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Datasets:
leeoxiang
/
multi_lingo_data

Tasks:
Text-to-Speech
Audio-to-Audio
Modalities:
Audio
Tabular
Text
Languages:
English
Japanese
Korean
Size:
1K - 10K
License:
Dataset card Data Studio Files
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  1. README.md +57 -12
  2. audio_prompts/metadata.csv +0 -0
  3. audio_prompts_expressive/en/en_124_EN_B00009_S03609/0205.wav +3 -0
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README.md CHANGED
@@ -19,17 +19,23 @@ configs:
19
  data_files:
20
  - split: train
21
  path: audio_prompts/metadata.csv
 
 
 
 
22
  ---
23
 
24
  # Multi-lingual TTS Data (leeoxiang/multi_lingo_data)
25
 
26
- Two related splits:
27
 
28
  1. **`references`** — real Emilia mono-lingual speaker clips (source of
29
  truth for voice cloning).
30
- 2. **`audio_prompts`** — synthesized cross-lingual clips: each real
31
- speaker cloned to speak every target language. Used as pseudo-refs
32
- for Step 2 same-lang expansion.
 
 
33
 
34
  ## References (real Emilia speakers)
35
 
@@ -47,14 +53,15 @@ points to its wav via ``audio_file`` (relative to repo root).
47
  | ko | 300 |
48
  | zh | 300 |
49
 
50
- ## Audio Prompts (cross-lingual synth)
 
51
 
52
  Generated by ``scripts/generate_audio_prompts_higgs_sglang.py`` against
53
  a local sglang-omni server serving ``bosonai/higgs-tts-3-4b``. For each
54
  reference speaker × target language × text prompt, one wav.
55
 
56
- - Total wavs: **34**
57
- - Total duration: **0.18 h**
58
  - Engine: `sglang-omni-higgs-audio-v3`
59
  - Layout: `audio_prompts/<target_lang>/<ref_id>/<text_id:04d>.wav`
60
 
@@ -62,19 +69,56 @@ reference speaker × target language × text prompt, one wav.
62
 
63
  | target_lang | n_wavs |
64
  |-------------|--------|
65
- | ko | 34 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
66
 
67
  ### Per source-language (Emilia speaker's native lang)
68
 
69
  | source_lang | n_wavs |
70
  |-------------|--------|
71
- | ko | 34 |
 
 
 
 
72
 
73
- ### CSV schema (``audio_prompts.csv``)
74
 
75
  | column | description |
76
  |------------------|---------------------------------------------------|
77
- | file_name | wav path relative to repo root |
78
  | ref_id | source speaker key |
79
  | ref_lang | source speaker's native language |
80
  | speaker_id | original Emilia speaker id |
@@ -97,5 +141,6 @@ from datasets import load_dataset
97
  refs = load_dataset("leeoxiang/multi_lingo_data", "references", split="train")
98
 
99
  # Audio prompts (cross-lingual synths)
100
- ap = load_dataset("leeoxiang/multi_lingo_data", "audio_prompts", split="train")
 
101
  ```
 
19
  data_files:
20
  - split: train
21
  path: audio_prompts/metadata.csv
22
+ - config_name: audio_prompts_expressive
23
+ data_files:
24
+ - split: train
25
+ path: audio_prompts_expressive/metadata.csv
26
  ---
27
 
28
  # Multi-lingual TTS Data (leeoxiang/multi_lingo_data)
29
 
30
+ Splits:
31
 
32
  1. **`references`** — real Emilia mono-lingual speaker clips (source of
33
  truth for voice cloning).
34
+ 2. **audio_prompts variants** — synthesized cross-lingual clips: each
35
+ real speaker cloned to speak every target language. Used as
36
+ pseudo-refs for Step 2 same-lang expansion.
37
+ - **`audio_prompts`** — 14400 wavs
38
+ - **`audio_prompts_expressive`** — 14400 wavs
39
 
40
  ## References (real Emilia speakers)
41
 
 
53
  | ko | 300 |
54
  | zh | 300 |
55
 
56
+
57
+ ## Audio Prompts — `audio_prompts`
58
 
59
  Generated by ``scripts/generate_audio_prompts_higgs_sglang.py`` against
60
  a local sglang-omni server serving ``bosonai/higgs-tts-3-4b``. For each
61
  reference speaker × target language × text prompt, one wav.
62
 
63
+ - Total wavs: **14400**
64
+ - Total duration: **59.56 h**
65
  - Engine: `sglang-omni-higgs-audio-v3`
66
  - Layout: `audio_prompts/<target_lang>/<ref_id>/<text_id:04d>.wav`
67
 
 
69
 
70
  | target_lang | n_wavs |
71
  |-------------|--------|
72
+ | en | 3600 |
73
+ | ja | 3600 |
74
+ | ko | 3600 |
75
+ | zh | 3600 |
76
+
77
+ ### Per source-language (Emilia speaker's native lang)
78
+
79
+ | source_lang | n_wavs |
80
+ |-------------|--------|
81
+ | en | 3600 |
82
+ | ja | 3600 |
83
+ | ko | 3600 |
84
+ | zh | 3600 |
85
+
86
+
87
+ ## Audio Prompts — `audio_prompts_expressive`
88
+
89
+ Generated by ``scripts/generate_audio_prompts_higgs_sglang.py`` against
90
+ a local sglang-omni server serving ``bosonai/higgs-tts-3-4b``. For each
91
+ reference speaker × target language × text prompt, one wav.
92
+
93
+ - Total wavs: **14400**
94
+ - Total duration: **59.84 h**
95
+ - Engine: `sglang-omni-higgs-audio-v3`
96
+ - Layout: `audio_prompts_expressive/<target_lang>/<ref_id>/<text_id:04d>.wav`
97
+
98
+ ### Per target-language
99
+
100
+ | target_lang | n_wavs |
101
+ |-------------|--------|
102
+ | en | 3600 |
103
+ | ja | 3600 |
104
+ | ko | 3600 |
105
+ | zh | 3600 |
106
 
107
  ### Per source-language (Emilia speaker's native lang)
108
 
109
  | source_lang | n_wavs |
110
  |-------------|--------|
111
+ | en | 3600 |
112
+ | ja | 3600 |
113
+ | ko | 3600 |
114
+ | zh | 3600 |
115
+
116
 
117
+ ### CSV schema (per-variant ``<subdir>/metadata.csv``)
118
 
119
  | column | description |
120
  |------------------|---------------------------------------------------|
121
+ | file_name | wav path relative to <subdir>/metadata.csv |
122
  | ref_id | source speaker key |
123
  | ref_lang | source speaker's native language |
124
  | speaker_id | original Emilia speaker id |
 
141
  refs = load_dataset("leeoxiang/multi_lingo_data", "references", split="train")
142
 
143
  # Audio prompts (cross-lingual synths)
144
+ audio_prompts = load_dataset("leeoxiang/multi_lingo_data", "audio_prompts", split="train")
145
+ audio_prompts_expressive = load_dataset("leeoxiang/multi_lingo_data", "audio_prompts_expressive", split="train")
146
  ```
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