# Kokoro [hexgrad/Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) served via [remsky/Kokoro-FastAPI](https://github.com/remsky/Kokoro-FastAPI). ## Why this stack exists Lowest-latency English TTS in the fleet by a wide margin — ~300 ms time-to-first-audio on GPU, 35-100x realtime, ~1 GB VRAM at fp16. Native streaming (Kokoro's `KPipeline.__call__` is a per-phrase generator) and the wrapper exposes it via OpenAI-compat `stream=true` over chunked HTTP — drop-in for any OpenAI SDK client. Complementary to the rest of the TTS slate: | | use case | |---|---| | **Kokoro** | low-latency English, fixed voice library | | **Chatterbox Turbo** | low-latency English w/ voice cloning + paralinguistic tags | | **IndexTTS-2** | English voice cloning + emotion vector / text control | | **Qwen3-TTS-1.7B-Base** | high-quality English voice cloning | | **CosyVoice 3** | multilingual (Chinese-leaning) | | **VibeVoice 1.5B** | long-form podcast / multi-speaker dialogue | ## API OpenAI-compat at `http://10.100.79.3:8193`: ```bash # List built-in voices (~60 of them, named like af_bella, am_adam, jf_*, zf_*). curl http://10.100.79.3:8193/v1/audio/voices # Single-shot synthesis. curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \ -H 'Content-Type: application/json' \ -d '{"model":"kokoro","input":"Hello there.","voice":"af_bella","response_format":"wav"}' \ > out.wav # Streaming — pipe straight into a player. curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \ -H 'Content-Type: application/json' \ -d '{"model":"kokoro","input":"long passage here…","voice":"af_bella","stream":true}' \ | mpv --no-cache - # Voice mixing — sum voicepacks with weights. curl -fsS -X POST http://10.100.79.3:8193/v1/audio/speech \ -H 'Content-Type: application/json' \ -d '{"model":"kokoro","input":"hello","voice":"af_bella(2)+af_heart(1)","response_format":"wav"}' \ > mix.wav ``` Web UI at `/web` (browse voices + try in-place); OpenAPI at `/docs`. Supported `response_format`: `mp3 | wav | opus | flac | pcm`. ## Voices - **Built-in**: 60+ in 8 languages (en-US, en-GB, ja, zh, es, fr, hi, it). Discoverable via `GET /v1/audio/voices` — naming convention is `_` (e.g. `af_bella`, `am_adam`, `jf_alpha`, `zf_xiaobei`). - **Custom blends**: weighted-average existing voicepacks into a new named voice using `playbooks/blend-kokoro-voice.yaml` (script: `scripts/blend_kokoro_voice.py`). Mirrors the wrapper's request-time `voice="a(w)+b(w)"` math but persists the result so it shows up in `/v1/audio/voices`. Example shipped: `athena = af_bella(2)+af_aoede(1)` normalized. ```bash scripts/elway irv-ml1 --playbook playbooks/blend-kokoro-voice.yaml \ --var 'recipe=af_bella(1)+am_adam(1)' --var out_name=androgyne # add --var force=1 to overwrite an existing voice ``` Persistence design: the wrapper enumerates exactly one directory (`VOICES_DIR`, default in-image `/app/api/src/voices/v1_0`). compose.yaml overrides `VOICES_DIR=/app/user_voices` (host bind mount `/worktank/kokoro/user_voices`) and adds a `command:` shim that `cp -r`'s the in-image built-ins into that dir on every container start. Result: * built-in voicepacks re-seed on each start, so image upgrades that add/change built-ins propagate automatically * custom blends written by the script live on the host bind mount — survive `docker restart`, `up --force-recreate`, image upgrade, and host reboot * one-time prereq: the host dir must be chowned to `1001:1001` (uid of `appuser` inside the container) so the shim's cp can write — handled by `playbooks/deploy-kokoro.yaml` - **Cloned voices**: training a Kokoro voice from samples is non- trivial — consult the hexgrad community for how-to. For voice cloning use Chatterbox Turbo or IndexTTS-2 instead. ## Deploy ```bash scripts/elway irv-ml1 --playbook playbooks/deploy-kokoro.yaml ``` First deploy: ~6.5 GB image pull from GHCR (~2-5 min on a fast link). No model download on first run — Kokoro-82M weights are baked in. Subsequent starts: a few seconds. ## License Apache-2.0 for both the wrapper code (remsky/Kokoro-FastAPI) and the Kokoro-82M weights (hexgrad).