Comparison
STT vs Kokoro-FastAPI
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.
Markdown twin · STT alternatives · Kokoro-FastAPI alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | STT | Kokoro-FastAPI |
|---|---|---|
| Maintenance | Dormant (871d since push) As of 3w · github_public_v1 | Active (8d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No published findings from this source as of 2026-07-11 As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- STT
- A fast open-source deep-learning toolkit for speech-to-text
- Kokoro-FastAPI
- Dockerized FastAPI wrapper for Kokoro-82M text-to-speech model
Stars
- STT
- 2.6k
- Kokoro-FastAPI
- 5.3k
Forks
- STT
- 299
- Kokoro-FastAPI
- 858
Open issues
- STT
- 106
- Kokoro-FastAPI
- 109
Language
- STT
- C++
- Kokoro-FastAPI
- Python
Adopt for
- STT
- 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
- 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
- STT
- -
- Kokoro-FastAPI
- -
Runtime
- STT
- -
- Kokoro-FastAPI
- -
License
- STT
- MPL-2.0
- Kokoro-FastAPI
- Apache-2.0
Last pushed
- STT
- Mar 11, 2024
- Kokoro-FastAPI
- Jul 21, 2026
Categories
- STT
- Speech & Audio
- Kokoro-FastAPI
- Speech & Audio
Trust and health
Maintenance
- STT
- Dormant (18%)
- Kokoro-FastAPI
- Active (82%)
Days since push
- STT
- 871d
- Kokoro-FastAPI
- 8d
Open issues (now)
- STT
- 106
- Kokoro-FastAPI
- 109
Owner type
- STT
- Organization
- Kokoro-FastAPI
- User
OSV dependency advisories
- STT
- No lockfile (source not queried)
- Kokoro-FastAPI
- No published findings from this source as of 2026-07-11
Full report
- STT
- Trust report
- Kokoro-FastAPI
- Trust report
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
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (coqui-ai/STT) · observed Jul 30, 2026
- GitHub forks (coqui-ai/STT) · observed Jul 30, 2026
- Last push (coqui-ai/STT) · observed Mar 11, 2024
- License file (MPL-2.0) · observed Jul 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (remsky/Kokoro-FastAPI) · observed Jul 29, 2026
- GitHub forks (remsky/Kokoro-FastAPI) · observed Jul 29, 2026
- Last push (remsky/Kokoro-FastAPI) · observed Jul 21, 2026
- License file (Apache-2.0) · observed Jul 29, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: STT 2.6k · Kokoro-FastAPI 5.3k (synced Jul 30, 2026).
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 and Kokoro-FastAPI alternatives (STT markdown twin, Kokoro-FastAPI markdown twin), 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 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; Kokoro-FastAPI trust report.