I created an open‑source project soundstream-light, integrating a lightweight C++ inference implementation with the official TFLite model, and packaging it as a Python module and a cross‑platform command‑line tool.

SoundStream is an end‑to‑end neural audio compression model proposed by Google, capable of achieving high‑fidelity reconstruction at low bitrates, suitable for real‑time speech, online meetings, and multimedia applications.

Specific Usage

Install from PyPI using uv,

uv pip install soundstream-light

Download the model:

uv run soundstream-cli models fetch

Use the model in Python:

from scipy.io import wavfile
import numpy as np
from soundstream_light import encode, decode

rate, pcm = wavfile.read("test.wav")
waveform = pcm.astype(np.float32) / 32768.0
embeddings, meta = encode(waveform)
recon = decode(embeddings, metadata=meta)

wavfile.write("test_recon.wav", rate, (np.clip(recon, -1.0, 1.0) * 32768.0).astype(np.int16))

Run the model via the command line:

uv run soundstream-cli encode test.wav test.embeddings
uv run soundstream-cli decode test.embeddings test_recon.wav

The default model path is ./models; it can be customized with the environment variable SOUNDSTREAM_MODELS_DIR.

The source code for this open‑source project is at https://github.com/binbinsh/soundstream-light/.