VieNeu-TTS is an advanced on-device Vietnamese text-to-speech library with instant voice cloning, supporting bilingual (Vietnamese/English) synthesis. The system provides a Python SDK (`vieneu`) with multiple backends: v3 Turbo (default, 48kHz, ONNX or PyTorch), v3 Nano (lightweight 24kHz, ONNX only), and legacy v1/v2 (deprecated). It includes a Gradio web UI (`vieneu-web`), a FastAPI streaming server, and a browser-based HTML/JS client. The core architecture is a factory pattern (`Vieneu(mode=...)`) that returns the appropriate engine, all inheriting from a shared base class. Inference can run entirely torch-free on CPU via ONNX Runtime, with optional GPU acceleration (CUDA) for batched generation and lower latency. Feature highlights include instant voice cloning from a 3β8 second reference clip using a speaker encoder (ONNX) and denoiser, inline emotion cues (`[cΖ°α»i]`, `[thα» dΓ i]`), frame-level streaming, SRT subtitle-to-speech, and LoRA fine-tuning for custom voices (requires ~6GB GPU). The system is deployed via Docker with CPU and GPU profiles, and has CI/CD pipelines for testing and PyPI publishing. The codebase is well-structured with clear separation of concerns, though it carries some legacy weight from older model versions and a few deprecated parameters. Other notable components include a sliding-window repetition penalty, a babble guard heuristic for short utterances, and a voice persistence system for user-defined presets.
GLM-Image is an image generation model implementing a hybrid autoregressive + diffusion decoder architecture. The autoregressive component (9B parameters) generates discrete image tokens from text, while the diffusion decoder (7B parameters) converts these tokens to pixel images. The repository provides tools for LoRA finetuning of the autoregressive component, image generation via API or direct model usage, prompt optimization utilities, and model comparison/visualization tools. Entry points include finetune_lora.py for training, glm_image_cli.py for API-based generation, and compare.py/plot_loss.py for evaluation and monitoring.
PMetal is a comprehensive machine learning framework specifically optimized for Apple Silicon hardware. It provides a complete suite for LLM training, fine-tuning, inference, and deployment through multiple interfaces (CLI, TUI, GUI, Python SDK, and REST API). The framework leverages Apple-specific technologies like Metal GPU, ANE, and UltraFusion for performance optimization while supporting standard ML workflows including LoRA/QLoRA fine-tuning, knowledge distillation, model merging, and quantization to formats like GGUF. The architecture follows a modular workspace design with over 20 specialized crates handling different aspects of the ML lifecycle, unified through a job specification system and configuration framework.