Add int8 dynamic quantization support (#147)

# Add int8 dynamic quantization
 
Closes #7
 
## Summary
 
This PR implements mixed-precision int8 dynamic quantization for
pocket-tts, as requested in issue #7. It adds a `quantize=True` flag to
`TTSModel.load_model()` that reduces runtime memory usage by ~48% and
improves inference speed by ~27% on x86 and up to 23% on ARM, with no
measurable impact on audio quality.

docs/quantization.md

@@ -0,0 +1,76 @@
+# Quantization
+
+Pocket TTS supports dynamic int8 quantization to reduce runtime memory usage and improve inference speed on x86 CPUs.
+
+## Quick Start
+
+### CLI
+
+```bash
+pocket-tts generate --quantize --text "Hello world"
+pocket-tts serve --quantize
+```
+
+### Python API
+
+```python
+from pocket_tts import TTSModel
+
+model = TTSModel.load_model(quantize=True)
+voice_state = model.get_state_for_audio_prompt("alba")
+audio = model.generate_audio(voice_state, "Hello world!")
+```
+
+## Installation
+
+Quantization works out of the box on any supported PyTorch version (2.5+) using `torch.ao`.
+
+For optimized performance, install `torchao` (requires torch 2.10+):
+
+```bash
+pip install pocket-tts[quantize]
+```
+
+The quantization module automatically selects the best available backend:
+
+- **torchao** (torch 2.10+): optimized C++ kernels, faster on both ARM and x86
+- **torch.ao** (torch 2.5-2.9): deprecated but functional fallback
+
+## Performance
+
+Benchmarks on the full eval paragraph across 8 voices, 5 isolated runs per config.
+
+### x86 (FBGEMM, ubuntu-latest GitHub Actions runner)
+
+| Config                      | Runtime Memory | RTS       | Speedup vs Baseline |
+| --------------------------- | -------------- | --------- | ------------------- |
+| baseline                    | 450 MB         | 3.17x     | --                  |
+| **attention_ffn (default)** | **234 MB**     | **4.04x** | **1.27x**           |
+| all                         | 206 MB         | 4.01x     | 1.26x               |
+
+### ARM (QNNPACK, Apple M4 MacBook Air, torchao backend)
+
+| Config                      | Runtime Memory | RTS       | Speedup vs Baseline |
+| --------------------------- | -------------- | --------- | ------------------- |
+| baseline                    | 450 MB         | 6.33x     | --                  |
+| **attention_ffn (default)** | **234 MB**     | **7.76x** | **1.23x**           |
+
+With the `torch.ao` fallback (torch 2.5-2.9), ARM performance is ~16% slower than baseline rather than faster. Upgrading to torch 2.10+ with torchao is recommended for ARM users.
+
+## Quality
+
+Quantization has no measurable impact on speech quality:
+
+- **WER (Word Error Rate)**: WER delta for the default attention_ffn config is −0.022 ±0.032 - the ± range crosses zero, meaning the delta is indistinguishable from measurement noise.
+- **Subjective listening**: no audible difference across all 8 voices
+
+## What gets quantized
+
+When `quantize=True`, the following layer groups in the FlowLM transformer are quantized to int8:
+
+| Group       | Params | Description                                      |
+| ----------- | ------ | ------------------------------------------------ |
+| `attention` | ~25M   | Q/K/V/output projections in transformer layers   |
+| `ffn`       | ~50M   | Feed-forward linear layers in transformer layers |
+
+The flow matching network (`flow_net`, ~7M params) and the Mimi VAE decoder (convolutional) remain in float32.