diff --git a/tests/test_metric.py b/tests/test_metric.py
index 6d4b036..5f769c3 100644
--- a/tests/test_metric.py
+++ b/tests/test_metric.py
@@ -89,7 +89,7 @@ def test_forward_silence(self):
 
                     # Generate random inputs (scale between -1 and 1)
                     audio_lengths_samples = int(audio_length * sample_rate)
-                    unprocessed_audio = torch.zeros(batch, audio_channels, audio_lengths_samples)
+                    unprocessed_audio = torch.rand(batch, audio_channels, audio_lengths_samples) * convert_decibels_to_amplitude_ratio(-75)
                     processed_audio = torch.rand(batch, audio_stems, audio_channels, audio_lengths_samples) * convert_decibels_to_amplitude_ratio(-60)
                     target_audio = torch.zeros(batch, audio_stems, audio_channels, audio_lengths_samples)
 
diff --git a/torch_log_wmse_audio_quality/metric.py b/torch_log_wmse_audio_quality/metric.py
index 41074d0..bcbc665 100644
--- a/torch_log_wmse_audio_quality/metric.py
+++ b/torch_log_wmse_audio_quality/metric.py
@@ -95,8 +95,8 @@ def _calculate_log_wmse(
             Tensor: The logWMSE between the processed audio and target audio.
         """
 
-        # Add EPS if input_rms is 0 (silence) to avoid NaNs
-        if input_rms.sum() == 0:
+        # Add EPS if input_rms is 0 (silence), or close to it, to avoid NaNs
+        if input_rms.sum() < ERROR_TOLERANCE_THRESHOLD:
             input_rms = torch.ones_like(input_rms) * ERROR_TOLERANCE_THRESHOLD
 
         # Calculate the scaling factor based on the input RMS