bithuman 1.11.3

Real-time avatar engine — 100+ FPS on CPU. Generate lip-synced video, stream live avatars to browsers. 1-2 CPU cores, <200ms latency. ARM, x86, macOS.

bitHuman Avatar Runtime

Real-time avatar engine for visual AI agents, digital humans, and creative characters.

Photorealistic avatars with audio-driven lip sync at 25 FPS. Runs on edge devices — typically 1–2 CPU cores, <200 ms end-to-end latency. Use it for voice agents with faces, video chatbots, tutors, NPCs, digital humans.

Which avatar should I use?

The SDK ships one API — AsyncBithuman.create(model_path=…) — driving two model types. Start with Essence. It's the default, runs on every supported platform, and is what every new user should reach for.

	Essence ← default	Expression
Runs on	Linux / macOS / Windows, any CPU	macOS 14+ on Apple Silicon M3 or later (on-device)
Rendering	Pre-built .imx bundle, in-process	Bundled Swift daemon via IPC
Footprint	1–2 CPU cores, <200 MB RAM	~4 GB RAM working set
Best for	Voice agents, kiosks, edge devices, everywhere	Custom-face avatars on Mac M3+

Loading an Expression .imx on an unsupported host raises a typed ExpressionModelNotSupported — not a crash. For cloud or self-hosted-GPU Expression dispatch (Linux + NVIDIA, or bitHuman's cloud workers), use the LiveKit plugin (bithuman.AvatarSession), not AsyncBithuman.

Architecture deep dive + production patterns at docs.bithuman.ai.

Install

pip install bithuman --upgrade

Pre-built wheels for Python 3.9 – 3.14 on Linux x86_64 + ARM64, macOS Intel + Apple Silicon, Windows x86_64. No compile step.

For LiveKit Agent integration (voice agents with faces over WebRTC):

pip install bithuman[agent]

Quick start — Essence (cross-platform, default)

Grab an .imx from your bitHuman dashboard (⋮ → Download), export your API secret, then:

CLI

export BITHUMAN_API_SECRET=your_secret
bithuman generate avatar.imx --audio speech.wav --output demo.mp4

Don't have a WAV to test with? Grab the 13-second sample bundled in the examples repo:

curl -O https://raw.githubusercontent.com/bithuman-product/bithuman-sdk-public/main/Examples/essence-selfhosted/speech.wav

Python

import asyncio, os
from bithuman import AsyncBithuman
from bithuman.audio import load_audio, float32_to_int16

async def main():
    runtime = await AsyncBithuman.create(
        model_path="avatar.imx",
        api_secret=os.environ["BITHUMAN_API_SECRET"],
    )
    await runtime.start()

    pcm, sr = load_audio("speech.wav")
    pcm = float32_to_int16(pcm)
    chunk = sr // 25  # one chunk per video frame
    for i in range(0, len(pcm), chunk):
        await runtime.push_audio(pcm[i : i + chunk].tobytes(), sr)
    await runtime.flush()

    async for frame in runtime.run():
        if frame.has_image:
            image = frame.bgr_image        # np.ndarray (H, W, 3), uint8
            audio = frame.audio_chunk      # synchronized output
        if frame.end_of_speech:
            break
    await runtime.stop()

asyncio.run(main())

Bithuman (no Async) is the threaded sync equivalent — same surface, no await. Usage examples live in the bithuman-examples repo.

Quick start — Expression on macOS M3+ (on-device, optional)

macOS 14+ on Apple Silicon M3 or later (M3+ recommended). M1 / M2 / Intel / Linux / Windows: use Essence above, or the LiveKit cloud plugin for Expression dispatch.

Expression bundles a diffusion-based animator that renders any face image in real time. Same API — just point at an Expression .imx.

bithuman demo --model expression.imx --audio speech.wav         # CLI one-shot

runtime = await AsyncBithuman.create(
    model_path="expression.imx",
    api_secret=os.environ["BITHUMAN_API_SECRET"],
    identity="alice.jpg",         # optional — overrides the bundle's default face
    quality="medium",             # "high" is offline-only (~2× slower)
)

await runtime.set_identity("bob.jpg")           # swap face mid-session, ~300 ms
await runtime.set_identity("bob_cached.npy")    # pre-encoded — instant

identity=	Cost on load	Cost on swap
None	0 (bundle's baked-in face)	n/a
.jpg / .png	~300 ms	~300 ms
.npy (pre-encoded)	instant	instant

quality=	Realtime @ 384×384 on M5	Realtime @ 512×512 on M5
"medium" (default)	1.84×	1.14×
"high"	1.05×	0.67× ⚠ sub-realtime — offline only

On a supported Mac, AsyncBithuman transparently spawns the bundled bithuman-expression-daemon subprocess when it sees an Expression manifest — there's nothing to configure.

API surface

from bithuman import (
    AsyncBithuman, Bithuman,                  # runtimes
    AudioChunk, VideoFrame, VideoControl,     # data classes
    Emotion, EmotionPrediction,               # emotion input
    BithumanError, TokenExpiredError, ...     # typed exceptions
)

# Stream audio in, pull frames out:
await runtime.push_audio(pcm_bytes, sample_rate)    # int16, any SR (auto-resampled)
await runtime.flush()
runtime.interrupt()                                 # cancel current playback

async for frame in runtime.run():
    frame.bgr_image          # (H, W, 3) uint8 BGR
    frame.audio_chunk        # synchronized output
    frame.end_of_speech
    frame.frame_index

# Controls:
await runtime.push(VideoControl(action="wave"))
await runtime.push(VideoControl(target_video="idle"))

# Identity (Expression only):
await runtime.set_identity("face.jpg")

Full reference: docs.bithuman.ai.

CLI

Every command reads $BITHUMAN_API_SECRET by default.

Command	Works with	Description
bithuman generate <model> --audio <file>	Essence + Expression	Render a lip-synced MP4
bithuman stream <model>	Essence + Expression	Live streaming server at localhost:3001
bithuman speak <audio>	—	Send audio to a running stream server
bithuman demo --model <imx> [--audio <file>]	Expression (macOS M3+)	Zero-friction Expression demo with a bundled sample clip
bithuman convert <model>	Essence	Convert legacy TAR .imx to IMX v2 (smaller, 1000× faster load)
bithuman pack …	Expression	Pack an Expression bundle from raw animator + encoder + renderer weights
bithuman info <model>	Essence + Expression	Show model metadata
bithuman validate <path>	Essence + Expression	Sanity-check a model file

Environment variables

Variable	Description
BITHUMAN_API_SECRET	API secret — recommended
BITHUMAN_API_KEY	Legacy alias read by generate / stream; use BITHUMAN_API_SECRET in new code
BITHUMAN_RUNTIME_TOKEN	Pre-minted JWT (alternative to API secret)
BITHUMAN_VERBOSE	Enable debug logging

LiveKit agents

Build full voice agents with faces:

from bithuman import AsyncBithuman
from bithuman.utils.agent import LocalAvatarRunner, LocalVideoPlayer, LocalAudioIO

runtime = await AsyncBithuman.create(model_path="avatar.imx", api_secret="…")
avatar = LocalAvatarRunner(
    bithuman_runtime=runtime,
    audio_input=session.audio,
    audio_output=LocalAudioIO(session, agent_output),
    video_output=LocalVideoPlayer(window_size=(1280, 720)),
)
await avatar.start()

For end-to-end Docker Compose stacks (LiveKit + OpenAI + bitHuman) see the bithuman-examples repo.

Troubleshooting

objc: Class AVFFrameReceiver is implemented in both …/cv2/… and …/av/… Both OpenCV and PyAV (av) ship their own FFmpeg dylibs. The warning appears as soon as both are imported — it's printed once at import and usually harmless. If you have the full opencv-python installed (rather than opencv-python-headless that bithuman depends on), it's a much larger collision — fix that variant explicitly:

pip uninstall -y opencv-python && pip install opencv-python-headless

Links

• Docs · PyPI
• bithuman-examples — end-to-end stacks (Docker Compose, LiveKit agents, web UIs)
• bitHuman Halo — free macOS desktop app built on this SDK
• bithuman.ai · API Keys

License

Commercial license required. See bithuman.ai for pricing.