Add an --export_quantize flag to control TFLite export quantization

training/coqui_stt_training/train.py

@@ -1116,7 +1116,10 @@ def export():
                 input_tensors=inputs.values(),
                 output_tensors=outputs.values(),
             )
+
+            if Config.export_quantize:
                 converter.optimizations = [tf.lite.Optimize.DEFAULT]
+
             # AudioSpectrogram and Mfcc ops are custom but have built-in kernels in TFLite
             converter.allow_custom_ops = True
             tflite_model = converter.convert()

training/coqui_stt_training/util/config.py

@@ -22,7 +22,7 @@ class _ConfigSingleton:
     _config = None
 
     def __getattr__(self, name):
-        if not _ConfigSingleton._config:
+        if _ConfigSingleton._config is None:
             raise RuntimeError("Global configuration not yet initialized.")
         if not hasattr(_ConfigSingleton._config, name):
             raise RuntimeError(
@@ -478,6 +478,10 @@ class _SttConfig(Coqpit):
     export_tflite: bool = field(
         default=False, metadata=dict(help="export a graph ready for TF Lite engine")
     )
+    export_quantize: bool = field(
+        default=True,
+        metadata=dict(help="export a quantized model (optimized for size)"),
+    )
     n_steps: int = field(
         default=16,
         metadata=dict(