feat: add Apple Silicon (MPS) support for macOS ARM64

[jasagiri]

Summary

• Introduce a device abstraction layer (cosyvoice/utils/device.py) that unifies CUDA, MPS (Apple Silicon), and CPU device management
• Replace all hardcoded CUDA-specific code paths in the inference pipeline with device-agnostic alternatives
• Enable CosyVoice to run natively on Apple Silicon Macs (M1/M2/M3/M4) via PyTorch MPS backend

Changes

New files

• cosyvoice/utils/device.py — Unified device detection (get_device()), stream context, autocast, cache management, and random seed utilities
• requirements-cuda.txt — Separated CUDA-specific PyPI index URLs for Linux GPU environments
• setup_macos.sh — One-command setup script for Apple Silicon

Modified files

• cosyvoice/cli/model.py — Replace CUDA device init, streams (torch.cuda.stream), AMP (torch.cuda.amp.autocast), and cache clearing across CosyVoiceModel, CosyVoice2Model, CosyVoice3Model
• cosyvoice/cli/cosyvoice.py — MPS-aware feature gates: TRT/vLLM require CUDA, JIT/fp16 work on any GPU including MPS
• cosyvoice/cli/frontend.py — Add CoreMLExecutionProvider fallback for ONNX Runtime on Apple Silicon
• cosyvoice/utils/common.py — Guard torch.cuda.manual_seed_all for non-CUDA environments
• requirements.txt — Remove CUDA-only index URLs, loosen PyTorch version pin (>=2.3.1)
• README.md — Add macOS Apple Silicon setup instructions

Design decisions

• Device priority: cuda > mps > cpu — CUDA environments are unaffected
• TensorRT/vLLM: Remain CUDA-only (no ARM64 builds exist) — gracefully disabled with warning on MPS
• JIT/fp16: Enabled on MPS since PyTorch MPS supports both
• Training: Out of scope — DeepSpeed/DDP do not support MPS. This PR focuses on inference only
• Zero behavioral change on CUDA: All abstractions are transparent passthrough when CUDA is available

Platform support matrix

Feature	CUDA	MPS (Apple Silicon)	CPU
Inference	✅	✅	✅
fp16	✅	✅	❌
JIT	✅	✅	❌
TensorRT	✅	❌	❌
vLLM	✅	❌	❌
Training	✅	❌	❌

Test plan

• device.py: All functions tested on MPS (device detection, stream context, autocast with float16, cache clear, seed)
• common.py: set_all_random_seed() does not crash without CUDA; fade_in_out() works on MPS tensors
• model.py: All 3 model classes import correctly; no hardcoded CUDA references remain (except intentional load_trt assert)
• cosyvoice.py: Feature gates correctly disable TRT/vLLM on MPS while keeping JIT/fp16
• frontend.py: Device abstraction and CoreML provider fallback verified
• Clean clone test: All checks pass from fresh git clone
• End-to-end inference with model weights (requires pretrained model download)

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