--- title: "STT vs Kokoro-FastAPI" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/coqui-ai-stt-vs-remsky-kokoro-fastapi" tools: ["coqui-ai-stt", "remsky-kokoro-fastapi"] --- # STT vs Kokoro-FastAPI *GraphCanon updated Jul 30, 2026* ## Verdict Pick STT if sTT is an open-source deep-learning toolkit for speech-to-text with high-quality pre-trained models and efficient training on multi-GPU setups; pick Kokoro-FastAPI if kokoro-FastAPI is a Dockerized wrapper for the Kokoro-82M text-to-speech model using FastAPI. It supports multi-language capability and provides prebuilt images for various hardware types. [STT](https://coqui.ai) reports 2.6k GitHub stars, 299 forks, and 106 open issues, last pushed Mar 11, 2024. [Kokoro-FastAPI](https://github.com/remsky/Kokoro-FastAPI) has 5.3k stars, 858 forks, and 109 open issues, last pushed Jul 21, 2026. Figures are from public GitHub metadata via [STT's repository](https://github.com/coqui-ai/STT) and [Kokoro-FastAPI's repository](https://github.com/remsky/Kokoro-FastAPI). | | [STT](/tools/coqui-ai-stt.md) | [Kokoro-FastAPI](/tools/remsky-kokoro-fastapi.md) | | --- | --- | --- | | Tagline | A fast open-source deep-learning toolkit for speech-to-text | Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model | | Stars | 2,599 | 5,265 | | Forks | 299 | 858 | | Open issues | 106 | 109 | | Language | C++ | Python | | Adopt for | STT is an open-source deep-learning toolkit for speech-to-text with high-quality pre-trained models and efficient training on multi-GPU setups. | Kokoro-FastAPI is a Dockerized wrapper for the Kokoro-82M text-to-speech model using FastAPI. It supports multi-language capability and provides prebuilt images for various hardware types. | | Persona | - | - | | Runtime | - | - | | License | MPL-2.0 | Apache-2.0 | | Categories | Speech & Audio | Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [STT](/tools/coqui-ai-stt.md) | [Kokoro-FastAPI](/tools/remsky-kokoro-fastapi.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 871d | 8d | | Open issues (now) | 106 | 109 | | Owner type | Organization | User | | Full report | [trust report](/tools/coqui-ai-stt/trust.md) | [trust report](/tools/remsky-kokoro-fastapi/trust.md) | ## Decision facts: STT - **Adopt for:** STT is an open-source deep-learning toolkit for speech-to-text with high-quality pre-trained models and efficient training on multi-GPU setups. ## Decision facts: Kokoro-FastAPI - **Requirements:** Min 4 GB RAM; Requires Docker; Hardware-specific Docker images are provided for CUDA, ROCm (experimental), and CPU support.; Users without supported GPUs can still utilize the service via a CPU image. - **Adopt for:** Kokoro-FastAPI is a Dockerized wrapper for the Kokoro-82M text-to-speech model using FastAPI. It supports multi-language capability and provides prebuilt images for various hardware types. ## Choose when ### Choose STT if… - STT is primarily C++; Kokoro-FastAPI is Python. - License: STT is MPL-2.0, Kokoro-FastAPI is Apache-2.0. - Tags unique to STT: asr, automatic-speech-recognition, deep-learning, speech-recognition. - When you need a tool with high-quality pre-trained STT models ### Choose Kokoro-FastAPI if… - Kokoro-FastAPI is primarily Python; STT is C++. - License: Kokoro-FastAPI is Apache-2.0, STT is MPL-2.0. - Requirements: Min 4 GB RAM; Requires Docker; Hardware-specific Docker images are provided for CUDA, ROCm (experimental), and CPU support.; Users without supported GPUs can still utilize the service via a CPU image.. - Tags unique to Kokoro-FastAPI: docker, fastapi, kokoro tts, multi-gpu support. - You require a solution that can auto-download models and run natively on Apple Silicon (MPS) with support via UV. ## When NOT to use STT - Since its development has slowed, it may not suit users needing the latest research advancements - Avoid if you require community support as active maintenance has decreased - Not ideal if newer STT models like Whisper offer more suitable features - Consider alternatives with better-sustained Model Zoo access for more diverse pre-trained models ## When NOT to use Kokoro-FastAPI - If your hardware is not supported by the provided Docker images, for example, if you do not have a compatible NVIDIA, AMD GPU or CPU setup. - You are looking for non-Dockerized solutions. Kokoro-FastAPI focuses on containerization and might not fit environments that strictly avoid containers. ## Common questions ### What is the difference between STT and Kokoro-FastAPI? STT: A fast open-source deep-learning toolkit for speech-to-text. Kokoro-FastAPI: Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model. See the comparison table for live GitHub stats and shared categories. ### When should I choose STT over Kokoro-FastAPI? Choose STT over Kokoro-FastAPI when STT is primarily C++; Kokoro-FastAPI is Python; License: STT is MPL-2.0, Kokoro-FastAPI is Apache-2.0; Tags unique to STT: asr, automatic-speech-recognition, deep-learning, speech-recognition; When you need a tool with high-quality pre-trained STT models. ### When should I choose Kokoro-FastAPI over STT? Choose Kokoro-FastAPI over STT when Kokoro-FastAPI is primarily Python; STT is C++; License: Kokoro-FastAPI is Apache-2.0, STT is MPL-2.0; Requirements: Min 4 GB RAM; Requires Docker; Hardware-specific Docker images are provided for CUDA, ROCm (experimental), and CPU support.; Users without supported GPUs can still utilize the service via a CPU image.; Tags unique to Kokoro-FastAPI: docker, fastapi, kokoro tts, multi-gpu support; You require a solution that can auto-download models and run natively on Apple Silicon (MPS) with support via UV. ### When should I avoid STT? Since its development has slowed, it may not suit users needing the latest research advancements Avoid if you require community support as active maintenance has decreased Not ideal if newer STT models like Whisper offer more suitable features Consider alternatives with better-sustained Model Zoo access for more diverse pre-trained models ### When should I avoid Kokoro-FastAPI? If your hardware is not supported by the provided Docker images, for example, if you do not have a compatible NVIDIA, AMD GPU or CPU setup. You are looking for non-Dockerized solutions. Kokoro-FastAPI focuses on containerization and might not fit environments that strictly avoid containers. ### Is STT or Kokoro-FastAPI more popular on GitHub? Kokoro-FastAPI has more GitHub stars (5,265 vs 2,599). Stars measure visibility, not whether either tool fits your constraints. ### Are STT and Kokoro-FastAPI open source? Yes - both are open-source projects on GitHub (STT: MPL-2.0, Kokoro-FastAPI: Apache-2.0). ### Where can I find alternatives to STT or Kokoro-FastAPI? GraphCanon lists graph-backed alternatives at [STT alternatives](/tools/coqui-ai-stt/alternatives) and [Kokoro-FastAPI alternatives](/tools/remsky-kokoro-fastapi/alternatives) ([STT markdown twin](/tools/coqui-ai-stt/alternatives.md), [Kokoro-FastAPI markdown twin](/tools/remsky-kokoro-fastapi/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/coqui-ai-stt-vs-remsky-kokoro-fastapi.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, STT or Kokoro-FastAPI? STT: Dormant. Kokoro-FastAPI: Active. 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 STT and Kokoro-FastAPI? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [STT trust report](/tools/coqui-ai-stt/trust); [Kokoro-FastAPI trust report](/tools/remsky-kokoro-fastapi/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=coqui-ai-stt`](/api/graphcanon/graph?tool=coqui-ai-stt) - 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/_