fix

data_utils.py

@@ -8,7 +8,7 @@ import torch.utils.data
 import commons
 from mel_processing import spectrogram_torch
 from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
+from text import cleaned_text_to_sequence
 
 """Multi speaker version"""
 
@@ -113,14 +113,14 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
                 word2ph[i] = word2ph[i] * 2
             word2ph[0] += 1
         bert_path = wav_path.replace(".wav", ".bert.pt")
-        try:
+        # try:
-            bert = torch.load(bert_path)
+        bert = torch.load(bert_path)
-            assert bert.shape[-1] == len(phone)
+        assert bert.shape[-1] == len(phone)
-        except:
+        # except:
-            bert = get_bert(text, word2ph, language_str)
+        #     bert = get_bert(text, word2ph, language_str)
-            torch.save(bert, bert_path)
+        #     torch.save(bert, bert_path)
-            print(bert.shape[-1], bert_path, text, pold)
+        #     print(bert.shape[-1], bert_path, text, pold)
-            assert bert.shape[-1] == len(phone)
+        #     assert bert.shape[-1] == len(phone)
 
         assert bert.shape[-1] == len(phone), (
         bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)

spec_gen.py

@@ -11,47 +11,47 @@ 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)
+# train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
-eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
+# eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
+#
+# 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_loader):
+#     pass
 
-collate_fn = TextAudioSpeakerCollate()
+for line in tqdm( open(hps.data.training_files).readlines()):
-train_loader = DataLoader(train_dataset, num_workers=12, shuffle=False,
+    _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
-                         batch_size=32, pin_memory=True,
+    phone = phones.split(" ")
-                         drop_last=False, collate_fn=collate_fn)
+    tone = [int(i) for i in tone.split(" ")]
-eval_loader = DataLoader(eval_dataset, num_workers=12, shuffle=False,
+    word2ph = [int(i) for i in word2ph.split(" ")]
-                         batch_size=32, pin_memory=True,
+    # print(text, word2ph,phone, tone, language_str)
-                         drop_last=False, collate_fn=collate_fn)
+    w2pho = [i for i in word2ph]
-for _ in tqdm(train_loader):
+    word2ph = [i for i in word2ph]
-    pass
+    phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-for _ in tqdm(eval_loader):
+    pold2 = phone
-    pass
 
-# for line in tqdm( open(hps.data.training_files).readlines()):
+    if hps.data.add_blank:
-#     _id, spk, language_str, text, phones, tone, word2ph = line.strip().split("|")
+        phone = commons.intersperse(phone, 0)
-#     phone = phones.split(" ")
+        tone = commons.intersperse(tone, 0)
-#     tone = [int(i) for i in tone.split(" ")]
+        language = commons.intersperse(language, 0)
-#     word2ph = [int(i) for i in word2ph.split(" ")]
+        for i in range(len(word2ph)):
-#     # print(text, word2ph,phone, tone, language_str)
+            word2ph[i] = word2ph[i] * 2
-#     w2pho = [i for i in word2ph]
+        word2ph[0] += 1
-#     word2ph = [i for i in word2ph]
+    wav_path = f'dataset/{spk}/{_id}.wav'
-#     phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
-#     pold2 = phone
 
-#     if hps.data.add_blank:
+    bert_path = wav_path.replace(".wav", ".bert.pt")
-#         phone = commons.intersperse(phone, 0)
+    try:
-#         tone = commons.intersperse(tone, 0)
+        bert = torch.load(bert_path)
-#         language = commons.intersperse(language, 0)
+        assert bert.shape[-1] == len(phone)
-#         for i in range(len(word2ph)):
+    except:
-#             word2ph[i] = word2ph[i] * 2
+        bert = get_bert(text, word2ph, language_str)
-#         word2ph[0] += 1
+        assert bert.shape[-1] == len(phone)
-#     wav_path = f'dataset/{spk}/{_id}.wav'
+        torch.save(bert, bert_path)
-
-#     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)
 

text/tone_sandhi.py

@@ -82,13 +82,13 @@ class ToneSandhi():
             } and word not in self.must_not_neural_tone_words:
                 finals[j] = finals[j][:-1] + "5"
         ge_idx = word.find("个")
-        if len(word) >= 1 and word[-1] in "吧呢哈啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
+        if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
             finals[-1] = finals[-1][:-1] + "5"
         elif len(word) >= 1 and word[-1] in "的地得":
             finals[-1] = finals[-1][:-1] + "5"
         # e.g. 走了, 看着, 去过
-        elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
+        # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
-            finals[-1] = finals[-1][:-1] + "5"
+        #     finals[-1] = finals[-1][:-1] + "5"
         elif len(word) > 1 and word[-1] in "们子" and pos in {
                 "r", "n"
         } and word not in self.must_not_neural_tone_words: