fix

spec_gen.py

@@ -1,14 +1,57 @@
+import torch
+from torch.utils.data import DataLoader
+
+import commons
 import utils
-from data_utils import TextAudioSpeakerLoader
+from data_utils import TextAudioSpeakerLoader, TextAudioSpeakerCollate
 from tqdm import tqdm
 
-config_path = 'configs/fzh.json'
+from text import cleaned_text_to_sequence, get_bert
+
+config_path = 'configs/config.json'
 hps = utils.get_hparams_from_file(config_path)
 
 train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
 eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
 
-for _ in tqdm(train_dataset):
+collate_fn = TextAudioSpeakerCollate()
+train_loader = DataLoader(train_dataset, num_workers=12, shuffle=False,
+                         batch_size=32, pin_memory=True,
+                         drop_last=False, collate_fn=collate_fn)
+eval_loader = DataLoader(eval_dataset, num_workers=12, shuffle=False,
+                         batch_size=32, pin_memory=True,
+                         drop_last=False, collate_fn=collate_fn)
+for _ in tqdm(train_loader):
     pass
-for _ in tqdm(eval_dataset):
-    pass
+for _ in tqdm(eval_loader):
+    pass
+
+# for line in tqdm( open(hps.data.training_files).readlines()):
+#     _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
+#     phone = phones.split(" ")
+#     tone = [int(i) for i in tone.split(" ")]
+#     word2ph = [int(i) for i in word2ph.split(" ")]
+#     # print(text, word2ph,phone, tone, language_str)
+#     w2pho = [i for i in word2ph]
+#     word2ph = [i for i in word2ph]
+#     phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
+#     pold2 = phone
+
+#     if hps.data.add_blank:
+#         phone = commons.intersperse(phone, 0)
+#         tone = commons.intersperse(tone, 0)
+#         language = commons.intersperse(language, 0)
+#         for i in range(len(word2ph)):
+#             word2ph[i] = word2ph[i] * 2
+#         word2ph[0] += 1
+#     wav_path = f'dataset/{spk}/{_id}.wav'
+
+#     bert_path = wav_path.replace(".wav", ".bert.pt")
+#     try:
+#         bert = torch.load(bert_path)
+#         assert bert.shape[-1] == len(phone)
+#     except:
+#         bert = get_bert(text, word2ph, language_str)
+#         assert bert.shape[-1] == len(phone)
+#         torch.save(bert, bert_path)
+