magenta-community/magenta-realtime-2-small

Magenta RealTime 2 — PyTorch

A pure-PyTorch, transformers-compatible port of google/magenta-realtime-2, a real-time streaming music generation model. Every component (Depthformer LLM, SpectroStream neural codec, MusicCoCa style encoder) was reimplemented in torch and validated bit/token-exact against the original JAX/TFLite reference.

Loads with trust_remote_code=True — no JAX, no TFLite. Runtime deps: torch, transformers, sentencepiece (+ soundfile to save audio).

Usage

import torch, soundfile as sf
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "magenta-community/magenta-realtime-2-small", trust_remote_code=True, dtype=torch.bfloat16
).to("cuda").eval()

# Text / audio prompts via the MusicCoCa processor:
model.load_processor()                       # magenta-community/magenta-rt-musiccoca-torch
model.compile_steps()                        # optional: torch.compile the per-frame step (faster generation)
audio, state = model.generate(style="lo-fi hip hop, mellow", frames=50, temperature=1.1)
sf.write("out.wav", audio, 48000)            # ~2 s, 48 kHz stereo

# Continuous / live steering — keep passing `state` back; change `style` to morph:
chunk, state = model.generate(style="drum and bass", frames=25, state=state)

# Or skip the processor and pass explicit style tokens (12 RVQ ids):
audio, _ = model.generate(style=[100] * 12, frames=50)

# --- Real-time streaming: stateful per-frame (40 ms) decode, low latency ---
# small chunks are cheap (no overlap-save re-decode); keep passing `state` back,
# change `style` any time to morph live:
state = None
for _ in range(40):                          # ~8 s, ~0.2 s latency per step
    chunk, state = model.generate(style="techno", frames=5, state=state)
    # send `chunk` (48 kHz stereo float32) straight to your audio output

model.generate(...) returns (audio, state). Pass state back for seamless continuation; only the newly-available audio is returned each call (use flush=True on the final call to emit the tail).

Architecture

Component	What it is	Validation vs reference
Depthformer	decoder-only LLM, per-frame RVQ depth-autoregression	token-exact
SpectroStream	RVQ neural audio codec (encoder + decoder)	decode 2.7e-6 · encode codes 100%
MusicCoCa	text+audio style encoder (separate MusicCoCaProcessor)	tokens 100% exact

Generation is custom streaming, not GenerationMixin: the per-frame multi-codebook depth loop + streaming codec decode don't fit a single-token-stream _sample.

Streaming

generate returns only the newly-available audio and a state; pass state back to continue seamlessly, and change style between calls to steer the stream live:

import sounddevice as sd, numpy as np
state = None
with sd.OutputStream(samplerate=48000, channels=2, dtype="float32") as out:
    for i in range(20):                       # ~20 s
        chunk, state = model.generate(style="techno", frames=25, state=state, flush=(i == 19))
        out.write(np.ascontiguousarray(chunk, dtype=np.float32))

A runnable version (live playback or wav-out) is in examples/streaming.py.

Real-time / speed

torch.compile the per-frame step for faster-than-real-time generation (one-time warmup, any CUDA GPU):

model.compile_steps()                         # torch.compile (dynamic shapes); warms on first call
audio, state = model.generate(style="techno", frames=25)

To skip even that startup compile in real-time / production, export ahead-of-time AOTInductor graphs once and reload them with no compile-time (graphs are GPU-architecture-specific, so export on the GPU you run on):

model.export_aoti("./aoti")                   # compile once on your target GPU
# later / elsewhere on the same GPU arch:
model.load_aoti("./aoti")                     # instant load, no torch.compile

Live demos (ZeroGPU Spaces)

• 🎹 Jam — real-time note / keyboard control
• 🌀 Collider — explore prompt space
• 🎛️ Studio — producer-style controls

Sizes

• magenta-community/magenta-realtime-2 — base (canonical, higher quality)
• magenta-community/magenta-realtime-2-small — small (real-time)

Provenance

Weights are torch-native (re-keyed from google's checkpoint, numerically identical). The JAX→torch conversion + parity harness lives in the dev repo (fork). Apache-2.0, after upstream magenta-realtime.