example-models

This repository contains example models for the anira library, designed for validation and demonstration purposes.

Content

• SimpleGainNetwork: This folder contains an example model, designed for multichannel audio inference. It includes:

  • Inputs:
    • Audio data: A multichannel input array representing the audio signal. Models are generated for 1 (mono), 2 (stereo), 4 and 16 channels.
    • Gain parameter: A parameter to apply gain (amplification) to the input audio.
  • Outputs:
    • Processed audio: The input audio after applying the gain.
    • Peak gain: The maximum absolute gain measured across all channels.
  • Formats: Each channel count is exported to TorchScript (.pt), ONNX (.onnx), TFLite (.tflite), and ExecuTorch (.pte). The build script cross-checks that all four runtimes produce consistent outputs.

• StatefulAccumulatorNetwork: This folder contains an example model with explicit state, designed to validate state passing: the state is an ordinary input tensor and an ordinary output tensor, and the caller feeds state_out back as the next state_in. The model itself keeps nothing between two calls, so the same model runs in every runtime (none of them needs a hidden state) and the feedback is observable from outside. It has no weights and no randomness. It includes:

  • Inputs, in this order:
    • state_in: float32 [1, C, 2]. Per channel c, state_in[0, c, 0] is the running sum and state_in[0, c, 1] a block counter. It starts as zeros.
    • data: float32 [1, C, N], the audio block. N is dynamic. Models are generated for C = 1 (mono) and C = 2 (stereo).
  • Outputs, in this order:
    • processed_data: float32 [1, C, N], with processed_data[0, c, n] = data[0, c, n] + state_in[0, c, 0].
    • state_out: float32 [1, C, 2], with state_out[0, c, 0] = state_in[0, c, 0] + sum_n data[0, c, n] and state_out[0, c, 1] = state_in[0, c, 1] + 1.
  • State position: The state is deliberately the first input and the last output, because state tensors may sit anywhere in a model's tensor order.
  • Expectation: With the state fed back, the output of block k is its input plus the sum of every sample of the blocks 0..k-1 of the same channel, and the counter of state_out reads k + 1. Without feedback (zero state at every block) the output equals the input. With small integer-valued inputs every value is exact in float32, so both hold bit-exactly, and a stale or zeroed state is visible in the samples.
  • Tensor order per format: TorchScript, ONNX and ExecuTorch keep the order above; the ONNX tensor names are exactly state_in, data, processed_data, state_out. The converted TFLite files keep it on the input side only. By position (get_input_details() / get_output_details(), which is what a consumer that binds TFLite tensors by position sees, as anira does), mono and stereo alike:
    • input 0: state_in (serving_default_args_0:0, [1, C, 2]), input 1: data (serving_default_args_0_1:0, [1, C, -1])
    • output 0: state_out (PartitionedCall:1, [1, C, 2]), output 1: processed_data (PartitionedCall:0, [1, C, -1])
    • The serving_default signature keeps the declared mapping: args_0 = state_in, args_0_1 = data, output_0 = processed_data, output_1 = state_out.
  • Formats: Each channel count is exported to TorchScript (.pt), ONNX (.onnx), TFLite (.tflite), and ExecuTorch (.pte). The build script prints the tensor order of the ONNX and TFLite files, then runs the saved TorchScript trace and every export with N = 10 and N = 64 over three consecutive blocks, with the state fed back and without, and asserts the expectation above with exact equality.

• RaveDjembe: A RAVE neural audio codec (v1 architecture, 44.1 kHz mono, trained on djembe) ported from its TorchScript streaming export to ONNX Runtime, LiteRT/TFLite and ExecuTorch with all streaming state passed as explicit input/output tensors, so stateless runtimes can run real-time block-by-block inference. Ships separate encoder / decoder / forward models per format, parity tests against golden reference vectors, and the full export pipeline. See third-party/ircam-acids/RAVE/RaveDjembe/README.md.

To (re)generate the SimpleGainNetwork models:

pip install -r requirements.txt
python SimpleGainNetwork/simple_gain_network.py

By default this builds the (1, 2, 4, 16) channel set; edit the channel_counts argument to main() to change it.

To (re)generate the StatefulAccumulatorNetwork models:

pip install -r requirements.txt
python StatefulAccumulatorNetwork/stateful_accumulator_network.py

By default this builds the (1, 2) channel set. The .pte export is skipped when executorch is not installed. The committed files were produced and verified with Python 3.12.14 on Linux aarch64, with torch 2.14.0 (CPU build), onnx 1.23.0, onnxruntime 1.30.0, onnxscript 0.7.2, tensorflow 2.21.0 (its bundled TFLite converter and interpreter), executorch 1.5.0 and numpy 2.5.3.