pipecat-ojin 0.1.2

Ojin avatar (Speech-To-Video) service for Pipecat

pipecat-ojin

Ojin's Pipecat integration: drop a lip-synced talking-avatar face (OjinVideoService) into your existing pipeline in minutes.

OjinVideoService is the one stage that turns a voice agent into a video-call avatar — it lip-syncs to whatever your TTS produces and streams the avatar video back. It sits in the same slot as other video services in pipecat:

transport.input() -> STT -> LLM -> TTS -> [OjinVideoService] -> transport.output()

This package is a thin adapter over the framework-agnostic ojin-client SDK — all avatar behaviour (A/V sync, audio-as-clock playback, barge-in re-sync) lives in the SDK. It is not a fork of Pipecat — it depends on pipecat-ai as a library.

The SDK also shapes the audio it forwards to Ojin — priming a lead, then coalescing your TTS into large chunks — so the inference head never starves and lip-sync stays stable, whatever cadence your TTS produces. You don't manage input buffering, the playback clock, or frame-dropping; OjinVideoService just sits after your TTS and lip-syncs to it.

Install

pip install pipecat-ojin

This pulls in pipecat-ai and ojin-client[stv]. You provide the STT / LLM / TTS services for your pipeline (e.g. pip install "pipecat-ai[deepgram,groq,elevenlabs]").

Quickstart — the avatar face

from pipecat.pipeline.pipeline import Pipeline
from pipecat_ojin import OjinVideoService, OjinVideoSettings

avatar = OjinVideoService(
    OjinVideoSettings(
        api_key="OJIN_API_KEY",
        config_id="OJIN_CONFIG_ID",   # the Face model to drive
    )
)

pipeline = Pipeline(
    [transport.input(), stt, llm, tts, avatar, transport.output()]
)

The avatar's frame size comes from your Face model (config_id) — set your transport's video_out_width / video_out_height to match it (the example uses 512×512).

Get your OJIN_API_KEY and a Face model OJIN_CONFIG_ID from ojin.ai (docs: docs.ojin.ai).

Session tracing (optional)

Pass an ojin.stv.OjinSessionTrace to record a per-call Perfetto trace; the service dumps it on close:

from ojin.stv import OjinSessionTrace

trace = OjinSessionTrace(session_id="my-call", config_id="OJIN_CONFIG_ID")
avatar = OjinVideoService(OjinVideoSettings(...), session_trace=trace)

Example

A complete, runnable voice + avatar agent (browser WebRTC or Daily) lives in examples/ojin-bot/.

Deployment

OjinVideoService connects to Ojin over a WebSocket built for server-to-server use on a stable connection. Run your pipeline on a backend — ideally in US East, near Ojin's inference — for the lowest latency, and deliver the final media to your users over a realtime transport such as WebRTC or Daily.

Troubleshooting

• Avatar's mouth barely moves — confirm TTS audio is actually flowing into OjinVideoService. The SDK shapes the feed for you, so this usually means the pipeline isn't producing audio rather than a chunking problem.
• Garbled or stretched video — your transport's video_out_width / video_out_height must match the Face model's frame size (image_size, e.g. 512×512).
• Higher latency than expected — run the pipeline server-side in US East over a stable connection; don't run it on an end-user device.
• No backend servers available — inference capacity is momentarily exhausted; retry shortly.

Full guidance lives at docs.ojin.ai → Guides → Optimizing Performance / Troubleshooting.

Compatibility

Requirement	Version
Python	≥ 3.11
pipecat-ai	≥ 1.3.0
ojin-client[stv]	≥ 0.7.1

License

Apache-2.0. See LICENSE.