--- title: "WhisperLive vs awesome-whisper" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/collabora-whisperlive-vs-sindresorhus-awesome-whisper" tools: ["collabora-whisperlive", "sindresorhus-awesome-whisper"] --- # WhisperLive vs awesome-whisper *GraphCanon updated Jul 30, 2026* ## Verdict Pick WhisperLive if whisperLive offers nearly real-time speech transcription based on OpenAI's Whisper model across multiple hardware-accelerated backends; 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. [WhisperLive](https://github.com/collabora/WhisperLive) reports 4.2k GitHub stars, 574 forks, and 37 open issues, last pushed Jul 27, 2026. [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 [WhisperLive's repository](https://github.com/collabora/WhisperLive) and [awesome-whisper's repository](https://github.com/sindresorhus/awesome-whisper). | | [WhisperLive](/tools/collabora-whisperlive.md) | [awesome-whisper](/tools/sindresorhus-awesome-whisper.md) | | --- | --- | --- | | Tagline | A nearly-live implementation of OpenAI's Whisper for real-time voice recognition | Curated resources for Whisper speech recognition system | | Stars | 4,190 | 2,361 | | Forks | 574 | 156 | | Open issues | 37 | 7 | | Language | Python | - | | Adopt for | WhisperLive offers nearly real-time speech transcription based on OpenAI's Whisper model across multiple hardware-accelerated backends. | 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 | MIT | CC0-1.0 | | Categories | Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [WhisperLive](/tools/collabora-whisperlive.md) | [awesome-whisper](/tools/sindresorhus-awesome-whisper.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 134d | | Open issues (now) | 37 | 7 | | Owner type | Organization | User | | Full report | [trust report](/tools/collabora-whisperlive/trust.md) | [trust report](/tools/sindresorhus-awesome-whisper/trust.md) | ## Decision facts: WhisperLive - **Requirements:** Min 4 GB RAM; Requires Docker; PortAudio system dependency required.; Requires Python 3.12 environment and virtual environments - **Adopt for:** WhisperLive offers nearly real-time speech transcription based on OpenAI's Whisper model across multiple hardware-accelerated backends. ## 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 WhisperLive if… - License: WhisperLive is MIT, awesome-whisper is CC0-1.0. - Requirements: Min 4 GB RAM; Requires Docker; PortAudio system dependency required.; Requires Python 3.12 environment and virtual environments. - Tags unique to WhisperLive: dictation, text-to-speech, translation, voice-recognition. - When you require low-latency voice recognition and can leverage high-performance GPUs or OpenVINO for significant speedups. ### Choose awesome-whisper if… - License: awesome-whisper is CC0-1.0, WhisperLive is MIT. - 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 WhisperLive - Avoid using WhisperLive if the project lacks necessary hardware acceleration via TensorRT, OpenVINO, or is deployed on environments not compatible with Docker configurations. - Do not choose WhisperLive if a Windows-only solution is required, as its setup instructions are tailored for Linux and macOS. ## 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 WhisperLive and awesome-whisper? WhisperLive: A nearly-live implementation of OpenAI's Whisper for real-time voice recognition. awesome-whisper: Curated resources for Whisper speech recognition system. See the comparison table for live GitHub stats and shared categories. ### When should I choose WhisperLive over awesome-whisper? Choose WhisperLive over awesome-whisper when License: WhisperLive is MIT, awesome-whisper is CC0-1.0; Requirements: Min 4 GB RAM; Requires Docker; PortAudio system dependency required.; Requires Python 3.12 environment and virtual environments; Tags unique to WhisperLive: dictation, text-to-speech, translation, voice-recognition; When you require low-latency voice recognition and can leverage high-performance GPUs or OpenVINO for significant speedups. ### When should I choose awesome-whisper over WhisperLive? Choose awesome-whisper over WhisperLive when License: awesome-whisper is CC0-1.0, WhisperLive is MIT; 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 WhisperLive? Avoid using WhisperLive if the project lacks necessary hardware acceleration via TensorRT, OpenVINO, or is deployed on environments not compatible with Docker configurations. Do not choose WhisperLive if a Windows-only solution is required, as its setup instructions are tailored for Linux and macOS. ### 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 WhisperLive or awesome-whisper more popular on GitHub? WhisperLive has more GitHub stars (4,190 vs 2,361). Stars measure visibility, not whether either tool fits your constraints. ### Are WhisperLive and awesome-whisper open source? Yes - both are open-source projects on GitHub (WhisperLive: MIT, awesome-whisper: CC0-1.0). ### Where can I find alternatives to WhisperLive or awesome-whisper? GraphCanon lists graph-backed alternatives at [WhisperLive alternatives](/tools/collabora-whisperlive/alternatives) and [awesome-whisper alternatives](/tools/sindresorhus-awesome-whisper/alternatives) ([WhisperLive markdown twin](/tools/collabora-whisperlive/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/collabora-whisperlive-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, WhisperLive or awesome-whisper? WhisperLive: Very active. 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 WhisperLive and awesome-whisper? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [WhisperLive trust report](/tools/collabora-whisperlive/trust); [awesome-whisper trust report](/tools/sindresorhus-awesome-whisper/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=collabora-whisperlive`](/api/graphcanon/graph?tool=collabora-whisperlive) - 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/_