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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 Sep 20, 2026

12views this month

STT logo

STT

coqui-ai/STT

★ 2.6kpushed Mar 11, 2024
vs
Kokoro-FastAPI logo

Kokoro-FastAPI

remsky/Kokoro-FastAPI

★ 5.5kpushed Sep 10, 2026

Trust & integrity

SignalSTTKokoro-FastAPI
Maintenance
Dormant (922d since push)
As of Sep 20, 2026 · github_public_v1
Active (10d since push)
As of Sep 20, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 20, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 20, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No published findings from this source as of 2026-07-11
As of Jul 11, 2026 · 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
STT trust reportKokoro-FastAPI trust reportTrust methodology

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
5.5k

Forks

STT
295
Kokoro-FastAPI
902

Open issues

STT
106
Kokoro-FastAPI
13

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
Sep 10, 2026

Categories

STT
Speech & Audio
Kokoro-FastAPI
Speech & Audio

Trust and health

Maintenance

STT
Dormant (18%)
Kokoro-FastAPI
Active (82%)

Days since push

STT
922d
Kokoro-FastAPI
10d

Open issues (now)

STT
106
Kokoro-FastAPI
13

Stars delta

STT
+7 (30d)
Kokoro-FastAPI
+196 (30d)

Open issues delta

STT
0 (30d)
Kokoro-FastAPI
-96 (30d)

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
GitHubDetails

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.
GitHubDetails

Explore

STT trust report →Kokoro-FastAPI trust report →Speech & Audio category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: STT 2.6k · Kokoro-FastAPI 5.5k (synced Sep 20, 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 (5,461 vs 2,606). 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.

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