mrkeyoor.com_
Wed 16 Sept 23:53 UTC
AI Toolsevaluationupdated 26 Aug 2026

voice-pro review

Voice-Pro is a Gradio application for turning video and audio into transcripts, translations, subtitles, separated vocals, and generated speech. It combines Whisper variants, translation services, several text-to-speech engines, YouTube downloading, and zero-shot voice cloning in one creator-facing interface.

+53stars / 7d
Verdict

Our Voice-Pro install used 7,192 MB for 310 packages, and pip-audit found 7 known vulnerabilities before any test target was available. Try it only on a dedicated Windows and NVIDIA workstation where its all-in-one dubbing workflow saves enough time to justify the dependency and model footprint. Keep it away from unattended production until the advisories, update pause, consent policy, and absence of tests are addressed.

We ran it

Lab card: what happened when we ran voice-proScreenshot of voice-pro (www.wctokyoseoul.com)
Install✓ · 178s310 packages · 7192 MB
Build✓ · 8s
Testsn/ano test script
Known vulns7(pip-audit)
Repo507 files~45,677 lines of source · 44.8 MB · 0 CI workflows

Answers from our run

Does voice-pro build from source?

Dependencies installed in 178 seconds (310 packages), and the build succeeded in 8 seconds. We cloned commit 7231384 into a clean Debian container with 3 CPUs and no project-specific setup.

Does voice-pro have tests you can run?

Not through a standard command: the project exposes no test script or target that our harness could run.

Does voice-pro have known vulnerabilities in its dependencies?

pip-audit flagged 7 known advisories in the dependency tree at the time of our run.

Who should not use voice-pro?

Mac or Linux teams needing verified support: the README lists launch scripts for both but plainly says operation on those systems has not been verified.

What are the alternatives to voice-pro?

Whisper WebUI, pyVideoTrans, F5-TTS. Our Voice-Pro install used 7,192 MB for 310 packages, and pip-audit found 7 known vulnerabilities before any test target was available.

Setup2/5178-second install consumed 7,192 MB before model downloads
Docs4/5Feature and Windows setup detail is strong; platform claims conflict
Community3/512,647 stars and issue activity, while maintainer updates are paused
Maturity2/5No tests or CI, plus 7 audited vulnerabilities and paused updates

Discussed on

  1. hnShow HN: Voice-Pro – AI Voice Cloning271 points
  2. hnShow HN: Voice Cloning and Multilingual TTS in One Click (Windows)9 points
  3. hnShow HN: Voice-Pro – Now More Powerful and Easier to Use3 points

Who it’s for

Windows creators with an NVIDIA GPU who want one local interface for transcription, translation, and dubbing.
Video teams willing to compare Whisper, Edge-TTS, F5-TTS, CosyVoice, and Kokoro output themselves.
Researchers who need a visual workbench for subtitle and speech experiments.
Users prepared to manage large model downloads, media storage, and voice-consent rules.

Who it’s NOT for

Mac or Linux teams needing verified support: the README lists launch scripts for both but plainly says operation on those systems has not been verified.
Security-sensitive users who need a clean dependency audit: our installed environment reported 7 known vulnerabilities.
Small machines or disposable containers: our base install alone occupied 7,192 MB before first-use model downloads.
Production teams that require automated regression coverage: the repository exposed no test script or target to our harness and had no CI workflows.
Anyone cloning a person's voice without documented permission: the project advertises zero-shot cloning and publishes a celebrity reference-voice catalog, leaving consent and usage rights to the operator.
Users expecting regular fixes: the README says development and updates are paused because the team is focused on another product.

Setup reality

Our Python install succeeded in 178 seconds, adding 310 packages and occupying 7,192 MB. The build passed in 8 seconds. The repository exposed no test script or target, so tests were skipped. Pip-audit reported 7 known vulnerabilities.

The v4 Windows path uses start.bat to obtain uv, Python 3.12, packages, ffmpeg when needed, and later AI models. Free translation and Edge-TTS work without Azure credentials; Azure Translator and Speech require keys in .env.

The 44.8 MB checkout held 507 files and about 45,677 source lines, with no CI workflows, Dockerfile, or root tests directory. NVIDIA GPU use is the documented strong path, with 4 GB VRAM minimum and 8 GB preferred. Mac and Linux operation is unverified.

One Gradio app joins transcription, translation, and dubbing

Voice-Pro puts a long media-processing chain behind browser tabs. It can download YouTube media, separate vocals with Demucs, transcribe through Whisper implementations, translate text, generate subtitles, and synthesize replacement speech. F5-TTS, E2-TTS, and CosyVoice cover cloning paths, while Edge-TTS and Kokoro offer other speech choices. The result is attractive for creators who would otherwise pass files between several command-line tools and align outputs by hand.

The breadth also makes failures harder to isolate. A dubbed video may depend on yt-dlp, ffmpeg, a separator, an ASR model, a translation endpoint, a TTS engine, and Gradio state. Open pull request 103 describes Demucs failures surfacing later as missing ffmpeg inputs, which produced a misleading error. Version 4 adds persistent error toasts, but an operator still needs to know which stage produced a bad subtitle, missing stem, or mistimed replacement track.

The 7,192 MB install is larger than many complete creator apps

Our fresh Debian sandbox installed 310 Python packages in 178 seconds and occupied 7,192 MB. The build then passed in 8 seconds. That measured environment is unusually large for a web UI and reflects the number of speech, media, and machine-learning components pulled together. The repository itself was only 44.8 MB, with 507 files and around 45,677 source lines.

First use still obtains selected AI models, so the base dependency result is not the final storage requirement. The v4 release moved setup to uv with a committed lockfile and uses Python 3.12, Torch 2.8.0 with CUDA 12.8, and Gradio 6.20. On Windows, start.bat can download a portable ffmpeg and does not require the CUDA Toolkit or Visual Studio Build Tools. Those improvements reduce manual prerequisites without making the installation small.

What happened when we ran it

Our run confirmed that the Python install completed in 178 seconds and the build completed in 8 seconds. The harness found no test script or target, so it skipped tests. We did not launch the Gradio interface, transcribe media, clone a voice, translate subtitles, or render a dubbed video. No such outcome should be inferred from the passing build.

Pip-audit found 7 known vulnerabilities in the 310-package environment. The supplied measurement does not list advisory identifiers or severity, so we cannot say which dependency or feature path is affected. The correct next step is to reproduce the audit with the locked v4 environment, map each advisory to a loaded component, and upgrade or isolate it. Shipping the app with an unexplained 7-advisory result would be a poor trade for setup convenience.

Windows with NVIDIA is the only platform the README endorses confidently

The requirements section lists Windows 10 or 11, Linux, and Apple Silicon Mac. A nearby notice says Voice-Pro works well on Windows with NVIDIA and that Mac and Linux operation has not been verified. Earlier history also claimed all three platforms. These statements conflict, so a buyer should use the narrower current warning: Windows is the supported trial path, while start.sh on other systems is an experiment.

GPU guidance asks for a recent NVIDIA driver, at least 4 GB of VRAM, and preferably 8 GB or more. CPU mode can be selected, but issue 85 records repeated dependency failures on Windows 11 with an AMD GPU using older releases. It does show why testing the exact GPU mode, ASR model, TTS engine, and video length matters.

Free services avoid keys but add network dependence

By default, translation uses Deep-Translator with Google's free web endpoint, and speech synthesis can use Edge-TTS. Azure Translator and Azure Speech are optional and require keys in .env. The README warns that corporate security appliances may rate-limit or block the free translation endpoint. Voice-Pro retries and preserves original text for failed lines, while Azure is the documented route around that network behavior.

A local UI therefore does not guarantee a fully local workflow. YouTube download, free translation, Edge-TTS, Azure, and model repositories all introduce external services at different points. Decide which media may leave the workstation, choose local model paths where needed, and test a blocked-network scenario. Keep .env out of version control as the README instructs, especially when workspaces contain client media.

Voice cloning needs consent controls the app does not supply

The README advertises zero-shot cloning and includes a large reference catalog named after public figures. A technical ability to generate similar speech does not establish permission to use a recording, imitate a person, or distribute the result. Voice-Pro's interface cannot make that decision for a creator. A workplace deployment needs an intake record for the source recording, consent, allowed uses, retention, and review before publication.

The same caution applies to downloaded video and translated subtitles. Keep source attribution, verify the right to download and modify media, and have a speaker review the final language when accuracy carries consequences. ASR and translation can return fluent text that is still wrong. Since our run never processed audio and the project supplies no automated test target, quality checks belong in the production workflow rather than in a hopeful reading of the feature list.

Version 4 arrived before an explicit development pause

GitHub showed 12,647 stars, 59 combined open issues and pull requests, and a last push on July 13, 2026. Release v4.0.0 was published the same day. The README also says Voice-Pro development and updates are unavailable for the time being because of work on WeConnect. Issue comments continued into August, but user discussion is not the same as maintainer releases or merged fixes.

Licensing needs one more check before redistribution. GitHub detects GPL-3.0 and the repository's LICENSE contains GPL version 3, while the README's metadata comment says LGPL. Treat the actual license file as the operative repository signal and ask counsel if bundled models, sample voices, or third-party media carry separate terms. The combination of paused updates, 7 advisories, no tests, and a 7,192 MB install makes Voice-Pro a capable workstation experiment rather than a dependable platform.

Alternatives

ProjectWhat it isPick it when
Whisper WebUIA browser interface focused on Whisper transcription and subtitle generation.pick this instead when transcription is the main job and Voice-Pro's dubbing stack is unnecessary.
pyVideoTransA desktop-oriented video translation and dubbing toolbox with multiple providers.pick this instead when video translation workflow and provider choice matter more than Voice-Pro's bundled voice lab.
F5-TTSThe focused speech-generation project behind one of Voice-Pro's cloning options.pick this instead when you need to build around one speech model and can supply your own transcription and media pipeline.

What people are saying

  1. [github-trending] abus-aikorea/voice-pro

Sources

  1. Voice-Pro README
  2. Voice-Pro v4.0.0 release
  3. Issue 90: Linux installation failures
  4. Issue 85: Windows CPU-mode installation failures
  5. Voice-Pro GPL-3.0 license
  6. Voice-Pro repository metadata

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