From 78f03e7dc0563e438307ff62c76a062b46083ec4 Mon Sep 17 00:00:00 2001
From: Blaise <133521603+blaise-tk@users.noreply.github.com>
Date: Fri, 22 Dec 2023 02:35:51 +0100
Subject: [PATCH] Fix return_complex warning on training (#1627)

* Fix return_complex warning on training

* remove unused prints
---
 infer/lib/train/mel_processing.py | 19 ++++++-------------
 1 file changed, 6 insertions(+), 13 deletions(-)

diff --git a/infer/lib/train/mel_processing.py b/infer/lib/train/mel_processing.py
index 04a11f1..14a960f 100644
--- a/infer/lib/train/mel_processing.py
+++ b/infer/lib/train/mel_processing.py
@@ -38,7 +38,6 @@ def spectral_de_normalize_torch(magnitudes):
 mel_basis = {}
 hann_window = {}
 
-
 def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
     """Convert waveform into Linear-frequency Linear-amplitude spectrogram.
 
@@ -52,12 +51,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
     Returns:
         :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
     """
-    # Validation
-    if torch.min(y) < -1.07:
-        logger.debug("min value is %s", str(torch.min(y)))
-    if torch.max(y) > 1.07:
-        logger.debug("max value is %s", str(torch.max(y)))
-
+    
     # Window - Cache if needed
     global hann_window
     dtype_device = str(y.dtype) + "_" + str(y.device)
@@ -66,7 +60,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
         hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
             dtype=y.dtype, device=y.device
         )
-
+    
     # Padding
     y = torch.nn.functional.pad(
         y.unsqueeze(1),
@@ -74,7 +68,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
         mode="reflect",
     )
     y = y.squeeze(1)
-
+    
     # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
     spec = torch.stft(
         y,
@@ -86,14 +80,13 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
         pad_mode="reflect",
         normalized=False,
         onesided=True,
-        return_complex=False,
+        return_complex=True,
     )
-
+    
     # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
+    spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
     return spec
 
-
 def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
     # MelBasis - Cache if needed
     global mel_basis