe
NVIDIA RTX 3060 Ti (CUDA)	~7-8 minutes
Intel Arc 140V iGPU (CPU mode, no CUDA)	~24 minutes

CPU mode runs to completion and produces comparable output quality — it just takes longer. If your machine doesn't have an NVIDIA GPU, GenSRT will still work; plan for the wait.

Features

• Plug-in any HuggingFace Whisper model — add custom faster-whisper-compatible models via the GUI's Model selector or the --model CLI argument.
• WebVTT alongside SRT — every generation writes both .srt and .vtt so the output works in HTML5 <video> elements natively.
• Live in-player subtitle display while editing — Split, Merge, Delete, and text edits show in the player immediately.
• Burn-in subtitles — bake subtitles into a copy of the video with one click; runs in the background.
• Bundled ffmpeg — no separate install required on target machines.
• Any-to-any translation — Korean → Malayalam, Japanese → Tamil, Spanish → Hindi, all supported via Google Translate.
• Plex / Jellyfin / Kodi compatible filename suffixes for SRT output.
• Self-contained user_guide.html shipped alongside the executable.

Documentation

• User guide: user_guide.html shipped alongside the executable, covering full GUI workflow and CLI usage.
• Architecture and decisions: docs/V12_PLAN.md — release plan, scope decisions, architectural choices.
• Investigation history: docs/INVESTIGATIONS.md — technical investigations, including evaluation of alternative ASR engines (IndicConformer) and forced-alignment approaches (wav2vec2).

Known limitations

• Fine-tuned Whisper models like vegam occasionally emit a phrase from earlier in the audio at chunk tails. Visible as substring overlap with the previous cue. A cleanup post-processor is candidate work for v1.3.
• Whisper's tokenizer can stop generating mid-character on Indic scripts; a � at the end of a subtitle line is GenSRT signaling this honestly rather than masking it. The text before the � is accurate.
• Built-in Whisper models struggle with fast, dense speech in Indian languages (news broadcasts with English code-switching). Use a fine-tuned model where available; verify against audio before publishing where one isn't.

See the user guide's "Known Limitations" section for the complete list.

Acknowledgments

GenSRT's chunked inference path was developed against vegam-whisper-medium-ml from Swathanthra Malayalam Computing (SMC). Kavya Manohar, Leena G Pillai, and Elizabeth Sherly's analysis of Indic-script ASR evaluation pitfalls (arxiv 2409.02449) shaped how we think about quality measurement for these models. AI4Bharat's OIWER benchmark (arxiv 2603.00941) provides the most rigorous published Malayalam ASR comparison.

Built with

OpenAI Whisper · faster-whisper · CTranslate2 · Flask · pywebview · ffmpeg

License

GenSRT is licensed under the GNU Affero General Public License v3.0 — see LICENSE. You can redistribute and modify GenSRT under AGPL terms. If you build derivative works or run modified versions as a network-accessible service, the AGPL terms apply, including the obligation to make source code of your modified version available to its users.

For commercial licensing options (e.g. proprietary integration), contact the maintainer.