diff --git a/README.md b/README.md
index 2460d59..81fc92e 100644
--- a/README.md
+++ b/README.md
@@ -284,10 +284,17 @@ Or run stages individually:
 livekit-wakeword generate configs/prod.yaml  # TTS synthesis + adversarial negatives
 livekit-wakeword augment configs/prod.yaml   # Augment + extract features
 livekit-wakeword train configs/prod.yaml     # 3-phase adaptive training
-livekit-wakeword export configs/prod.yaml    # Export to ONNX
+livekit-wakeword export configs/prod.yaml    # Export to ONNX (default)
 livekit-wakeword eval configs/prod.yaml      # Evaluate model (DET curve, AUT, FPPH)
 ```
 
+The export format defaults to ONNX. Pass `--format tflite` (or set `output_format: tflite` in the config) to also emit an [openWakeWord](https://github.com/dscripka/openWakeWord)-compatible TFLite model — this requires the `tflite` extra and currently supports the `dnn` head only. See [Export & Inference](docs/export-and-inference.md#tflite-export-openwakeword-compatible) for details.
+
+```bash
+pip install livekit-wakeword[tflite]
+livekit-wakeword export configs/prod.yaml --format tflite
+```
+
 You can also evaluate any compatible ONNX model (e.g., one trained with openWakeWord):
 
 ```bash
diff --git a/docs/export-and-inference.md b/docs/export-and-inference.md
index 54e8579..67d26ab 100644
--- a/docs/export-and-inference.md
+++ b/docs/export-and-inference.md
@@ -1,15 +1,17 @@
 # Export & Inference
 
-The export stage converts the trained PyTorch classifier to ONNX for deployment. The inference API provides `WakeWordModel` for prediction and `WakeWordListener` for async microphone detection.
+The export stage converts the trained PyTorch classifier to ONNX or TFLite for deployment. The inference API provides `WakeWordModel` for prediction and `WakeWordListener` for async microphone detection.
 
-**Source:** `src/livekit/wakeword/export/onnx.py`, `src/livekit/wakeword/inference/model.py`, `src/livekit/wakeword/inference/listener.py`
-**CLI:** `livekit-wakeword export <config>`
+**Source:** `src/livekit/wakeword/export/onnx.py`, `src/livekit/wakeword/export/tflite.py`, `src/livekit/wakeword/inference/model.py`, `src/livekit/wakeword/inference/listener.py`
+**CLI:** `livekit-wakeword export <config> [--format onnx|tflite]`
+
+The output format is chosen by (in priority order) the `--format` flag, then the `output_format` field in the config (defaults to `onnx`).
 
 ## ONNX Export
 
 ### Classifier Export
 
-`export_classifier()` exports the trained PyTorch classifier head to ONNX format.
+`export_onnx()` exports the trained PyTorch classifier head to ONNX format.
 
 | Property | Value |
 |----------|-------|
@@ -34,7 +36,43 @@ livekit-wakeword export configs/hey_jarvis.yaml --quantize
 
 ### Export Entry Point
 
-`run_export()` loads the trained model from `output/<model_name>/<model_name>.pt`, exports it to ONNX, and optionally quantizes it. Raises `FileNotFoundError` if the trained model doesn't exist.
+`run_export(config, quantize=False, format=None)` loads the trained model from `output/<model_name>/<model_name>.pt`, exports it to ONNX, and optionally quantizes it. `format` defaults to `config.output_format`. ONNX is always produced (it is the conversion source for TFLite); when `format="tflite"`, the TFLite artifact is produced as well and its path is returned. Raises `FileNotFoundError` if the trained model doesn't exist.
+
+## TFLite Export (openWakeWord-compatible)
+
+`export_tflite()` converts an exported ONNX classifier to TFLite via `onnx2tf` (ONNX → TF SavedModel → TFLite), producing an artifact that [openWakeWord](https://github.com/dscripka/openWakeWord) can load directly.
+
+Requires the optional extra:
+
+```bash
+uv sync --extra tflite     # or: pip install 'livekit-wakeword[tflite]'
+```
+
+```bash
+livekit-wakeword export configs/hey_jarvis.yaml --format tflite
+```
+
+### openWakeWord contract
+
+openWakeWord loads classifier models with `ai_edge_litert.interpreter` and runs them without resizing tensors, so the artifact must satisfy:
+
+| Requirement | Detail |
+|-------------|--------|
+| Input shape | **Static** `(1, 16, 96)` float32 (no dynamic batch — openWakeWord never calls `resize_tensor_input`) |
+| Output shape | `(1, 1)` float32 sigmoid score |
+| Ops | **Builtin TFLite ops only** — the LiteRT interpreter has no Flex/SELECT_TF delegate |
+
+We pin the input shape with onnx2tf's `overwrite_input_shape` + `keep_shape_absolutely_input_names` (without the latter, onnx2tf's NCHW→NHWC pass transposes the input to `(1, 96, 16)`) and restrict the converter to `TFLITE_BUILTINS`.
+
+### Head support
+
+| Head | TFLite export | Notes |
+|------|---------------|-------|
+| `dnn` | Supported | Bit-exact vs ONNX/PyTorch (verified, maxdiff `0.0`) |
+| `conv_attention` | Not supported | onnx2tf emits an unsupported constant for the attention block |
+| `rnn` | Not supported | LSTM lowers to `TensorList` ops requiring the Flex delegate (which openWakeWord can't load) |
+
+Use `dnn` for openWakeWord-compatible TFLite; deploy `conv_attention`/`rnn` via ONNX. Requesting TFLite for an unsupported head raises `NotImplementedError` before any export work begins.
 
 ## Inference API
 
diff --git a/pyproject.toml b/pyproject.toml
index 0e5fde3..b8e5296 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ readme = "README.md"
 requires-python = ">=3.11"
 license = "Apache-2.0"
 authors = [{ name = "Binh Pham", email = "binh.pham@livekit.io" }]
-keywords = ["wake-word", "keyword-spotting", "voice", "speech", "livekit", "onnx"]
+keywords = ["wake-word", "keyword-spotting", "voice", "speech", "livekit", "onnx", "tflite"]
 classifiers = [
     "Development Status :: 4 - Beta",
     "Intended Audience :: Developers",
@@ -64,6 +64,15 @@ export = [
     "onnxruntime>=1.17",
     "onnxscript>=0.6.2",
 ]
+tflite = [
+    "onnx2tf>=1.22",
+    "tensorflow>=2.15",
+    "tf-keras>=2.15",
+    "onnx-graphsurgeon>=0.3.27",
+    "sng4onnx>=1.0.4",
+    "psutil>=5.9",
+    "ai-edge-litert>=1.0",
+]
 [project.urls]
 Homepage = "https://github.com/livekit/livekit-wakeword"
 Repository = "https://github.com/livekit/livekit-wakeword"
diff --git a/src/livekit/wakeword/__init__.py b/src/livekit/wakeword/__init__.py
index e146f49..0c633c0 100644
--- a/src/livekit/wakeword/__init__.py
+++ b/src/livekit/wakeword/__init__.py
@@ -9,6 +9,7 @@
 # works with only numpy + onnxruntime (no torch, pydantic, etc.).
 _LAZY_IMPORTS: dict[str, tuple[str, str]] = {
     "WakeWordConfig": (".config", "WakeWordConfig"),
+    "ExportFormat": (".config", "ExportFormat"),
     "load_config": (".config", "load_config"),
     "run_augment": (".data.augment", "run_augment"),
     "run_extraction": (".data.features", "run_extraction"),
@@ -31,6 +32,7 @@ def __getattr__(name: str) -> object:
 
 __all__ = [
     "WakeWordConfig",
+    "ExportFormat",
     "WakeWordListener",
     "WakeWordModel",
     "Detection",
diff --git a/src/livekit/wakeword/cli.py b/src/livekit/wakeword/cli.py
index bc732ee..3dd393d 100644
--- a/src/livekit/wakeword/cli.py
+++ b/src/livekit/wakeword/cli.py
@@ -313,17 +313,24 @@ def train(
 @app.command()
 def export(
     config_path: str = typer.Argument(..., help="Path to wake word config YAML"),
+    format: str = typer.Option(
+        None,
+        "--format",
+        "-f",
+        help="Export format: onnx or tflite (default: config.output_format)",
+    ),
     quantize: bool = typer.Option(False, "--quantize", help="Apply INT8 quantization"),
 ) -> None:
-    """Export trained model to ONNX (optionally quantize for embedded)."""
+    """Export trained model (optionally quantize for embedded)."""
     config = load_config(config_path)
 
-    logger.info(f"Exporting '{config.model_name}' to ONNX...")
+    fmt = format or config.output_format
+    logger.info(f"Exporting '{config.model_name}' to {fmt}...")
 
     from .export.onnx import run_export
 
-    onnx_path = run_export(config, quantize=quantize)
-    logger.info(f"Export complete! ONNX model at {onnx_path}")
+    out_path = run_export(config, quantize=quantize, format=fmt)
+    logger.info(f"Export complete! Model at {out_path}")
 
 
 @app.command()
@@ -361,8 +368,17 @@ def run(
     config_path: str = typer.Argument(..., help="Path to wake word config YAML"),
 ) -> None:
     """Run entire pipeline end-to-end: generate → augment → train → export."""
+    from .config import ExportFormat
+
     config = load_config(config_path)
 
+    # Validate the export target up front so we don't train for hours and then
+    # fail at the export step (e.g. tflite + an unsupported head).
+    if config.output_format == ExportFormat.tflite:
+        from .export.tflite import ensure_tflite_supported
+
+        ensure_tflite_supported(config.model.model_type)
+
     logger.info(f"Running full pipeline for '{config.model_name}'...")
 
     from .data.augment import run_augment
@@ -384,8 +400,11 @@ def run(
     logger.info("Step 4/6: Train classifier")
     run_train(config)
 
-    logger.info("Step 5/6: Export to ONNX")
-    onnx_path = run_export(config)
+    logger.info(f"Step 5/6: Export to {config.output_format}")
+    run_export(config)
+
+    # Eval runs on the ONNX artifact (run_export always produces it).
+    onnx_path = config.model_output_dir / f"{config.model_name}.onnx"
 
     logger.info("Step 6/6: Evaluate model")
     results = run_eval(config, onnx_path)
diff --git a/src/livekit/wakeword/config.py b/src/livekit/wakeword/config.py
index bbe7b1c..5ec26be 100644
--- a/src/livekit/wakeword/config.py
+++ b/src/livekit/wakeword/config.py
@@ -36,6 +36,13 @@ class TtsBackend(StrEnum):
     voxcpm = "voxcpm"
 
 
+class ExportFormat(StrEnum):
+    """Artifact format produced by the export stage."""
+
+    onnx = "onnx"
+    tflite = "tflite"
+
+
 # Preset mapping: size -> (layer_dim, n_blocks)
 MODEL_SIZE_PRESETS: dict[ModelSize, tuple[int, int]] = {
     ModelSize.tiny: (16, 1),
@@ -132,6 +139,9 @@ class WakeWordConfig(BaseModel):
     data_dir: Annotated[str, Field(description="Root data directory")] = "./data"
     output_dir: str = "./output"
 
+    # Export
+    output_format: ExportFormat = ExportFormat.onnx
+
     # Augmentation
     augmentation: AugmentationConfig = Field(default_factory=AugmentationConfig)
 
diff --git a/src/livekit/wakeword/export/__init__.py b/src/livekit/wakeword/export/__init__.py
index 8d8af04..d306089 100644
--- a/src/livekit/wakeword/export/__init__.py
+++ b/src/livekit/wakeword/export/__init__.py
@@ -1,9 +1,11 @@
-"""ONNX export and quantization."""
+"""Model export (ONNX, TFLite) and quantization."""
 
-from .onnx import export_classifier, quantize_onnx, run_export
+from .onnx import export_onnx, quantize_onnx, run_export
+from .tflite import export_tflite
 
 __all__ = [
-    "export_classifier",
+    "export_onnx",
+    "export_tflite",
     "quantize_onnx",
     "run_export",
 ]
diff --git a/src/livekit/wakeword/export/onnx.py b/src/livekit/wakeword/export/onnx.py
index e690888..acf66d2 100644
--- a/src/livekit/wakeword/export/onnx.py
+++ b/src/livekit/wakeword/export/onnx.py
@@ -3,18 +3,19 @@
 from __future__ import annotations
 
 import logging
+import tempfile
 from pathlib import Path
 
 import onnx
 import torch
 
-from ..config import WakeWordConfig
+from ..config import ExportFormat, WakeWordConfig
 from ..models.pipeline import WakeWordClassifier
 
 logger = logging.getLogger(__name__)
 
 
-def export_classifier(
+def export_onnx(
     config: WakeWordConfig,
     model_path: Path,
     output_path: Path,
@@ -60,34 +61,91 @@ def export_classifier(
 
 def quantize_onnx(input_path: Path, output_path: Path | None = None) -> Path:
     """Apply INT8 dynamic quantization to an ONNX model."""
-    from onnxruntime.quantization import quantize_dynamic, QuantType
+    from onnxruntime.quantization import QuantType, quantize_dynamic
 
     if output_path is None:
         output_path = input_path.with_suffix(".int8.onnx")
 
-    quantize_dynamic(
-        model_input=str(input_path),
-        model_output=str(output_path),
-        weight_type=QuantType.QInt8,
-    )
+    # The torch dynamo ONNX exporter emits value_info entries describing the
+    # weight initializers (e.g. a Gemm B of shape [out, in]). When the dynamic
+    # quantizer rewrites Gemm->MatMul it transposes those weights in place but
+    # leaves the value_info stale, so its strict shape-inference pass then fails
+    # with "Inferred shape and existing shape differ". Dropping initializer
+    # value_info (which is redundant — shapes are inferred from the tensors)
+    # avoids the conflict. We do this on a temp copy so the input model on disk
+    # is left untouched.
+    model = onnx.load(str(input_path))
+    init_names = {init.name for init in model.graph.initializer}
+    kept = [vi for vi in model.graph.value_info if vi.name not in init_names]
+
+    if len(kept) == len(model.graph.value_info):
+        # Nothing to strip — quantize the input directly.
+        quantize_dynamic(
+            model_input=str(input_path),
+            model_output=str(output_path),
+            weight_type=QuantType.QInt8,
+        )
+    else:
+        del model.graph.value_info[:]
+        model.graph.value_info.extend(kept)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            cleaned = Path(tmp_dir) / "cleaned.onnx"
+            onnx.save(model, str(cleaned))
+            quantize_dynamic(
+                model_input=str(cleaned),
+                model_output=str(output_path),
+                weight_type=QuantType.QInt8,
+            )
+
     logger.info(f"Quantized ONNX model to {output_path}")
     return output_path
 
 
-def run_export(config: WakeWordConfig, quantize: bool = False) -> Path:
-    """Export trained model to ONNX."""
+def run_export(
+    config: WakeWordConfig,
+    quantize: bool = False,
+    format: ExportFormat | str | None = None,
+) -> Path:
+    """Export the trained classifier head.
+
+    Args:
+        config: Wake word config.
+        quantize: Apply INT8 quantization to the exported artifact.
+        format: Output format (``onnx`` or ``tflite``). Defaults to
+            ``config.output_format`` when ``None``.
+
+    Returns:
+        Path to the primary exported artifact for the chosen format. ONNX is
+        always produced as well, since TFLite is converted from it.
+    """
+    fmt = ExportFormat(format) if format is not None else config.output_format
+
+    # Fail fast on unsupported (head, format) combinations before doing any work.
+    if fmt == ExportFormat.tflite:
+        from .tflite import ensure_tflite_supported
+
+        ensure_tflite_supported(config.model.model_type)
+
     model_dir = config.model_output_dir
     model_path = model_dir / f"{config.model_name}.pt"
 
     if not model_path.exists():
         raise FileNotFoundError(f"Trained model not found: {model_path}")
 
-    # Export classifier head
+    # Export classifier head to ONNX (also the conversion source for TFLite).
     onnx_path = model_dir / f"{config.model_name}.onnx"
-    export_classifier(config, model_path, onnx_path)
+    export_onnx(config, model_path, onnx_path)
+
+    if fmt == ExportFormat.onnx:
+        if quantize:
+            quantize_onnx(onnx_path)
+        return onnx_path
+
+    if fmt == ExportFormat.tflite:
+        # TFLite quantization is applied by the TF converter, not the ONNX path.
+        from .tflite import export_tflite
 
-    # Optionally quantize
-    if quantize:
-        quantize_onnx(onnx_path)
+        tflite_path = model_dir / f"{config.model_name}.tflite"
+        return export_tflite(onnx_path, tflite_path, quantize=quantize)
 
-    return onnx_path
+    raise ValueError(f"Unsupported export format: {fmt}")
diff --git a/src/livekit/wakeword/export/tflite.py b/src/livekit/wakeword/export/tflite.py
new file mode 100644
index 0000000..094cd93
--- /dev/null
+++ b/src/livekit/wakeword/export/tflite.py
@@ -0,0 +1,141 @@
+"""TFLite export for wake word classifier heads (openWakeWord-compatible).
+
+The classifier is first exported to ONNX (see ``onnx.py``), then converted
+ONNX → TF SavedModel → TFLite via ``onnx2tf``, which handles the NCHW↔NHWC
+layout transposes that a naive converter would get wrong.
+
+openWakeWord compatibility contract
+-----------------------------------
+openWakeWord loads classifier models with ``ai_edge_litert.interpreter`` (the
+LiteRT successor to ``tflite_runtime``) and runs them like so::
+
+    interp.set_tensor(input_index, x)   # x is (1, 16, 96) float32
+    interp.invoke()
+    score = interp.get_tensor(output_index)[0][0]   # (1, 1) -> scalar
+
+That imposes three hard requirements on the artifact we emit:
+
+1. **Static input shape ``(1, 16, 96)`` float32.** openWakeWord never calls
+   ``resize_tensor_input``, so a dynamic/None batch axis would break it. We
+   force the shape via ``overwrite_input_shape`` during conversion.
+2. **Single output of shape ``(1, 1)``** (the sigmoid confidence).
+3. **Builtin TFLite ops only** — the LiteRT interpreter is created without the
+   Flex/SELECT_TF delegate. ``onnx2tf`` targets builtin ops and fails the
+   conversion rather than emitting Flex, so a successful export is loadable.
+
+Head support (verified via onnx2tf 1.28 / TF 2.x):
+
+- ``dnn``: converts cleanly and is **bit-exact** with the ONNX/PyTorch model.
+- ``conv_attention`` / ``rnn``: do **not** currently convert through onnx2tf
+  (the attention emits an unsupported constant; the LSTM lowers to ``TensorList``
+  ops that require the Flex delegate openWakeWord cannot load). Use ``dnn`` for
+  openWakeWord-compatible TFLite, or deploy those heads via ONNX.
+
+Requires the optional ``tflite`` extra: ``uv sync --extra tflite``.
+"""
+
+from __future__ import annotations
+
+import logging
+import tempfile
+from pathlib import Path
+
+from ..config import ModelType
+
+logger = logging.getLogger(__name__)
+
+# Fixed classifier I/O contract shared with openWakeWord.
+_INPUT_NAME = "embeddings"
+_INPUT_SHAPE = "1,16,96"
+
+# Heads whose ONNX graph converts to builtin-op TFLite that openWakeWord can load.
+# conv_attention and rnn currently do not (see module docstring).
+TFLITE_SUPPORTED_HEADS = frozenset({ModelType.dnn})
+
+
+def ensure_tflite_supported(model_type: ModelType) -> None:
+    """Raise if ``model_type`` cannot be exported to openWakeWord-compatible TFLite.
+
+    Fails fast with an actionable message instead of letting the onnx2tf
+    conversion crash deep in its graph rewriter.
+    """
+    if model_type not in TFLITE_SUPPORTED_HEADS:
+        supported = ", ".join(sorted(h.value for h in TFLITE_SUPPORTED_HEADS))
+        raise NotImplementedError(
+            f"TFLite export is not supported for the '{model_type.value}' head. "
+            "It cannot be converted to builtin-op TFLite that openWakeWord can load "
+            "(the attention block emits an unsupported constant; the LSTM requires the "
+            "Flex delegate, which openWakeWord's LiteRT interpreter does not enable).\n"
+            f"Supported heads: {supported}.\n"
+            "Use '--format onnx' for this head, or set model.model_type to 'dnn'."
+        )
+
+
+def export_tflite(
+    onnx_path: Path,
+    output_path: Path,
+    quantize: bool = False,
+) -> Path:
+    """Convert an exported classifier ONNX model to openWakeWord-compatible TFLite.
+
+    Args:
+        onnx_path: Source ``.onnx`` produced by ``export_onnx``.
+        output_path: Destination ``.tflite`` path.
+        quantize: Apply default (dynamic-range INT8) TFLite optimizations.
+
+    Returns:
+        Path to the written ``.tflite`` file with a static ``(1, 16, 96)`` input.
+
+    Raises:
+        ImportError: The optional ``tflite`` extra is not installed.
+        FileNotFoundError: ``onnx_path`` does not exist.
+        RuntimeError: onnx2tf/TFLite could not convert the graph (e.g. the head
+            needs ops outside the builtin TFLite set).
+    """
+    try:
+        import onnx2tf
+        import tensorflow as tf
+    except ImportError as e:
+        raise ImportError(
+            "TFLite export requires the optional 'tflite' extra. Install with:\n"
+            "  uv sync --extra tflite\n"
+            "or: pip install 'livekit-wakeword[tflite]'"
+        ) from e
+
+    if not onnx_path.exists():
+        raise FileNotFoundError(f"Source ONNX model not found: {onnx_path}")
+
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    try:
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            # onnx2tf writes a TF SavedModel into tmp_dir. We pin the input to a
+            # static (1, 16, 96) so the LiteRT interpreter (no resize) can feed it.
+            onnx2tf.convert(
+                input_onnx_file_path=str(onnx_path),
+                output_folder_path=tmp_dir,
+                overwrite_input_shape=[f"{_INPUT_NAME}:{_INPUT_SHAPE}"],
+                # Keep the input as (1, 16, 96) exactly -- without this, onnx2tf's
+                # NCHW->NHWC pass transposes it to (1, 96, 16) and breaks openWakeWord.
+                keep_shape_absolutely_input_names=[_INPUT_NAME],
+                copy_onnx_input_output_names_to_tflite=True,
+                non_verbose=True,
+            )
+
+            converter = tf.lite.TFLiteConverter.from_saved_model(tmp_dir)
+            # Builtin ops only -- openWakeWord's interpreter has no Flex delegate.
+            converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
+            if quantize:
+                converter.optimizations = [tf.lite.Optimize.DEFAULT]
+            tflite_bytes = converter.convert()
+    except Exception as e:
+        raise RuntimeError(
+            f"Failed to convert {onnx_path.name} to TFLite. The classifier head likely "
+            "uses ops that cannot be mapped to the builtin TFLite set that openWakeWord "
+            "requires (it loads models without the Flex delegate). Only the 'dnn' head is "
+            f"currently supported for TFLite export.\nUnderlying error: {e}"
+        ) from e
+
+    output_path.write_bytes(tflite_bytes)
+    logger.info(f"Exported openWakeWord-compatible TFLite to {output_path}")
+    return output_path
diff --git a/tests/test_config.py b/tests/test_config.py
index 52cc0e4..a2887f1 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -8,6 +8,7 @@
 
 from livekit.wakeword.config import (
     MODEL_SIZE_PRESETS,
+    ExportFormat,
     ModelConfig,
     ModelSize,
     ModelType,
@@ -65,6 +66,21 @@ def test_load_config_from_yaml(tmp_path: Path):
     assert config.model.model_size == ModelSize.large
 
 
+def test_output_format_default():
+    config = WakeWordConfig(model_name="test", target_phrases=["test"])
+    assert config.output_format is ExportFormat.onnx
+
+
+def test_output_format_from_yaml(tmp_path: Path):
+    yaml_path = tmp_path / "cfg.yaml"
+    yaml_path.write_text(
+        "model_name: t\ntarget_phrases: [a]\noutput_format: tflite\n",
+        encoding="utf-8",
+    )
+    config = load_config(yaml_path)
+    assert config.output_format is ExportFormat.tflite
+
+
 def test_batch_n_per_class_default():
     config = WakeWordConfig(model_name="test", target_phrases=["test"])
     assert config.batch_n_per_class["positive"] == 50
diff --git a/tests/test_export.py b/tests/test_export.py
new file mode 100644
index 0000000..3890bbc
--- /dev/null
+++ b/tests/test_export.py
@@ -0,0 +1,142 @@
+"""Tests for model export (ONNX + TFLite) and quantization."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import onnxruntime as ort
+import pytest
+import torch
+
+from livekit.wakeword.config import ExportFormat, ModelType, WakeWordConfig
+from livekit.wakeword.export import export_tflite, run_export
+from livekit.wakeword.export.tflite import TFLITE_SUPPORTED_HEADS, ensure_tflite_supported
+from livekit.wakeword.models.pipeline import WakeWordClassifier
+
+
+def _make_checkpoint(tmp_path: Path, model_type: ModelType) -> WakeWordConfig:
+    """Build a config + a randomly-initialized .pt checkpoint at the expected path."""
+    config = WakeWordConfig(
+        model_name="ww",
+        target_phrases=["hey test"],
+        output_dir=str(tmp_path / "output"),
+    )
+    config.model.model_type = model_type
+    model = WakeWordClassifier(config)
+    model.eval()
+    config.model_output_dir.mkdir(parents=True, exist_ok=True)
+    torch.save(model.state_dict(), config.model_output_dir / f"{config.model_name}.pt")
+    return config
+
+
+def test_run_export_onnx_io_contract(tmp_path: Path):
+    config = _make_checkpoint(tmp_path, ModelType.dnn)
+    out = run_export(config)  # default format == onnx
+    assert out.suffix == ".onnx" and out.exists()
+
+    session = ort.InferenceSession(str(out), providers=["CPUExecutionProvider"])
+    assert session.get_inputs()[0].name == "embeddings"
+    x = np.random.randn(1, 16, 96).astype(np.float32)
+    y = session.run(None, {"embeddings": x})[0]
+    assert y.shape == (1, 1)
+    assert 0.0 <= float(y[0, 0]) <= 1.0
+
+
+def test_run_export_format_accepts_enum_and_string(tmp_path: Path):
+    config = _make_checkpoint(tmp_path, ModelType.dnn)
+    assert run_export(config, format="onnx").suffix == ".onnx"
+    assert run_export(config, format=ExportFormat.onnx).suffix == ".onnx"
+
+
+def test_quantize_onnx_dnn(tmp_path: Path):
+    """Regression: dynamic quantization of a dynamo-exported Gemm head.
+
+    The torch dynamo exporter emits value_info for weight initializers; the ORT
+    dynamic quantizer transposes those weights (Gemm->MatMul) without updating it,
+    which previously crashed quantization with a ShapeInferenceError.
+    """
+    config = _make_checkpoint(tmp_path, ModelType.dnn)
+    out = run_export(config, quantize=True, format="onnx")
+    int8 = config.model_output_dir / "ww.int8.onnx"
+    assert int8.exists()
+
+    session = ort.InferenceSession(str(int8), providers=["CPUExecutionProvider"])
+    y = session.run(None, {"embeddings": np.random.randn(1, 16, 96).astype(np.float32)})[0]
+    assert y.shape == (1, 1)
+
+    # The source ONNX must not be mutated by quantization, and must still run.
+    src = ort.InferenceSession(str(out), providers=["CPUExecutionProvider"])
+    assert src.get_inputs()[0].name == "embeddings"
+
+
+def test_tflite_supported_heads():
+    assert ModelType.dnn in TFLITE_SUPPORTED_HEADS
+    ensure_tflite_supported(ModelType.dnn)  # does not raise
+
+
+@pytest.mark.parametrize("model_type", [ModelType.conv_attention, ModelType.rnn])
+def test_tflite_rejects_unsupported_heads(tmp_path: Path, model_type: ModelType):
+    """run_export(format=tflite) must fail fast for unsupported heads.
+
+    The guard fires before any checkpoint load or conversion, so this does not
+    require the optional tflite extra.
+    """
+    config = _make_checkpoint(tmp_path, model_type)
+    with pytest.raises(NotImplementedError, match="TFLite export is not supported"):
+        run_export(config, format="tflite")
+
+
+def test_tflite_export_dnn(tmp_path: Path):
+    """Full ONNX->TFLite conversion for the dnn head (skipped without the extra)."""
+    pytest.importorskip("onnx2tf")
+    tf = pytest.importorskip("tensorflow")
+
+    config = _make_checkpoint(tmp_path, ModelType.dnn)
+    out = run_export(config, format="tflite")
+    assert out.suffix == ".tflite" and out.exists()
+
+    interp = tf.lite.Interpreter(model_path=str(out))
+    interp.allocate_tensors()
+    in_det = interp.get_input_details()[0]
+    out_det = interp.get_output_details()[0]
+    # openWakeWord contract: static (1, 16, 96) input, (1, 1) output.
+    assert tuple(in_det["shape"]) == (1, 16, 96)
+    assert tuple(out_det["shape"]) == (1, 1)
+
+    # Numerical parity with the ONNX source.
+    x = np.random.randn(1, 16, 96).astype(np.float32)
+    interp.set_tensor(in_det["index"], x)
+    interp.invoke()
+    y_tflite = float(interp.get_tensor(out_det["index"])[0, 0])
+    session = ort.InferenceSession(
+        str(config.model_output_dir / "ww.onnx"), providers=["CPUExecutionProvider"]
+    )
+    y_onnx = float(session.run(None, {"embeddings": x})[0][0, 0])
+    assert abs(y_tflite - y_onnx) < 1e-4
+
+
+def test_tflite_export_dnn_quantized(tmp_path: Path):
+    """--quantize --format tflite must still satisfy the openWakeWord contract."""
+    pytest.importorskip("onnx2tf")
+    tf = pytest.importorskip("tensorflow")
+
+    config = _make_checkpoint(tmp_path, ModelType.dnn)
+    out = run_export(config, quantize=True, format="tflite")
+    assert out.suffix == ".tflite" and out.exists()
+
+    interp = tf.lite.Interpreter(model_path=str(out))
+    interp.allocate_tensors()
+    assert tuple(interp.get_input_details()[0]["shape"]) == (1, 16, 96)
+    assert tuple(interp.get_output_details()[0]["shape"]) == (1, 1)
+
+
+def test_export_tflite_missing_extra_message(tmp_path: Path):
+    """If onnx2tf is unavailable, export_tflite raises a clear install hint."""
+    try:
+        import onnx2tf  # noqa: F401
+    except ImportError:
+        with pytest.raises(ImportError, match="tflite"):
+            export_tflite(tmp_path / "missing.onnx", tmp_path / "out.tflite")
+    else:
+        pytest.skip("tflite extra installed; ImportError path not exercised")
diff --git a/uv.lock b/uv.lock
index 560ca72..fd26684 100644
--- a/uv.lock
+++ b/uv.lock
@@ -16,6 +16,15 @@ resolution-markers = [
     "python_full_version < '3.12' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
+[[package]]
+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
+]
+
 [[package]]
 name = "addict"
 version = "2.4.0"
@@ -25,6 +34,37 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc", size = 3832, upload-time = "2020-11-21T16:21:29.588Z" },
 ]
 
+[[package]]
+name = "ai-edge-litert"
+version = "2.1.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "backports-strenum" },
+    { name = "flatbuffers" },
+    { name = "numpy" },
+    { name = "protobuf" },
+    { name = "tqdm" },
+    { name = "typing-extensions" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e4/7f/ec543ef2f79cf5b5909521cb614162ec1e6fc9df863ccae23142260dd8c7/ai_edge_litert-2.1.5-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:cf9a6e98677d26fd900ec9459a19cf5cce9f94c64132d728485eeb9accd5c162", size = 9709620, upload-time = "2026-05-15T23:34:15.065Z" },
+    { url = "https://files.pythonhosted.org/packages/3b/84/a1ec0a9c6c345e79207b2a3a2a8e1e9572f412143100dc650f332c53d84f/ai_edge_litert-2.1.5-cp311-cp311-manylinux_2_27_aarch64.whl", hash = "sha256:1edf83d132919b0b95bb5ded64e79bfed018168d3584c28fa30a8eb0d3bfca6b", size = 12866358, upload-time = "2026-05-15T23:11:20.539Z" },
+    { url = "https://files.pythonhosted.org/packages/9a/44/3c365bc63b744316ef61c75761e53f34f9cdc0ba31fddc1d1039ca5322b4/ai_edge_litert-2.1.5-cp311-cp311-manylinux_2_27_x86_64.whl", hash = "sha256:7c54e45589831fbaeec1481df530e336d0112002a6193bc28f7aafa5f4898a3a", size = 17566472, upload-time = "2026-05-15T23:05:31.285Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/47/d3956aeddc00194463dcc83991ad58759b1a94672d66e7b7c5d2be1d95de/ai_edge_litert-2.1.5-cp311-cp311-win_amd64.whl", hash = "sha256:ad7844c7c2431dfe36debf6d4541f27af198848ed3090cbaacbdf2bb2cd377c7", size = 17753382, upload-time = "2026-05-15T23:39:22.178Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/37/cf525a4ed6aff573b10a162b7bff19673e50ce6f45daac0a095dcd099a28/ai_edge_litert-2.1.5-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:b62fd3d90e643bcc3e3a31885a7636b5662527d48c27154cf07793f26896f018", size = 9711976, upload-time = "2026-05-15T23:34:16.988Z" },
+    { url = "https://files.pythonhosted.org/packages/4a/6f/b43fc56831ecc439a6e6eb0e1bfdffbfe6213e8c706aa421017dce1ac56b/ai_edge_litert-2.1.5-cp312-cp312-manylinux_2_27_aarch64.whl", hash = "sha256:1d282722cdfb70bb42d457f4bfe7789f88187ccadd73fea9f0107dce6e98fe45", size = 12867617, upload-time = "2026-05-15T23:11:22.759Z" },
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+    { url = "https://files.pythonhosted.org/packages/82/fa/5399085daffd9949668d13a83a48db9f69af535ef9f9d06fdf71f0205321/ai_edge_litert-2.1.5-cp312-cp312-win_amd64.whl", hash = "sha256:6d2db6759188b12be9fd095468c3c7069c1bc7c128de78f42f862fb654d247ea", size = 17755006, upload-time = "2026-05-15T23:39:24.555Z" },
+    { url = "https://files.pythonhosted.org/packages/3d/fd/28f24305904954f31ee1a979eea39b670c6a1ab13cf6e5e4fe5f128e9eff/ai_edge_litert-2.1.5-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:2af16685a4cc8923d9a89ee5c732b8c66ae2c4d0cb538dbcebba3b93afb662ca", size = 9713185, upload-time = "2026-05-15T23:34:19.431Z" },
+    { url = "https://files.pythonhosted.org/packages/d3/88/e7e32b37f972b4877df788d1ad0995388f823d74dc2743b200bf71a25a53/ai_edge_litert-2.1.5-cp313-cp313-manylinux_2_27_aarch64.whl", hash = "sha256:57506e9e9b91ee04e4cf3e986d184b0c92f1aa9f510c32841835c0d719a7fbb5", size = 12867247, upload-time = "2026-05-15T23:11:24.667Z" },
+    { url = "https://files.pythonhosted.org/packages/7d/c1/5192c360a467dd6e09fa28388022c37bca5d9777e616da40dc5d517186a9/ai_edge_litert-2.1.5-cp313-cp313-manylinux_2_27_x86_64.whl", hash = "sha256:b7825e13454a90e7a4782ecdb6f6d987a2bcc9147aa90eb181193867bf696f24", size = 17570488, upload-time = "2026-05-15T23:05:36.078Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/ba/154599984a8eccbdd14bcd89fabc2e9cbdfcbf537a3c1ab08712c7ce7d02/ai_edge_litert-2.1.5-cp313-cp313-win_amd64.whl", hash = "sha256:602fc90f6baf396df0fe4b0317b35627305c2233beb48db194b79e7e420bafa7", size = 17755085, upload-time = "2026-05-15T23:39:26.901Z" },
+    { url = "https://files.pythonhosted.org/packages/5e/51/34f0ad0def8b15093d602ca6904b129d3ab242ae4711c872190d5c95c010/ai_edge_litert-2.1.5-cp314-cp314-macosx_12_0_arm64.whl", hash = "sha256:597ea79f947452c79ca7e9dd7abfedad6179302851df4a4bb4c755069a2f5ffe", size = 9712496, upload-time = "2026-05-15T23:34:21.36Z" },
+    { url = "https://files.pythonhosted.org/packages/42/43/2b8ef317dca3096372cbf820aa5a3a6c8857dad67f31840a1e17a951e2d3/ai_edge_litert-2.1.5-cp314-cp314-manylinux_2_27_aarch64.whl", hash = "sha256:41d2e8267f047ed14f14a5ff4e0f677a9c5143dd17a36991513f8cc5ef580a3b", size = 12867899, upload-time = "2026-05-15T23:11:26.584Z" },
+    { url = "https://files.pythonhosted.org/packages/c2/d5/9640952de755409c84e09eec5d107675c59fe4f4859391464275eb2c3db6/ai_edge_litert-2.1.5-cp314-cp314-manylinux_2_27_x86_64.whl", hash = "sha256:4fa10d99b2f8647678850684d31075fbb304f50535d5788c2ca93e273ce727a8", size = 17570849, upload-time = "2026-05-15T23:05:37.981Z" },
+    { url = "https://files.pythonhosted.org/packages/0d/d4/0843e5a41bf71eb99ccd1e54714485df527bec19e8c8e679e1395176345d/ai_edge_litert-2.1.5-cp314-cp314-win_amd64.whl", hash = "sha256:14b35558bfce76146046cc2fc647f3fde97146e8b7622ea7fb02e85ac16cd906", size = 18380722, upload-time = "2026-05-15T23:39:29.365Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -227,6 +267,19 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/8e/25/4422cf7777c0cedd2fbcbdf8face786be76186e6d2e7b66f5f37e266b604/argbind-0.3.9.tar.gz", hash = "sha256:1b159c04af56858a91d59c7a47bc9ea39d96adfce1d7fcfa38050d7ac9815745", size = 17567, upload-time = "2024-05-24T21:33:21.281Z" }
 
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+    { name = "wheel" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f3/af/4182184d3c338792894f34a62672919db7ca008c89abee9b564dd34d8029/astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872", size = 18290, upload-time = "2019-12-22T18:12:13.129Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8", size = 12732, upload-time = "2019-12-22T18:12:11.297Z" },
+]
+
 [[package]]
 name = "attrs"
 version = "26.1.0"
@@ -323,6 +376,15 @@ wheels = [
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+[[package]]
+name = "backports-strenum"
+version = "1.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/35/c7/2ed54c32fed313591ffb21edbd48db71e68827d43a61938e5a0bc2b6ec91/backports_strenum-1.3.1.tar.gz", hash = "sha256:77c52407342898497714f0596e86188bb7084f89063226f4ba66863482f42414", size = 7257, upload-time = "2023-12-09T14:36:40.937Z" }
+wheels = [
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+]
+
 [[package]]
 name = "brotli"
 version = "1.2.0"
@@ -824,7 +886,7 @@ name = "cuda-bindings"
 version = "12.9.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder" },
+    { name = "cuda-pathfinder", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
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+[[package]]
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+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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+]
+
+[[package]]
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+version = "0.2.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+]
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+    { name = "onnx-graphsurgeon" },
+    { name = "onnx2tf" },
+    { name = "psutil" },
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+    { name = "tensorflow" },
+    { name = "tf-keras" },
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 source = { registry = "https://pypi.org/simple" }
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