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+ # Voices
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+
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+ - 🇺🇸 [American English](#american-english): 11F 9M
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+ - 🇬🇧 [British English](#british-english): 4F 4M
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+ - 🇯🇵 [Japanese](#japanese): 4F 1M
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+ - 🇨🇳 [Mandarin Chinese](#mandarin-chinese): 4F 4M
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+ - 🇪🇸 [Spanish](#spanish): 1F 2M
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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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+ - 🇧🇷 [Brazilian Portuguese](#brazilian-portuguese): 1F 2M
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+
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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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+
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+ Subjectively, voices will sound better or worse to different people.
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+
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+ Support 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).
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+
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+ Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 possible. Voices may perform worse at the extremes:
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+ - **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.
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+ - **Rushing** on long utterances, especially over 400 tokens. You can chunk down to shorter utterances or adjust the `speed` parameter to mitigate this.
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+
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+ **Target Quality**
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+ - How high quality is the reference voice? This grade may be impacted by audio quality, artifacts, compression, & sample rate.
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+ - How well do the text labels match the audio? Text/audio misalignment (e.g. from hallucinations) will lower this grade.
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+
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+ **Training Duration**
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+ - How much audio was seen during training? Smaller durations result in a lower overall grade.
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+ - 10 hours <= **HH hours** < 100 hours
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+ - 1 hour <= H hours < 10 hours
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+ - 10 minutes <= MM minutes < 100 minutes
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+ - 1 minute <= _M minutes_ 🤏 < 10 minutes
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+
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+ ### American English
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+
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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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+
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+ | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
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+ | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
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+ | **af\_heart** | 🚺❤️ | | | **A** | `0ab5709b` |
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+ | af_alloy | 🚺 | B | MM minutes | C | `6d877149` |
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+ | af_aoede | 🚺 | B | H hours | C+ | `c03bd1a4` |
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+ | af_bella | 🚺🔥 | **A** | **HH hours** | **A-** | `8cb64e02` |
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+ | af_jessica | 🚺 | C | MM minutes | D | `cdfdccb8` |
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+ | af_kore | 🚺 | B | H hours | C+ | `8bfbc512` |
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+ | af_nicole | 🚺🎧 | B | **HH hours** | B- | `c5561808` |
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+ | af_nova | 🚺 | B | MM minutes | C | `e0233676` |
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+ | af_river | 🚺 | C | MM minutes | D | `e149459b` |
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+ | af_sarah | 🚺 | B | H hours | C+ | `49bd364e` |
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+ | af_sky | 🚺 | B | _M minutes_ 🤏 | C- | `c799548a` |
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+ | am_adam | 🚹 | D | H hours | F+ | `ced7e284` |
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+ | am_echo | 🚹 | C | MM minutes | D | `8bcfdc85` |
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+ | am_eric | 🚹 | C | MM minutes | D | `ada66f0e` |
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+ | am_fenrir | 🚹 | B | H hours | C+ | `98e507ec` |
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+ | am_liam | 🚹 | C | MM minutes | D | `c8255075` |
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+ | am_michael | 🚹 | B | H hours | C+ | `9a443b79` |
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+ | am_onyx | 🚹 | C | MM minutes | D | `e8452be1` |
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+ | am_puck | 🚹 | B | H hours | C+ | `dd1d8973` |
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+ | am_santa | 🚹 | C | _M minutes_ 🤏 | D- | `7f2f7582` |
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+
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+ ### British English
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+
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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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+
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+ | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
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+ | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
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+ | bf_alice | 🚺 | C | MM minutes | D | `d292651b` |
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+ | bf_emma | 🚺 | B | **HH hours** | B- | `d0a423de` |
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+ | bf_isabella | 🚺 | B | MM minutes | C | `cdd4c370` |
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+ | bf_lily | 🚺 | C | MM minutes | D | `6e09c2e4` |
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+ | bm_daniel | 🚹 | C | MM minutes | D | `fc3fce4e` |
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+ | bm_fable | 🚹 | B | MM minutes | C | `d44935f3` |
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+ | bm_george | 🚹 | B | MM minutes | C | `f1bc8122` |
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+ | bm_lewis | 🚹 | C | H hours | D+ | `b5204750` |
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+
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+ ### Japanese
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+
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+ - `lang_code='j'` in [`misaki[ja]`](https://github.com/hexgrad/misaki)
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+ - Total Japanese training data: H hours
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+
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+ | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |
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+ | ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |
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+ | jf_alpha | 🚺 | B | H hours | C+ | `1bf4c9dc` | |
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+ | jf_gongitsune | 🚺 | B | MM minutes | C | `1b171917` | [gongitsune](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__gongitsune.txt) |
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+ | jf_nezumi | 🚺 | B | _M minutes_ 🤏 | C- | `d83f007a` | [nezuminoyomeiri](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__nezuminoyomeiri.txt) |
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+ | jf_tebukuro | 🚺 | B | MM minutes | C | `0d691790` | [tebukurowokaini](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__tebukurowokaini.txt) |
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+ | jm_kumo | 🚹 | B | _M minutes_ 🤏 | C- | `98340afd` | [kumonoito](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__kumonoito.txt) |
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+
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+ ### Mandarin Chinese
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+
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+ - `lang_code='z'` in [`misaki[zh]`](https://github.com/hexgrad/misaki)
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+ - Total Mandarin Chinese training data: H hours
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+
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+ | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
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+ | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
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+ | zf_xiaobei | 🚺 | C | MM minutes | D | `9b76be63` |
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+ | zf_xiaoni | 🚺 | C | MM minutes | D | `95b49f16` |
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+ | zf_xiaoxiao | 🚺 | C | MM minutes | D | `cfaf6f2d` |
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+ | zf_xiaoyi | 🚺 | C | MM minutes | D | `b5235dba` |
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+ | zm_yunjian | 🚹 | C | MM minutes | D | `76cbf8ba` |
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+ | zm_yunxi | 🚹 | C | MM minutes | D | `dbe6e1ce` |
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+ | zm_yunxia | 🚹 | C | MM minutes | D | `bb2b03b0` |
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+ | zm_yunyang | 🚹 | C | MM minutes | D | `5238ac22` |
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+
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+ ### Spanish
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+
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+ - `lang_code='e'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
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+ - espeak-ng `es`
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+
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+ | Name | Traits | SHA256 |
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+ | ---- | ------ | ------ |
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+ | ef_dora | 🚺 | `d9d69b0f` |
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+ | em_alex | 🚹 | `5eac53f7` |
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+ | em_santa | 🚹 | `aa8620cb` |
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+
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+ ### French
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+
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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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+
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+ | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 | CC BY |
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+ | ---- | ------ | -------------- | ----------------- | ------------- | ------ | ----- |
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+ | ff_siwis | 🚺 | B | <11 hours | B- | `8073bf2d` | [SIWIS](https://datashare.ed.ac.uk/handle/10283/2353) |
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+
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+ ### Hindi
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+
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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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+
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+ | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
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+ | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
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+ | hf_alpha | 🚺 | B | MM minutes | C | `06906fe0` |
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+ | hf_beta | 🚺 | B | MM minutes | C | `63c0a1a6` |
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+ | hm_omega | 🚹 | B | MM minutes | C | `b55f02a8` |
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+ | hm_psi | 🚹 | B | MM minutes | C | `2f0f055c` |
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+ ### Italian
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+
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+ - `lang_code='i'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
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+ - espeak-ng `it`
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+ - Total Italian training data: H hours
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+
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+ | Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
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+ | ---- | ------ | -------------- | ----------------- | ------------- | ------ |
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+ | if_sara | 🚺 | B | MM minutes | C | `6c0b253b` |
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+ | im_nicola | 🚹 | B | MM minutes | C | `234ed066` |
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+
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+ ### Brazilian Portuguese
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+
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+ - `lang_code='p'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
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+ - espeak-ng `pt-br`
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+
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+ | Name | Traits | SHA256 |
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+ | ---- | ------ | ------ |
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+ | pf_dora | 🚺 | `07e4ff98` |
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+ | pm_alex | 🚹 | `cf0ba8c5` |
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+ | pm_santa | 🚹 | `d4210316` |