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  1. speaker_embeddings/v2/zh_speaker_8_coarse_prompt.npy +3 -0
  2. speaker_embeddings/v2/zh_speaker_8_fine_prompt.npy +3 -0
  3. speaker_embeddings/v2/zh_speaker_8_semantic_prompt.npy +3 -0
  4. speaker_embeddings/v2/zh_speaker_9_coarse_prompt.npy +3 -0
  5. speaker_embeddings/v2/zh_speaker_9_fine_prompt.npy +3 -0
  6. speaker_embeddings/v2/zh_speaker_9_semantic_prompt.npy +3 -0
  7. speaker_embeddings/zh_speaker_0_coarse_prompt.npy +3 -0
  8. speaker_embeddings/zh_speaker_0_fine_prompt.npy +3 -0
  9. speaker_embeddings/zh_speaker_0_semantic_prompt.npy +3 -0
  10. speaker_embeddings/zh_speaker_1_coarse_prompt.npy +3 -0
  11. speaker_embeddings/zh_speaker_1_fine_prompt.npy +3 -0
  12. speaker_embeddings/zh_speaker_1_semantic_prompt.npy +3 -0
  13. speaker_embeddings/zh_speaker_2_coarse_prompt.npy +3 -0
  14. speaker_embeddings/zh_speaker_2_fine_prompt.npy +3 -0
  15. speaker_embeddings/zh_speaker_2_semantic_prompt.npy +3 -0
  16. speaker_embeddings/zh_speaker_3_coarse_prompt.npy +3 -0
  17. speaker_embeddings/zh_speaker_3_fine_prompt.npy +3 -0
  18. speaker_embeddings/zh_speaker_3_semantic_prompt.npy +3 -0
  19. speaker_embeddings/zh_speaker_4_coarse_prompt.npy +3 -0
  20. speaker_embeddings/zh_speaker_4_fine_prompt.npy +3 -0
  21. speaker_embeddings/zh_speaker_4_semantic_prompt.npy +3 -0
  22. speaker_embeddings/zh_speaker_5_coarse_prompt.npy +3 -0
  23. speaker_embeddings/zh_speaker_5_fine_prompt.npy +3 -0
  24. speaker_embeddings/zh_speaker_5_semantic_prompt.npy +3 -0
  25. speaker_embeddings/zh_speaker_6_coarse_prompt.npy +3 -0
  26. speaker_embeddings/zh_speaker_6_fine_prompt.npy +3 -0
  27. speaker_embeddings/zh_speaker_6_semantic_prompt.npy +3 -0
  28. speaker_embeddings/zh_speaker_7_coarse_prompt.npy +3 -0
  29. speaker_embeddings/zh_speaker_7_fine_prompt.npy +3 -0
  30. speaker_embeddings/zh_speaker_7_semantic_prompt.npy +3 -0
  31. speaker_embeddings/zh_speaker_8_coarse_prompt.npy +3 -0
  32. speaker_embeddings/zh_speaker_8_fine_prompt.npy +3 -0
  33. speaker_embeddings/zh_speaker_8_semantic_prompt.npy +3 -0
  34. speaker_embeddings/zh_speaker_9_coarse_prompt.npy +3 -0
  35. speaker_embeddings/zh_speaker_9_fine_prompt.npy +3 -0
  36. speaker_embeddings/zh_speaker_9_semantic_prompt.npy +3 -0
  37. speaker_embeddings_path.json +1 -0
  38. special_tokens_map.json +7 -0
  39. tokenizer.json +0 -0
  40. tokenizer_config.json +14 -0
  41. vocab.txt +0 -0
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"v2/de_speaker_3": {"semantic_prompt": "speaker_embeddings/v2/de_speaker_3_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/v2/de_speaker_3_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/v2/de_speaker_3_fine_prompt.npy"}, "v2/pl_speaker_5": {"semantic_prompt": "speaker_embeddings/v2/pl_speaker_5_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/v2/pl_speaker_5_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/v2/pl_speaker_5_fine_prompt.npy"}, "ko_speaker_1": {"semantic_prompt": "speaker_embeddings/ko_speaker_1_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/ko_speaker_1_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/ko_speaker_1_fine_prompt.npy"}, "v2/ko_speaker_0": {"semantic_prompt": "speaker_embeddings/v2/ko_speaker_0_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/v2/ko_speaker_0_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/v2/ko_speaker_0_fine_prompt.npy"}, "v2/pt_speaker_2": {"semantic_prompt": "speaker_embeddings/v2/pt_speaker_2_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/v2/pt_speaker_2_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/v2/pt_speaker_2_fine_prompt.npy"}, "v2/de_speaker_5": {"semantic_prompt": "speaker_embeddings/v2/de_speaker_5_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/v2/de_speaker_5_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/v2/de_speaker_5_fine_prompt.npy"}, "v2/fr_speaker_6": {"semantic_prompt": "speaker_embeddings/v2/fr_speaker_6_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/v2/fr_speaker_6_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/v2/fr_speaker_6_fine_prompt.npy"}, "v2/zh_speaker_2": {"semantic_prompt": "speaker_embeddings/v2/zh_speaker_2_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/v2/zh_speaker_2_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/v2/zh_speaker_2_fine_prompt.npy"}, "en_speaker_1": {"semantic_prompt": "speaker_embeddings/en_speaker_1_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/en_speaker_1_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/en_speaker_1_fine_prompt.npy"}, "v2/de_speaker_8": {"semantic_prompt": "speaker_embeddings/v2/de_speaker_8_semantic_prompt.npy", "coarse_prompt": "speaker_embeddings/v2/de_speaker_8_coarse_prompt.npy", "fine_prompt": "speaker_embeddings/v2/de_speaker_8_fine_prompt.npy"}}
special_tokens_map.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "cls_token": "[CLS]",
3
+ "mask_token": "[MASK]",
4
+ "pad_token": "[PAD]",
5
+ "sep_token": "[SEP]",
6
+ "unk_token": "[UNK]"
7
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "clean_up_tokenization_spaces": true,
3
+ "cls_token": "[CLS]",
4
+ "do_lower_case": false,
5
+ "mask_token": "[MASK]",
6
+ "model_max_length": 512,
7
+ "pad_token": "[PAD]",
8
+ "processor_class": "BarkProcessor",
9
+ "sep_token": "[SEP]",
10
+ "strip_accents": null,
11
+ "tokenize_chinese_chars": true,
12
+ "tokenizer_class": "BertTokenizer",
13
+ "unk_token": "[UNK]"
14
+ }
vocab.txt ADDED
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