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nx_denoise

Running

HoneyTian commited on Feb 28

Commit

6dd520a

1 Parent(s): 5055ff3

update

Files changed (1) hide show

examples/nx_clean_unet/step_2_train_model.py CHANGED Viewed

@@ -245,8 +245,8 @@ def main():
             audio_g = generator.forward(noisy_audios)
-            clean_mag, clean_pha, clean_com = mag_pha_stft(clean_audios, config.n_fft, config.hop_size, config.win_size, config.compress_factor)
-            mag_g, pha_g, com_g = mag_pha_stft(audio_g, config.n_fft, config.hop_size, config.win_size, config.compress_factor)
             clean_audio_list = torch.split(clean_audios, 1, dim=0)
             enhanced_audio_list = torch.split(audio_g, 1, dim=0)
@@ -329,8 +329,8 @@ def main():
                 audio_g = generator.forward(noisy_audios)
-                clean_mag, clean_pha, clean_com = mag_pha_stft(clean_audios, config.n_fft, config.hop_size, config.win_size, config.compress_factor)
-                mag_g, pha_g, com_g = mag_pha_stft(audio_g, config.n_fft, config.hop_size, config.win_size, config.compress_factor)
                 clean_audio_list = torch.split(clean_audios, 1, dim=0)
                 enhanced_audio_list = torch.split(audio_g, 1, dim=0)

             audio_g = generator.forward(noisy_audios)
+            clean_mag, clean_pha, clean_com = mag_pha_stft(clean_audios, config.n_fft, config.hop_length, config.win_length, config.compress_factor)
+            mag_g, pha_g, com_g = mag_pha_stft(audio_g, config.n_fft, config.hop_length, config.win_length, config.compress_factor)
             clean_audio_list = torch.split(clean_audios, 1, dim=0)
             enhanced_audio_list = torch.split(audio_g, 1, dim=0)
                 audio_g = generator.forward(noisy_audios)
+                clean_mag, clean_pha, clean_com = mag_pha_stft(clean_audios, config.n_fft, config.hop_length, config.win_length, config.compress_factor)
+                mag_g, pha_g, com_g = mag_pha_stft(audio_g, config.n_fft, config.hop_length, config.win_length, config.compress_factor)
                 clean_audio_list = torch.split(clean_audios, 1, dim=0)
                 enhanced_audio_list = torch.split(audio_g, 1, dim=0)