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Kokoro-82M

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Upload VOICES.md

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VOICES.md CHANGED
@@ -1,12 +1,12 @@
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  # Voices
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- 🇺🇸 [American English](#american-english): 10F 9M
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- 🇬🇧 [British English](#british-english): 4F 4M
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- 🇫🇷 [French](#french): 1F
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- 🇮🇳 [Hindi](#hindi): 2F 2M
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- 🇮🇹 [Italian](#italian): 1F 1M
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- 🇯🇵 [Japanese](#japanese): 4F 1M
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- 🇨🇳 [Mandarin Chinese](#mandarin-chinese): 4F 4M
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  For each voice, the given grades are intended to be estimates of the **quality and quantity** of its associated training data, both of which impact overall inference quality.
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@@ -31,8 +31,8 @@ Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 p
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  ### American English
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- 🇺🇸 `lang_code='a'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
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- 🇺🇸 espeak-ng `en-us` fallback
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  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
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  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
@@ -58,8 +58,8 @@ Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 p
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  ### British English
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- 🇬🇧 `lang_code='b'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
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- 🇬🇧 espeak-ng `en-gb` fallback
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  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
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  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
@@ -74,9 +74,9 @@ Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 p
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  ### French
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- 🇫🇷 `lang_code='f'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
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- 🇫🇷 espeak-ng `fr-fr`
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- 🇫🇷 Total French training data: <11 hours
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  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |
82
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |
@@ -84,9 +84,9 @@ Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 p
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  ### Hindi
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- 🇮🇳 `lang_code='h'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
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- 🇮🇳 espeak-ng `hi`
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- 🇮🇳 Total Hindi training data: H hours
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  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
92
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
@@ -97,9 +97,9 @@ Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 p
97
 
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  ### Italian
99
 
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- 🇮🇹 `lang_code='i'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
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- 🇮🇹 espeak-ng `it`
102
- 🇮🇹 Total Italian training data: H hours
103
 
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  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
105
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
@@ -108,8 +108,8 @@ Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 p
108
 
109
  ### Japanese
110
 
111
- 🇯🇵 `lang_code='j'` in [`misaki[ja]`](https://github.com/hexgrad/misaki)
112
- 🇯🇵 Total Japanese training data: H hours
113
 
114
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |
115
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |
@@ -121,8 +121,8 @@ Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 p
121
 
122
  ### Mandarin Chinese
123
 
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- 🇨🇳 `lang_code='z'` in [`misaki[zh]`](https://github.com/hexgrad/misaki)
125
- 🇨🇳 Total Mandarin Chinese training data: H hours
126
 
127
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
128
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
 
1
  # Voices
2
 
3
+ - 🇺🇸 [American English](#american-english): 10F 9M
4
+ - 🇬🇧 [British English](#british-english): 4F 4M
5
+ - 🇫🇷 [French](#french): 1F
6
+ - 🇮🇳 [Hindi](#hindi): 2F 2M
7
+ - 🇮🇹 [Italian](#italian): 1F 1M
8
+ - 🇯🇵 [Japanese](#japanese): 4F 1M
9
+ - 🇨🇳 [Mandarin Chinese](#mandarin-chinese): 4F 4M
10
 
11
  For each voice, the given grades are intended to be estimates of the **quality and quantity** of its associated training data, both of which impact overall inference quality.
12
 
 
31
 
32
  ### American English
33
 
34
+ - `lang_code='a'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
35
+ - espeak-ng `en-us` fallback
36
 
37
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
38
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
 
58
 
59
  ### British English
60
 
61
+ - `lang_code='b'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
62
+ - espeak-ng `en-gb` fallback
63
 
64
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
65
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
 
74
 
75
  ### French
76
 
77
+ - `lang_code='f'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
78
+ - espeak-ng `fr-fr`
79
+ - Total French training data: <11 hours
80
 
81
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |
82
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |
 
84
 
85
  ### Hindi
86
 
87
+ - `lang_code='h'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
88
+ - espeak-ng `hi`
89
+ - Total Hindi training data: H hours
90
 
91
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
92
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
 
97
 
98
  ### Italian
99
 
100
+ - `lang_code='i'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
101
+ - espeak-ng `it`
102
+ - Total Italian training data: H hours
103
 
104
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
105
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
 
108
 
109
  ### Japanese
110
 
111
+ - `lang_code='j'` in [`misaki[ja]`](https://github.com/hexgrad/misaki)
112
+ - Total Japanese training data: H hours
113
 
114
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |
115
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |
 
121
 
122
  ### Mandarin Chinese
123
 
124
+ - `lang_code='z'` in [`misaki[zh]`](https://github.com/hexgrad/misaki)
125
+ - Total Mandarin Chinese training data: H hours
126
 
127
  | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
128
  | ---- | ------ | -------------- | ----------------- | ------------- | ------ |