hexgrad/Kokoro-82M

1# Voices2 3- 🇺🇸 [American English](#american-english): 11F 9M4- 🇬🇧 [British English](#british-english): 4F 4M5- 🇯🇵 [Japanese](#japanese): 4F 1M6- 🇨🇳 [Mandarin Chinese](#mandarin-chinese): 4F 4M7- 🇪🇸 [Spanish](#spanish): 1F 2M8- 🇫🇷 [French](#french): 1F9- 🇮🇳 [Hindi](#hindi): 2F 2M10- 🇮🇹 [Italian](#italian): 1F 1M11- 🇧🇷 [Brazilian Portuguese](#brazilian-portuguese): 1F 2M12 13For 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.14 15Subjectively, voices will sound better or worse to different people.16 17Support for non-English languages may be absent or thin due to weak G2P and/or lack of training data. Some languages are only represented by a small handful or even just one voice (French).18 19Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 possible. Voices may perform worse at the extremes:20- **Weakness** on short utterances, especially less than 10-20 tokens. Root cause could be lack of short-utterance training data and/or model architecture. One possible inference mitigation is to bundle shorter utterances together.21- **Rushing** on long utterances, especially over 400 tokens. You can chunk down to shorter utterances or adjust the `speed` parameter to mitigate this.22 23**Target Quality**24- How high quality is the reference voice? This grade may be impacted by audio quality, artifacts, compression, & sample rate.25- How well do the text labels match the audio? Text/audio misalignment (e.g. from hallucinations) will lower this grade.26 27**Training Duration**28- How much audio was seen during training? Smaller durations result in a lower overall grade.29- 10 hours <= **HH hours** < 100 hours30- 1 hour <= H hours < 10 hours31- 10 minutes <= MM minutes < 100 minutes32- 1 minute <= _M minutes_ 🤏 < 10 minutes33 34### American English35 36- `lang_code='a'` in [`misaki[en]`](https://github.com/hexgrad/misaki)37- espeak-ng `en-us` fallback38 39| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |40| ---- | ------ | -------------- | ----------------- | ------------- | ------ |41| **af\_heart** | 🚺❤️ | | | **A** | `0ab5709b` |42| af_alloy | 🚺 | B | MM minutes | C | `6d877149` |43| af_aoede | 🚺 | B | H hours | C+ | `c03bd1a4` |44| af_bella | 🚺🔥 | **A** | **HH hours** | **A-** | `8cb64e02` |45| af_jessica | 🚺 | C | MM minutes | D | `cdfdccb8` |46| af_kore | 🚺 | B | H hours | C+ | `8bfbc512` |47| af_nicole | 🚺🎧 | B | **HH hours** | B- | `c5561808` |48| af_nova | 🚺 | B | MM minutes | C | `e0233676` |49| af_river | 🚺 | C | MM minutes | D | `e149459b` |50| af_sarah | 🚺 | B | H hours | C+ | `49bd364e` |51| af_sky | 🚺 | B | _M minutes_ 🤏 | C- | `c799548a` |52| am_adam | 🚹 | D | H hours | F+ | `ced7e284` |53| am_echo | 🚹 | C | MM minutes | D | `8bcfdc85` |54| am_eric | 🚹 | C | MM minutes | D | `ada66f0e` |55| am_fenrir | 🚹 | B | H hours | C+ | `98e507ec` |56| am_liam | 🚹 | C | MM minutes | D | `c8255075` |57| am_michael | 🚹 | B | H hours | C+ | `9a443b79` |58| am_onyx | 🚹 | C | MM minutes | D | `e8452be1` |59| am_puck | 🚹 | B | H hours | C+ | `dd1d8973` |60| am_santa | 🚹 | C | _M minutes_ 🤏 | D- | `7f2f7582` |61 62### British English63 64- `lang_code='b'` in [`misaki[en]`](https://github.com/hexgrad/misaki)65- espeak-ng `en-gb` fallback66 67| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |68| ---- | ------ | -------------- | ----------------- | ------------- | ------ |69| bf_alice | 🚺 | C | MM minutes | D | `d292651b` |70| bf_emma | 🚺 | B | **HH hours** | B- | `d0a423de` |71| bf_isabella | 🚺 | B | MM minutes | C | `cdd4c370` |72| bf_lily | 🚺 | C | MM minutes | D | `6e09c2e4` |73| bm_daniel | 🚹 | C | MM minutes | D | `fc3fce4e` |74| bm_fable | 🚹 | B | MM minutes | C | `d44935f3` |75| bm_george | 🚹 | B | MM minutes | C | `f1bc8122` |76| bm_lewis | 🚹 | C | H hours | D+ | `b5204750` |77 78### Japanese79 80- `lang_code='j'` in [`misaki[ja]`](https://github.com/hexgrad/misaki)81- Total Japanese training data: H hours82 83| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |84| ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |85| jf_alpha | 🚺 | B | H hours | C+ | `1bf4c9dc` | |86| jf_gongitsune | 🚺 | B | MM minutes | C | `1b171917` | [gongitsune](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__gongitsune.txt) |87| jf_nezumi | 🚺 | B | _M minutes_ 🤏 | C- | `d83f007a` | [nezuminoyomeiri](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__nezuminoyomeiri.txt) |88| jf_tebukuro | 🚺 | B | MM minutes | C | `0d691790` | [tebukurowokaini](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__tebukurowokaini.txt) |89| jm_kumo | 🚹 | B | _M minutes_ 🤏 | C- | `98340afd` | [kumonoito](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__kumonoito.txt) |90 91### Mandarin Chinese92 93- `lang_code='z'` in [`misaki[zh]`](https://github.com/hexgrad/misaki)94- Total Mandarin Chinese training data: H hours95 96| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |97| ---- | ------ | -------------- | ----------------- | ------------- | ------ |98| zf_xiaobei | 🚺 | C | MM minutes | D | `9b76be63` |99| zf_xiaoni | 🚺 | C | MM minutes | D | `95b49f16` |100| zf_xiaoxiao | 🚺 | C | MM minutes | D | `cfaf6f2d` |101| zf_xiaoyi | 🚺 | C | MM minutes | D | `b5235dba` |102| zm_yunjian | 🚹 | C | MM minutes | D | `76cbf8ba` |103| zm_yunxi | 🚹 | C | MM minutes | D | `dbe6e1ce` |104| zm_yunxia | 🚹 | C | MM minutes | D | `bb2b03b0` |105| zm_yunyang | 🚹 | C | MM minutes | D | `5238ac22` |106 107### Spanish108 109- `lang_code='e'` in [`misaki[en]`](https://github.com/hexgrad/misaki)110- espeak-ng `es`111 112| Name | Traits | SHA256 |113| ---- | ------ | ------ |114| ef_dora | 🚺 | `d9d69b0f` |115| em_alex | 🚹 | `5eac53f7` |116| em_santa | 🚹 | `aa8620cb` |117 118### French119 120- `lang_code='f'` in [`misaki[en]`](https://github.com/hexgrad/misaki)121- espeak-ng `fr-fr`122- Total French training data: <11 hours123 124| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |125| ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |126| ff_siwis | 🚺 | B | <11 hours | B- | `8073bf2d` | [SIWIS](https://datashare.ed.ac.uk/handle/10283/2353) |127 128### Hindi129 130- `lang_code='h'` in [`misaki[en]`](https://github.com/hexgrad/misaki)131- espeak-ng `hi`132- Total Hindi training data: H hours133 134| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |135| ---- | ------ | -------------- | ----------------- | ------------- | ------ |136| hf_alpha | 🚺 | B | MM minutes | C | `06906fe0` |137| hf_beta | 🚺 | B | MM minutes | C | `63c0a1a6` |138| hm_omega | 🚹 | B | MM minutes | C | `b55f02a8` |139| hm_psi | 🚹 | B | MM minutes | C | `2f0f055c` |140 141### Italian142 143- `lang_code='i'` in [`misaki[en]`](https://github.com/hexgrad/misaki)144- espeak-ng `it`145- Total Italian training data: H hours146 147| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |148| ---- | ------ | -------------- | ----------------- | ------------- | ------ |149| if_sara | 🚺 | B | MM minutes | C | `6c0b253b` |150| im_nicola | 🚹 | B | MM minutes | C | `234ed066` |151 152### Brazilian Portuguese153 154- `lang_code='p'` in [`misaki[en]`](https://github.com/hexgrad/misaki)155- espeak-ng `pt-br`156 157| Name | Traits | SHA256 |158| ---- | ------ | ------ |159| pf_dora | 🚺 | `07e4ff98` |160| pm_alex | 🚹 | `cf0ba8c5` |161| pm_santa | 🚹 | `d4210316` |162