Update spec extractor

TTS/bin/extract_tts_spectrograms.py

@@ -13,6 +13,7 @@ from TTS.config import load_config
 from TTS.tts.datasets import TTSDataset, load_tts_samples
 from TTS.tts.models import setup_model
 from TTS.tts.utils.speakers import SpeakerManager
+from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 from TTS.utils.generic_utils import count_parameters
 
@@ -20,21 +21,20 @@ use_cuda = torch.cuda.is_available()
 
 
 def setup_loader(ap, r, verbose=False):
+    tokenizer, _ = TTSTokenizer.init_from_config(c)
     dataset = TTSDataset(
-        r,
-        c.text_cleaner,
+        outputs_per_step=r,
         compute_linear_spec=False,
-        meta_data=meta_data,
+        samples=meta_data,
+        tokenizer=tokenizer,
         ap=ap,
-        characters=c.characters if "characters" in c.keys() else None,
-        add_blank=c["add_blank"] if "add_blank" in c.keys() else False,
         batch_group_size=0,
-        min_seq_len=c.min_seq_len,
-        max_seq_len=c.max_seq_len,
+        min_text_len=c.min_text_len,
+        max_text_len=c.max_text_len,
+        min_audio_len=c.min_audio_len,
+        max_audio_len=c.max_audio_len,
         phoneme_cache_path=c.phoneme_cache_path,
-        use_phonemes=c.use_phonemes,
-        phoneme_language=c.phoneme_language,
-        enable_eos_bos=c.enable_eos_bos_chars,
+        precompute_num_workers=0,
         use_noise_augment=False,
         verbose=verbose,
         speaker_id_mapping=speaker_manager.speaker_ids if c.use_speaker_embedding else None,
@@ -44,7 +44,7 @@ def setup_loader(ap, r, verbose=False):
     if c.use_phonemes and c.compute_input_seq_cache:
         # precompute phonemes to have a better estimate of sequence lengths.
         dataset.compute_input_seq(c.num_loader_workers)
-    dataset.sort_and_filter_items(c.get("sort_by_audio_len", default=False))
+    dataset.preprocess_samples()
 
     loader = DataLoader(
         dataset,
@@ -75,8 +75,8 @@ def set_filename(wav_path, out_path):
 
 def format_data(data):
     # setup input data
-    text_input = data["text"]
-    text_lengths = data["text_lengths"]
+    text_input = data["token_id"]
+    text_lengths = data["token_id_lengths"]
     mel_input = data["mel"]
     mel_lengths = data["mel_lengths"]
     item_idx = data["item_idxs"]