TensorFlow Lite runtime

The .tflite model needs the TensorFlow Lite C shared library at runtime. Runtime controls where it comes from.

Runtime::Auto

This is the default. It checks in order:

1. MICRO_WAKEWORD_TFLITE_LIB environment variable;
2. TFLITE_C_LIB environment variable;
3. the bundled, checksum-verified TensorFlow Lite runtime for the current target.

Bundled targets are Windows x86-64, Linux x86-64, Linux ARM64, macOS Intel, and macOS Apple Silicon. Unsupported operating systems, CPU architectures, and musl Linux targets can supply a path or choose the system loader.

Explicit path

use micro_wakeword::{Detector, Runtime};

# fn run() -> micro_wakeword::Result<()> {
let detector = Detector::from_config_with_runtime(
    "wake-word.json",
    Runtime::from_path("tensorflowlite_c.dll"),
)?;
# Ok(()) }

With a listener builder:

# use micro_wakeword::{Listener, Runtime};
# fn run() -> micro_wakeword::Result<()> {
let listener = Listener::config_builder("wake-word.json")?
    .runtime(Runtime::from_path("/opt/tensorflow/libtensorflowlite_c.so"))
    .build()?;
# Ok(()) }

Runtime::System

# use micro_wakeword::{Detector, Runtime};
# fn run() -> micro_wakeword::Result<()> {
let detector = Detector::builder("model.tflite")
    .wake_word("hello")
    .probability_cutoff(0.5)
    .sliding_window_size(3)
    .runtime(Runtime::System)
    .build()?;
# Ok(()) }

This asks the underlying tflite-c-rs loader to search its supported system locations.

Why allow a custom path?

It lets applications support another target, control how native dependencies are packaged, use an approved runtime build, or test compatibility with another TensorFlow Lite release. The path points to the native library—not the model.

Extraction and verification

The native library is embedded in the crate. On first use, Runtime::Auto writes only the matching target’s library to the operating system cache, verifies its SHA-256 digest, and loads that exact path. Later runs reuse the verified file. No runtime download or network access occurs.

The Windows, Linux, and Apple Silicon builds use TensorFlow Lite 2.17.1. Intel macOS uses the upstream distributor’s newest Intel build, 2.17.0; the C API used by this crate is compatible with both.