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.

pocket_tts/models/tts_model.py

@@ -33,6 +33,7 @@ from pocket_tts.modules import mimi_transformer
 from pocket_tts.modules.dummy_quantizer import DummyQuantizer
 from pocket_tts.modules.seanet import SEANetDecoder, SEANetEncoder
 from pocket_tts.modules.stateful_module import StatefulModule, increment_steps, init_states
+from pocket_tts.quantization import RECOMMENDED_CONFIG, apply_dynamic_int8
 from pocket_tts.utils.config import Config, load_config
 from pocket_tts.utils.utils import (
     PREDEFINED_VOICES,
@@ -192,6 +193,7 @@ class TTSModel(nn.Module):
         lsd_decode_steps: int = DEFAULT_LSD_DECODE_STEPS,
         noise_clamp: float | int | None = DEFAULT_NOISE_CLAMP,
         eos_threshold: float = DEFAULT_EOS_THRESHOLD,
+        quantize: bool = False,
     ) -> Self:
         """Load a pre-trained TTS model with specified configuration.
 
@@ -210,6 +212,11 @@ class TTSModel(nn.Module):
                 is applied. Helps prevent extreme values in generation.
             eos_threshold: Threshold for end-of-sequence detection. Higher values
                 make the model more likely to continue generating.
+            quantize: If True, apply dynamic int8 quantization to the transformer's
+                attention and FFN layers. Reduces runtime memory by ~48% and improves
+                inference speed by ~27% on x86 (FBGEMM).
+                No measurable impact on speech quality (WER unchanged).
+                For optimized performance, install torchao: ``pip install pocket-tts[quantize]``
 
         Returns:
             TTSModel: Fully initialized model with loaded weights on cpu, ready for
@@ -227,8 +234,8 @@ class TTSModel(nn.Module):
             # Load with default settings
             model = TTSModel.load_model()
 
-            # Load with custom parameters
-            model = TTSModel.load_model(variant="b6369a24", temp=0.5, lsd_decode_steps=5, eos_threshold=-3.0)
+            # Load with int8 quantization
+            model = TTSModel.load_model(quantize=True)
             ```
         """
         if str(config).endswith(".yaml"):
@@ -241,6 +248,10 @@ class TTSModel(nn.Module):
         tts_model = TTSModel._from_pydantic_config_with_weights(
             config, temp, lsd_decode_steps, noise_clamp, eos_threshold
         )
+
+        if quantize:
+            apply_dynamic_int8(tts_model.flow_lm, RECOMMENDED_CONFIG)
+
         return tts_model
 
     def _run_flow_lm_and_increment_step(