* feat: update Python version requirement to be compatible with 3.13
* feat: add Python 3.13 to compatibility matrix
* feat: update onnxruntime dependency for Python version compatibility
* fix: drop onnxruntime restriction for python >= 3.10
- feat: upgrade PyTorch to 2.7.1 and CUDA 12.8
* Update README setup to require CUDA toolkit 12.8 instead of 12.4 (Linux and Windows)
* Bump torch dependency from 2.6.0 to 2.7.1
* Switch the PyTorch CUDA wheel index from cu124 to cu128
- Revert "docs: add troubleshooting section for libcudnn dependencies in README"
* The issue of relying on two different versions of CUDNN in this project has been resolved.
- build(pyproject): relax python version and constrain package deps
* Only download torch from PyTorch; obtain all other packages from PyPI.
* Restrict numpy, onnxruntime, pandas to be compatible with Python 3.9
- build(pyproject): require triton 3.3.0+ for arm64 support
* Add triton version 3.3.0 or newer to the dependencies to support arm64 architecture.
- build: skip Triton on Windows since it isn't supported
* Add a platform marker to the triton dependency to skip it on Windows, as triton does not support Windows.
- build: configure PyTorch sources for cross-platform compatibility
* macOS uses CPU-only PyTorch from pytorch-cpu index
* Linux and Windows use CUDA 12.8 PyTorch from pytorch index
* triton only installs on Linux with CUDA 12.8 support
* Update lockfile to support multi-platform builds
- fix: restrict av to <16.0.0 for Python 3.9 compatibility
* Add av<16.0.0 to dependencies to maintain Python 3.9 support
* Update comment to include av in the restriction list
* Update uv.lock accordingly
PyAV dropped Python 3.9 support in v16.0.0:
106089447c
- fix: resolve PyTorch ARM64 platform compatibility issue
* Update uv.lock to properly handle aarch64 platforms for PyTorch dependencies
* Add resolution markers for ARM64 Linux systems to use CPU-only PyTorch builds
* Ensure CUDA builds are only used on x86_64 platforms where supported
Resolves ARM64 Docker build failures by preventing uv from attempting to install CUDA PyTorch on unsupported platforms
- chore: change .python-version to 3.10
---
Signed-off-by: CHEN, CHUN <jim60105@gmail.com>
Signed-off-by: Jim Chen <Jim@ChenJ.im>
Co-authored-by: GitHub Copilot <bot@ChenJ.im>
Since the current model is a wav2vec2 pre-trained model for Vietnamese audio, it won't work with alignment tasks. To make it work as expected, I recommend chaining to a fine-tuned ASR version.
fix(asr): load VAD model on correct CUDA device
Previously, the VAD sub‐model was always initialized on the default CUDA device (cuda:0), even when a higher device_index was specified. This change sets `device_vad` to `cuda:{device_index}` whenever `device == 'cuda'`, while falling back to the original `device` string for non‐CUDA cases. This ensures the VAD model is loaded on the intended GPU.
Co-authored-by: dujing <dujing@xmov.ai>
Co-authored-by: Barabazs <31799121+Barabazs@users.noreply.github.com>
- Updated DiarizationPipeline to include a return_embeddings parameter for optional speaker embeddings.
- Modified assign_word_speakers to accept and process speaker embeddings.
- Updated CLI to support --speaker_embeddings flag for JSON output.
- Ensured backward compatibility for existing functionality.
