update

examples/spectrum_dfnet_aishell/step_2_train_model.py

@@ -322,14 +322,6 @@ def main():
 
             speech_loss = speech_mse_loss.forward(speech_spec_prediction, torch.view_as_real(speech_complex_spec))
             irm_loss = irm_mse_loss.forward(speech_irm_prediction, speech_irm_target)
-
-            lsnr_prediction = (lsnr_prediction - config.lsnr_min) / (config.lsnr_max - config.lsnr_min)
-            snr_db_target = (snr_db_target - config.lsnr_min) / (config.lsnr_max - config.lsnr_min)
-            if torch.max(lsnr_prediction) > 1 or torch.min(lsnr_prediction) < 0:
-                raise AssertionError(f"expected lsnr_prediction between 0 and 1.")
-            if torch.max(snr_db_target) > 1 or torch.min(snr_db_target) < 0:
-                raise AssertionError(f"expected snr_db_target between 0 and 1.")
-
             snr_loss = snr_mse_loss.forward(lsnr_prediction, snr_db_target)
 
             if torch.any(torch.isnan(snr_loss)) or torch.any(torch.isinf(snr_loss)):
@@ -377,14 +369,6 @@ def main():
 
                 speech_loss = speech_mse_loss.forward(speech_spec_prediction, torch.view_as_real(speech_complex_spec))
                 irm_loss = irm_mse_loss.forward(speech_irm_prediction, speech_irm_target)
-
-                lsnr_prediction = (lsnr_prediction - config.lsnr_min) / (config.lsnr_max - config.lsnr_min)
-                snr_db_target = (snr_db_target - config.lsnr_min) / (config.lsnr_max - config.lsnr_min)
-                if torch.max(lsnr_prediction) > 1 or torch.min(lsnr_prediction) < 0:
-                    raise AssertionError(f"expected lsnr_prediction between 0 and 1.")
-                if torch.max(snr_db_target) > 1 or torch.min(snr_db_target) < 0:
-                    raise AssertionError(f"expected snr_db_target between 0 and 1.")
-
                 snr_loss = snr_mse_loss.forward(lsnr_prediction, snr_db_target)
 
                 loss = speech_loss + irm_loss + snr_loss