name change

soundbay/utils/logging.py

@@ -171,7 +171,7 @@ def upload_artifacts(self, audio: torch.Tensor, label: torch.Tensor, raw_wav: to
 
         artifact_wav = torch.squeeze(raw_wav).detach().cpu().numpy()
         artifact_wav = artifact_wav / np.expand_dims(np.abs(artifact_wav).max(axis=1) + 1e-8, 1) * 0.5  # gain -6dB
-        list_of_wavs_objects = [wandb.Audio(data_or_path=wav, caption=f'label_{lab}_{ind}_{round(b_t/sample_rate,3)}sec_{f_n}_train', sample_rate=sample_rate) for wav, ind, lab, b_t, f_n in zip(artifact_wav,idx, label, meta['begin_time'], meta['org_file'])]
+        list_of_wavs_objects = [wandb.Audio(data_or_path=wav, caption=f'label{lab}_i{ind}_{round(b_t/sample_rate,3)}sec_{f_n}_train', sample_rate=sample_rate) for wav, ind, lab, b_t, f_n in zip(artifact_wav,idx, label, meta['begin_time'], meta['org_file'])]
 
         # Spectrograms batch
         artifact_spec = torch.squeeze(audio).detach().cpu().numpy()
@@ -181,7 +181,7 @@ def upload_artifacts(self, audio: torch.Tensor, label: torch.Tensor, raw_wav: to
             specs.append(librosa.display.specshow(artifact_spec[artifact_id,...], ax=ax[1]))
             plt.close('all')
             del ax
-        list_of_specs_objects = [wandb.Image(data_or_path=spec, caption=f'label_{lab}_{ind}_{round(b_t/sample_rate,2)}sec_{f_n}train') for spec, ind, lab, b_t, f_n in zip(specs,idx, label, meta['begin_time'], meta['org_file'])]
+        list_of_specs_objects = [wandb.Image(data_or_path=spec, caption=f'label{lab}_i{ind}_{round(b_t/sample_rate,2)}sec_{f_n}train') for spec, ind, lab, b_t, f_n in zip(specs,idx, label, meta['begin_time'], meta['org_file'])]
         log_wavs = {f'First batch {flag} original wavs': list_of_wavs_objects}
         log_specs = {f'First batch {flag} augmented spectrogram\'s': list_of_specs_objects}