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Comparison

transformers vs stt

Verdict

Pick transformers when license: transformers is Apache-2.0, stt is GPL-3.0; pick stt when license: stt is GPL-3.0, transformers is Apache-2.0.

Markdown twin · transformers alternatives · stt alternatives

GraphCanon updated today

transformers logo

transformers

huggingface/transformers

★ 162kpushed Jul 11, 2026
vs
stt logo

stt

jianchang512/stt

★ 4.7kpushed Jan 22, 2026

Trust & integrity

Signaltransformersstt
Maintenance
Very active (0d since push)
As of today · github_public_v1
Slowing (170d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Personal account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
1 critical, 2 high, 3 medium, 21 low (1 critical, 2 high, 3 medium, 21 low)
As of today · osv@v1
transformers trust reportstt trust reportTrust methodology

Tagline

transformers
Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models
stt
Voice Recognition to Text Tool / 一个离线运行的本地音视频转字幕工具，输出json、srt字幕、纯文字格式

Stars

transformers
162k
stt
4.7k

Forks

transformers
34k
stt
494

Open issues

transformers
2.5k
stt
100

Language

transformers
Python
stt
Python

Adopt for

transformers
Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3
stt
-

Persona

transformers
-
stt
-

Runtime

transformers
-
stt
-

License

transformers
Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems.
stt
GPL-3.0

Last pushed

transformers
Jul 11, 2026
stt
Jan 22, 2026

Categories

transformers
Computer Vision, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
stt
Inference & Serving, Model Training, Speech & Audio

Trust and health

Maintenance

transformers
Very active (96%)
stt
Slowing (36%)

Days since push

transformers
0d
stt
170d

Open issues (now)

transformers
2.5k
stt
100

Owner type

transformers
Organization
stt
User

Security scan

transformers
No lockfile
stt
1 critical, 2 high, 3 medium, 21 low (1 critical, 2 high, 3 medium, 21 low)

Full report

transformers
Trust report
stt
Trust report

Choose transformers if…

  • License: transformers is Apache-2.0, stt is GPL-3.0.
  • Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+.
  • Tags unique to transformers: audio, deep-learning, machine-learning, natural-language-processing.
  • Also covers Computer Vision, LLM Frameworks.
  • The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs.

When NOT to use transformers

  • If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable.
  • It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10.
GitHubDetails

Choose stt if…

  • License: stt is GPL-3.0, transformers is Apache-2.0.
  • Tags unique to stt: speech, speech-to-text, stt.
  • Leaner open-issue backlog (100).

When NOT to use stt

  • Last GitHub push was 171 days ago (slowing maintenance, Jan 22, 2026). Validate activity before betting a new project on stt.
  • Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
GitHubDetails

Explore

transformers trust report →stt trust report →Computer Vision category →Inference & Serving category →LLM Frameworks category →Model Training category →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: transformers 162k · stt 4.7k (synced Jul 11, 2026).

Common questions

What is the difference between transformers and stt?
transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. stt: Voice Recognition to Text Tool / 一个离线运行的本地音视频转字幕工具，输出json、srt字幕、纯文字格式. See the comparison table for live GitHub stats and shared categories.
When should I choose transformers over stt?
Choose transformers over stt when License: transformers is Apache-2.0, stt is GPL-3.0; Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+; Tags unique to transformers: audio, deep-learning, machine-learning, natural-language-processing; Also covers Computer Vision, LLM Frameworks; The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs.
When should I choose stt over transformers?
Choose stt over transformers when License: stt is GPL-3.0, transformers is Apache-2.0; Tags unique to stt: speech, speech-to-text, stt; Leaner open-issue backlog (100).
When should I avoid transformers?
If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable. It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10.
When should I avoid stt?
Last GitHub push was 171 days ago (slowing maintenance, Jan 22, 2026). Validate activity before betting a new project on stt. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Is transformers or stt more popular on GitHub?
transformers has more GitHub stars (162,482 vs 4,664). Stars measure visibility, not whether either tool fits your constraints.
Are transformers and stt open source?
Yes - both are open-source projects on GitHub (transformers: Apache-2.0, stt: GPL-3.0).
Where can I find alternatives to transformers or stt?
GraphCanon lists graph-backed alternatives at transformers alternatives and stt alternatives (transformers markdown twin, stt 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, transformers or stt?
transformers: Very active. stt: 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 transformers and stt?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: transformers trust report; stt trust report.