Style extract_tts_spectrogram.py

TTS/bin/extract_tts_spectrograms.py

@@ -76,14 +76,14 @@ def set_filename(wav_path, out_path):
 
 def format_data(data):
     # setup input data
-    text_input = data['text']
-    text_lengths = data['text_lengths']
-    mel_input = data['mel']
-    mel_lengths = data['mel_lengths']
-    item_idx = data['item_idxs']
-    d_vectors = data['d_vectors']
-    speaker_ids = data['speaker_ids']
-    attn_mask = data['attns']
+    text_input = data["text"]
+    text_lengths = data["text_lengths"]
+    mel_input = data["mel"]
+    mel_lengths = data["mel_lengths"]
+    item_idx = data["item_idxs"]
+    d_vectors = data["d_vectors"]
+    speaker_ids = data["speaker_ids"]
+    attn_mask = data["attns"]
     avg_text_length = torch.mean(text_lengths.float())
     avg_spec_length = torch.mean(mel_lengths.float())
 
@@ -132,7 +132,11 @@ def inference(
         elif d_vectors is not None:
             speaker_c = d_vectors
         outputs = model.inference_with_MAS(
-            text_input, text_lengths, mel_input, mel_lengths, aux_input={"d_vectors": speaker_c, "speaker_ids": speaker_ids}
+            text_input,
+            text_lengths,
+            mel_input,
+            mel_lengths,
+            aux_input={"d_vectors": speaker_c, "speaker_ids": speaker_ids},
         )
         model_output = outputs["model_outputs"]
         model_output = model_output.transpose(1, 2).detach().cpu().numpy()