--- title: "whishper vs awesome-whisper" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pluja-whishper-vs-sindresorhus-awesome-whisper" tools: ["pluja-whishper", "sindresorhus-awesome-whisper"] --- # whishper vs awesome-whisper *GraphCanon updated Jul 30, 2026* ## Verdict Pick whishper if whishper is an open-source audio transcription and subtitling suite with a web UI that supports both GPU and CPU for local transcription; pick awesome-whisper if awesome-whisper is an organized repository aggregating resources for OpenAI's Whisper AI-powered speech recognition system, covering models, apps, bindings, packages, and community tools. [whishper](https://whishper-docs.pages.dev/) reports 3.0k GitHub stars, 179 forks, and 108 open issues, last pushed Aug 15, 2025. [awesome-whisper](https://github.com/sindresorhus/awesome-whisper) has 2.4k stars, 156 forks, and 7 open issues, last pushed Mar 17, 2026. Figures are from public GitHub metadata via [whishper's repository](https://github.com/pluja/whishper) and [awesome-whisper's repository](https://github.com/sindresorhus/awesome-whisper). | | [whishper](/tools/pluja-whishper.md) | [awesome-whisper](/tools/sindresorhus-awesome-whisper.md) | | --- | --- | --- | | Tagline | Open-source local audio transcription and subtitling suite with web UI | Curated resources for Whisper speech recognition system | | Stars | 3,048 | 2,361 | | Forks | 179 | 156 | | Open issues | 108 | 7 | | Language | Svelte | - | | Adopt for | Whishper is an open-source audio transcription and subtitling suite with a web UI that supports both GPU and CPU for local transcription. | awesome-whisper is an organized repository aggregating resources for OpenAI's Whisper AI-powered speech recognition system, covering models, apps, bindings, packages, and community tools. | | Persona | - | - | | Runtime | - | - | | License | Whishper uses the AGPL-3.0 license, requiring users who modify and deploy the software in a network service to make their changes available under the same license. | CC0-1.0 | | Categories | Developer Tools, Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [whishper](/tools/pluja-whishper.md) | [awesome-whisper](/tools/sindresorhus-awesome-whisper.md) | | --- | --- | --- | | Days since push | 348d | 134d | | Open issues (now) | 108 | 7 | | Full report | [trust report](/tools/pluja-whishper/trust.md) | [trust report](/tools/sindresorhus-awesome-whisper/trust.md) | ## Decision facts: whishper - **Pricing:** freemium - Free, open-source solution that does not charge for usage or require payment plans. - **Requirements:** Min 4 GB RAM; Requires Docker; Supports both GPU and CPU for processing.; NVIDIA GPU is optional. - **Adopt for:** Whishper is an open-source audio transcription and subtitling suite with a web UI that supports both GPU and CPU for local transcription. - **License detail:** Whishper uses the AGPL-3.0 license, requiring users who modify and deploy the software in a network service to make their changes available under the same license. ## Decision facts: awesome-whisper - **Adopt for:** awesome-whisper is an organized repository aggregating resources for OpenAI's Whisper AI-powered speech recognition system, covering models, apps, bindings, packages, and community tools. ## Choose when ### Choose whishper if… - License: whishper is AGPL-3.0, awesome-whisper is CC0-1.0. - Pricing: Free, open-source solution that does not charge for usage or require payment plans.. - Requirements: Min 4 GB RAM; Requires Docker; Supports both GPU and CPU for processing.; NVIDIA GPU is optional.. - Tags unique to whishper: audio-to-text, golang, speech-recognition, subtitles. - Also covers Developer Tools. - whishper ships Docker support for self-hosted deployment. - When you need to transcribe media files locally without sending data to external servers, ensuring privacy. ### Choose awesome-whisper if… - License: awesome-whisper is CC0-1.0, whishper is AGPL-3.0. - Tags unique to awesome-whisper: ai, artificial-intelligence, gpt, openai. - When seeking curated information on Whisper variants optimized for various platforms and languages ## When NOT to use whishper - When real-time transcription accuracy outweighs local processing needs, because while faster on CPU, it may not offer the same level of immediate precision as cloud-based solutions. - If your project is resource-constrained, especially in terms of development time and effort required for setup since Whishper involves a few manual steps to start using. - When direct audio input from web browsers is necessary because this feature is pending implementation. ## When NOT to use awesome-whisper - If looking for resources related to speech recognition systems from other providers not listed under OpenAI's Whisper ecosystem - In cases where the focus is on using pre-integrated solutions without the need for model customization ## Common questions ### What is the difference between whishper and awesome-whisper? whishper: Open-source local audio transcription and subtitling suite with web UI. awesome-whisper: Curated resources for Whisper speech recognition system. See the comparison table for live GitHub stats and shared categories. ### When should I choose whishper over awesome-whisper? Choose whishper over awesome-whisper when License: whishper is AGPL-3.0, awesome-whisper is CC0-1.0; Pricing: Free, open-source solution that does not charge for usage or require payment plans.; Requirements: Min 4 GB RAM; Requires Docker; Supports both GPU and CPU for processing.; NVIDIA GPU is optional.; Tags unique to whishper: audio-to-text, golang, speech-recognition, subtitles; Also covers Developer Tools; whishper ships Docker support for self-hosted deployment; When you need to transcribe media files locally without sending data to external servers, ensuring privacy. ### When should I choose awesome-whisper over whishper? Choose awesome-whisper over whishper when License: awesome-whisper is CC0-1.0, whishper is AGPL-3.0; Tags unique to awesome-whisper: ai, artificial-intelligence, gpt, openai; When seeking curated information on Whisper variants optimized for various platforms and languages. ### When should I avoid whishper? When real-time transcription accuracy outweighs local processing needs, because while faster on CPU, it may not offer the same level of immediate precision as cloud-based solutions. If your project is resource-constrained, especially in terms of development time and effort required for setup since Whishper involves a few manual steps to start using. When direct audio input from web browsers is necessary because this feature is pending implementation. ### When should I avoid awesome-whisper? If looking for resources related to speech recognition systems from other providers not listed under OpenAI's Whisper ecosystem In cases where the focus is on using pre-integrated solutions without the need for model customization ### Is whishper or awesome-whisper more popular on GitHub? whishper has more GitHub stars (3,048 vs 2,361). Stars measure visibility, not whether either tool fits your constraints. ### Are whishper and awesome-whisper open source? Yes - both are open-source projects on GitHub (whishper: AGPL-3.0, awesome-whisper: CC0-1.0). ### Where can I find alternatives to whishper or awesome-whisper? GraphCanon lists graph-backed alternatives at [whishper alternatives](/tools/pluja-whishper/alternatives) and [awesome-whisper alternatives](/tools/sindresorhus-awesome-whisper/alternatives) ([whishper markdown twin](/tools/pluja-whishper/alternatives.md), [awesome-whisper markdown twin](/tools/sindresorhus-awesome-whisper/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/pluja-whishper-vs-sindresorhus-awesome-whisper.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, whishper or awesome-whisper? whishper: Slowing. awesome-whisper: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for whishper and awesome-whisper? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [whishper trust report](/tools/pluja-whishper/trust); [awesome-whisper trust report](/tools/sindresorhus-awesome-whisper/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pluja-whishper`](/api/graphcanon/graph?tool=pluja-whishper) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_