torchrir 0.4.1

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.

[!WARNING] TorchRIR is under active development and may contain bugs or breaking changes. Please validate results for your use case. If you find bugs or have feature requests, please open an issue. Contributions are welcome.

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: torchrir.sim.simulate_rir
• Dynamic RIR: torchrir.sim.simulate_dynamic_rir
• Dynamic convolution: torchrir.signal.DynamicConvolver
• Metadata export: torchrir.io.build_metadata, torchrir.io.save_metadata_json

Module Layout (for contributors)

• torchrir.sim: simulation backends (ISM implementation lives under torchrir.sim.ism)
• torchrir.signal: convolution utilities and dynamic convolver
• torchrir.geometry: array geometries, sampling, trajectories
• torchrir.viz: plotting and animation helpers
• torchrir.models: room/scene/result data models
• torchrir.io: audio I/O and metadata serialization (wav-only load/save/info with backend selection)
• torchrir.util: shared math/tensor/device helpers
• torchrir.logging: logging utilities
• torchrir.config: simulation configuration objects

from torchrir import MicrophoneArray, Room, Source
from torchrir.sim import simulate_rir
from torchrir.signal import DynamicConvolver

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 torchrir.sim.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 torchrir.experimental.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 torchrir.experimental.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
• pyrato