modify fx norm

inference.py

@@ -165,7 +165,7 @@ class MasteringStyleTransfer:
             return "No parameters available"
         
         param_mapper = {
-            'EQ': {
+            'eq': {
                 'low_shelf_gain_db': ('Low Shelf Gain', 'dB', -20, 20),
                 'low_shelf_cutoff_freq': ('Low Shelf Cutoff', 'Hz', 20, 2000),
                 'low_shelf_q_factor': ('Low Shelf Q', '', 0.1, 5.0),
@@ -185,11 +185,11 @@ class MasteringStyleTransfer:
                 'high_shelf_cutoff_freq': ('High Shelf Cutoff', 'Hz', 4000, 20000),  # Assuming sample_rate is 44100
                 'high_shelf_q_factor': ('High Shelf Q', '', 0.1, 5.0),
             },
-            'DISTORTION': {
+            'distortion': {
                 'drive_db': ('Drive', 'dB', 0, 8),
                 'parallel_weight_factor': ('Dry/Wet Mix', '%', 0, 100),
             },
-            'MULTIBAND_COMP': {
+            'multiband_comp': {
                 'low_cutoff': ('Low/Mid Crossover', 'Hz', 20, 1000),
                 'high_cutoff': ('Mid/High Crossover', 'Hz', 1000, 20000),
                 'parallel_weight_factor': ('Dry/Wet Mix', '%', 0, 100),
@@ -212,13 +212,13 @@ class MasteringStyleTransfer:
                 'high_shelf_at': ('High Band Attack Time', 'ms', 5, 100),
                 'high_shelf_rt': ('High Band Release Time', 'ms', 5, 100),
             },
-            'GAIN': {
+            'gain': {
                 'gain_db': ('Output Gain', 'dB', -24, 24),
             },
-            'IMAGER': {
+            'imager': {
                 'width': ('Stereo Width', '', 0, 1),
             },
-            'LIMITER': {
+            'limiter': {
                 'threshold': ('Threshold', 'dB', -60, 0),
                 'at': ('Attack Time', 'ms', 5, 100),
                 'rt': ('Release Time', 'ms', 5, 100),
@@ -227,7 +227,7 @@ class MasteringStyleTransfer:
         
         output = []
         for fx_name, fx_params in params.items():
-            output.append(f"{fx_name}:")
+            output.append(f"{fx_name.upper()}:")
             if isinstance(fx_params, dict):
                 for param_name, param_value in fx_params.items():
                     if isinstance(param_value, torch.Tensor):