diff --git a/examples/custom_model.yml b/examples/custom_model.yml
index a41d327..3e7dae1 100644
--- a/examples/custom_model.yml
+++ b/examples/custom_model.yml
@@ -32,6 +32,20 @@ piper_sample_generator_path: "./piper-sample-generator"
 # Sub-directories will be automatically created for train and test clips for both positive and negative examples
 output_dir: "./my_custom_model"
 
+# The path to model used by piper_sample_generator
+generator_model: "./piper-sample-generator/models/en_US-libritts_r-medium.pt"
+
+# Min phoneme count for piper_sample_generator
+min_phoneme_count: null
+
+# Noise setting for trainning samples
+noise_scales_train:
+  - 0.98
+
+# Noise setting for testing samples
+noise_scales_test:
+  - 1.0
+
 # The directories containing Room Impulse Response recordings
 rir_paths:
   - "./mit_rirs"
diff --git a/openwakeword/train.py b/openwakeword/train.py
index f564254..8c291e8 100755
--- a/openwakeword/train.py
+++ b/openwakeword/train.py
@@ -669,7 +669,9 @@ def convert_onnx_to_tflite(onnx_model_path, output_path):
             generate_samples(
                 text=config["target_phrase"], max_samples=config["n_samples"]-n_current_samples,
                 batch_size=config["tts_batch_size"],
-                noise_scales=[0.98], noise_scale_ws=[0.98], length_scales=[0.75, 1.0, 1.25],
+                model = config["generator_model"],
+                min_phoneme_count=config["min_phoneme_count"], 
+                noise_scales=config["noise_scales_train"], noise_scale_ws=config["noise_scales_train"], length_scales=[0.75, 1.0, 1.25],
                 output_dir=positive_train_output_dir, auto_reduce_batch_size=True,
                 file_names=[uuid.uuid4().hex + ".wav" for i in range(config["n_samples"])]
             )
@@ -685,7 +687,9 @@ def convert_onnx_to_tflite(onnx_model_path, output_path):
         if n_current_samples <= 0.95*config["n_samples_val"]:
             generate_samples(text=config["target_phrase"], max_samples=config["n_samples_val"]-n_current_samples,
                              batch_size=config["tts_batch_size"],
-                             noise_scales=[1.0], noise_scale_ws=[1.0], length_scales=[0.75, 1.0, 1.25],
+                             model = config["generator_model"],
+                             min_phoneme_count=config["min_phoneme_count"], 
+                             noise_scales=config["noise_scales_test"], noise_scale_ws=config["noise_scales_test"], length_scales=[0.75, 1.0, 1.25],
                              output_dir=positive_test_output_dir, auto_reduce_batch_size=True)
             torch.cuda.empty_cache()
         else:
@@ -706,7 +710,11 @@ def convert_onnx_to_tflite(onnx_model_path, output_path):
                     include_input_words=0.2))
             generate_samples(text=adversarial_texts, max_samples=config["n_samples"]-n_current_samples,
                              batch_size=config["tts_batch_size"]//7,
-                             noise_scales=[0.98], noise_scale_ws=[0.98], length_scales=[0.75, 1.0, 1.25],
+                             model = config["generator_model"],
+                             min_phoneme_count=config["min_phoneme_count"], 
+                             noise_scales=config["noise_scales_train"],
+                             noise_scale_ws=config["noise_scales_train"],
+                             length_scales=[0.75, 1.0, 1.25],
                              output_dir=negative_train_output_dir, auto_reduce_batch_size=True,
                              file_names=[uuid.uuid4().hex + ".wav" for i in range(config["n_samples"])]
                              )
@@ -729,7 +737,11 @@ def convert_onnx_to_tflite(onnx_model_path, output_path):
                     include_input_words=0.2))
             generate_samples(text=adversarial_texts, max_samples=config["n_samples_val"]-n_current_samples,
                              batch_size=config["tts_batch_size"]//7,
-                             noise_scales=[1.0], noise_scale_ws=[1.0], length_scales=[0.75, 1.0, 1.25],
+                             model = config["generator_model"],
+                             min_phoneme_count=config["min_phoneme_count"], 
+                             noise_scales=config["noise_scales_test"],
+                             noise_scale_ws=config["noise_scales_test"],
+                             length_scales=[0.75, 1.0, 1.25],
                              output_dir=negative_test_output_dir, auto_reduce_batch_size=True)
             torch.cuda.empty_cache()
         else: