torchrir 0.2.0

PyTorch-based room impulse response (RIR) simulation toolkit for static and dynamic scenes.

TorchRIR

PyTorch-based room impulse response (RIR) simulation toolkit focused on a clean, modern API with GPU support. This project has been substantially assisted by AI using Codex.

Installation

pip install torchrir

Examples

• examples/static.py: fixed sources/mics with binaural output.
  uv run python examples/static.py --plot
• examples/dynamic_src.py: moving sources, fixed mics.
  uv run python examples/dynamic_src.py --plot
• examples/dynamic_mic.py: fixed sources, moving mics.
  uv run python examples/dynamic_mic.py --plot
• examples/cli.py: unified CLI for static/dynamic scenes, JSON/YAML configs.
  uv run python examples/cli.py --mode static --plot
• examples/cmu_arctic_dynamic_dataset.py: small dynamic dataset generator (fixed room/mics, randomized source motion).
  uv run python examples/cmu_arctic_dynamic_dataset.py --num-scenes 4 --num-sources 2
• examples/benchmark_device.py: CPU/GPU benchmark for RIR simulation.
  uv run python examples/benchmark_device.py --dynamic

Core API Overview

• Geometry: Room, Source, MicrophoneArray
• Static RIR: simulate_rir
• Dynamic RIR: simulate_dynamic_rir
• Dynamic convolution: DynamicConvolver
• Metadata export: build_metadata, save_metadata_json

from torchrir import DynamicConvolver, MicrophoneArray, Room, Source, simulate_rir

room = Room.shoebox(size=[6.0, 4.0, 3.0], fs=16000, beta=[0.9] * 6)
sources = Source.from_positions([[1.0, 2.0, 1.5]])
mics = MicrophoneArray.from_positions([[2.0, 2.0, 1.5]])

rir = simulate_rir(room=room, sources=sources, mics=mics, max_order=6, tmax=0.3)
# For dynamic scenes, compute rirs with simulate_dynamic_rir and convolve:
# y = DynamicConvolver(mode="trajectory").convolve(signal, rirs)

For detailed documentation, see the docs under docs/ and Read the Docs.

Future Work

• Ray tracing backend: implement RayTracingSimulator with frequency-dependent absorption/scattering.
• CUDA-native acceleration: introduce dedicated CUDA kernels for large-scale RIR generation.
• Dataset expansion: add additional dataset integrations beyond CMU ARCTIC (see TemplateDataset), including torchaudio datasets (e.g., LibriSpeech, VCTK, LibriTTS, SpeechCommands, CommonVoice, GTZAN, MUSDB-HQ).
• Add regression tests comparing generated RIRs against gpuRIR outputs.

Related Libraries

• gpuRIR
• Cross3D
• pyroomacoustics
• das-generator
• rir-generator