Skip to content
← Tags

#Apache-2.0 (2)

Speech & AudioMediaTopA

Kokoro-82M: an Apache-2.0 TTS trained for a thousand dollars

An open weight TTS model from hexgrad at 82M parameters under Apache-2.0, repository hexgrad/kokoro (9,061 stars, verified through the GitHub API on 2026-09-29). We list it as the cost floor and the licence floor of the audio domain: what deserves to be remembered is not a leaderboard position but the fact that it published the full arithmetic of what one TTS deployment costs. Total training cost was 1000 dollars, being 500 A100 80GB GPU hours for each of v0.19 and v1.0; v0.19 (2024-12-25) trained on under 100 hours of audio with one language and ten voices, while v1.0 (2025-01-27) used a few hundred hours across eight languages and 54 voices. On price, two independent third party sources point at the same order of magnitude: ArtificialAnalysis records 65 cents per million characters on Replicate and DeepInfra lists 80 cents per million characters, which works out to roughly three to five cents per finished hour of audio. That price line is the reference floor for every comparison we make in the audio domain: whatever costs more than it is buying voice cloning, dialect coverage, emotion control, streaming latency or compliance backing, not the ability to be listened to at all. The technical form is a decoder-only StyleTTS 2 plus ISTFTNet stack with no diffusion and an unpublished encoder, G2P through the author maintained misaki library, espeak-ng on the system side and 24kHz output; input text accepts inline phoneme overrides such as [Kokoro](/kˈOkəɹO/), the same idea as the pinyin and CMU phoneme level pronunciation correction in CosyVoice 3, turning misreading from a probability problem into declarative configuration, except the scope here is a word rather than a whole prosody. The decoder-only shape also decides what it cannot do: there is no zero shot voice cloning, the 54 voices are trained in and a reference clip will not grow a new one, which is a division of labour rather than a defect. The compliance disclosure is close to unique in TTS: the model card states that only permissively licensed or copyright free audio was used, lists public domain audio, Apache and MIT licensed audio and synthetic audio generated by closed source TTS models as separate classes, explicitly excludes synthetic audio from open TTS models and custom voice cloning, publishes a CC BY attribution table, and publishes the model SHA256 so you can verify the weights you downloaded are the same ones. Boundaries: no voice cloning, so reference driven use cases are out; 24kHz is enough for broadcast and reading but not for music grade masters; the eight languages and 54 voices have not grown since v1.0 and Mandarin usability needs your own audition; the project is close to dormant with the last push on 2025-08-06, so bugs are yours to fix; and its fame has grown a phishing surface, with the model card naming lookalike domains containing kokoro as unrelated to the project, the only real entries being hexgrad/Kokoro-82M on Hugging Face and hexgrad/kokoro on GitHub, so never pay a search result that calls itself the Kokoro official site. Graded B (confirmed): what is confirmed is the cost and market price chain with independent third party sources, not audio quality.

<$1API 市场价(每百万字符,2025-04 模型卡口径)Confirmed · 2025-04
ProducthexgradSiteRepo
Kokoro-82M: an Apache-2.0 TTS trained for a thousand dollars
AI CodingDevelopmentTopC

Codex: an Apache-2.0 core, one agent in four form factors, and the reference harness for OpenAI models

Codex is the coding agent from OpenAI, and in 2026 it is not one product but four entry points onto one core: the CLI in your terminal, an IDE extension (officially for VS Code, Cursor and Windsurf), a desktop app launched with a single codex app command, and Codex Web running tasks remotely at chatgpt.com/codex. The openai/codex repository is Apache-2.0 and stood at 126,918 stars / 19,819 forks when fetched on 2026-09-28 - one of the few frontier-vendor coding harnesses whose harness is itself open source, so context assembly, tool ordering and approval gating can all be read. Safety is a first-class citizen: docs/ carries sandbox.md, execpolicy.md, exec.md, agents_md.md, skills.md and slash_commands.md, where execpolicy turns which commands may run unattended into an auditable, versionable, org-distributable policy instead of model goodwill, exec lets Codex run in CI, scripts and batch jobs rather than only waiting in a terminal, and AGENTS.md has become a de facto cross-tool standard. Four install channels (install.sh, install.ps1, npm, brew) and two billing models: usage included in ChatGPT plans versus per-token API keys, a cost question to settle before choosing. Codex is also the reference harness for OpenAI models - the Cursor Terminal-Bench 2.0 footnote states that OpenAI model scores use the Simple Codex harness, and the vendor-published Terminal-Bench 4.0 reading of 58.18% is already carried on the GPT-6 Astra card because it measures the model rather than the shell, so this entry does not claim it. Boundaries: models are closed and data egress cannot be avoided; developers.openai.com returns 403 to automated fetching, so the fact layer rests on the repository README/docs and the GitHub API. The card metric keeps only the verifiable fact layer (license plus stars). Confidence C (vendor-claim).

Apache-2.0 · 126,918★Harness 内核许可 + GitHub starsVendor Claim · 2026-09
ProductOpenAISiteRepo
Codex: an Apache-2.0 core, one agent in four form factors, and the reference harness for OpenAI models