Voices

🇺🇸 American English: 10F 9M
🇬🇧 British English: 4F 4M
🇯🇵 Japanese: 4F 1M
🇨🇳 Mandarin Chinese: 4F 4M
🇪🇸 Spanish: 1F 2M
🇫🇷 French: 1F
🇮🇳 Hindi: 2F 2M
🇮🇹 Italian: 1F 1M
🇧🇷 Brazilian Portuguese: 1F 2M

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.

Subjectively, voices will sound better or worse to different people.

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).

Most voices perform best on a "goldilocks range" of 100-200 tokens out of ~500 possible. Voices may perform worse at the extremes:

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.
Rushing on long utterances, especially over 400 tokens. You can chunk down to shorter utterances or adjust the speed parameter to mitigate this.

Target Quality

How high quality is the reference voice? This grade may be impacted by audio quality, artifacts, compression, & sample rate.
How well do the text labels match the audio? Text/audio misalignment (e.g. from hallucinations) will lower this grade.

Training Duration

How much audio was seen during training? Smaller durations result in a lower overall grade.
10 hours <= HH hours < 100 hours
1 hour <= H hours < 10 hours
10 minutes <= MM minutes < 100 minutes
1 minute <= M minutes 🤏 < 10 minutes

American English

lang_code='a' in misaki[en]
espeak-ng en-us fallback

Name	Traits	Target Quality	Training Duration	Overall Grade	SHA256
af_alloy	🚺	B	MM minutes	C	`6d877149`
af_aoede	🚺	B	H hours	C+	`c03bd1a4`
af_bella	🚺🔥	A	HH hours	A-	`8cb64e02`
af_jessica	🚺	C	MM minutes	D	`cdfdccb8`
af_kore	🚺	B	H hours	C+	`8bfbc512`
af_nicole	🚺🎧	B	HH hours	B-	`c5561808`
af_nova	🚺	B	MM minutes	C	`e0233676`
af_river	🚺	C	MM minutes	D	`e149459b`
af_sarah	🚺	B	H hours	C+	`49bd364e`
af_sky	🚺	B	M minutes 🤏	C-	`c799548a`
am_adam	🚹	D	H hours	F+	`ced7e284`
am_echo	🚹	C	MM minutes	D	`8bcfdc85`
am_eric	🚹	C	MM minutes	D	`ada66f0e`
am_fenrir	🚹	B	H hours	C+	`98e507ec`
am_liam	🚹	C	MM minutes	D	`c8255075`
am_michael	🚹	B	H hours	C+	`9a443b79`
am_onyx	🚹	C	MM minutes	D	`e8452be1`
am_puck	🚹	B	H hours	C+	`dd1d8973`
am_santa	🚹	C	M minutes 🤏	D-	`7f2f7582`

British English

lang_code='b' in misaki[en]
espeak-ng en-gb fallback

Name	Traits	Target Quality	Training Duration	Overall Grade	SHA256
bf_alice	🚺	C	MM minutes	D	`d292651b`
bf_emma	🚺	B	HH hours	B-	`d0a423de`
bf_isabella	🚺	B	MM minutes	C	`cdd4c370`
bf_lily	🚺	C	MM minutes	D	`6e09c2e4`
bm_daniel	🚹	C	MM minutes	D	`fc3fce4e`
bm_fable	🚹	B	MM minutes	C	`d44935f3`
bm_george	🚹	B	MM minutes	C	`f1bc8122`
bm_lewis	🚹	C	H hours	D+	`b5204750`

Japanese

lang_code='j' in misaki[ja]
Total Japanese training data: H hours

Name	Traits	Target Quality	Training Duration	Overall Grade	SHA256	CC BY
jf_alpha	🚺	B	H hours	C+	`1bf4c9dc`
jf_gongitsune	🚺	B	MM minutes	C	`1b171917`	gongitsune
jf_nezumi	🚺	B	M minutes 🤏	C-	`d83f007a`	nezuminoyomeiri
jf_tebukuro	🚺	B	MM minutes	C	`0d691790`	tebukurowokaini
jm_kumo	🚹	B	M minutes 🤏	C-	`98340afd`	kumonoito

Mandarin Chinese

lang_code='z' in misaki[zh]
Total Mandarin Chinese training data: H hours

Name	Traits	Target Quality	Training Duration	Overall Grade	SHA256
zf_xiaobei	🚺	C	MM minutes	D	`9b76be63`
zf_xiaoni	🚺	C	MM minutes	D	`95b49f16`
zf_xiaoxiao	🚺	C	MM minutes	D	`cfaf6f2d`
zf_xiaoyi	🚺	C	MM minutes	D	`b5235dba`
zm_yunjian	🚹	C	MM minutes	D	`76cbf8ba`
zm_yunxi	🚹	C	MM minutes	D	`dbe6e1ce`
zm_yunxia	🚹	C	MM minutes	D	`bb2b03b0`
zm_yunyang	🚹	C	MM minutes	D	`5238ac22`

Spanish

lang_code='e' in misaki[en]
espeak-ng es

Name	Traits	SHA256
ef_dora	🚺	`d9d69b0f`
em_alex	🚹	`5eac53f7`
em_santa	🚹	`aa8620cb`

French

lang_code='f' in misaki[en]
espeak-ng fr-fr
Total French training data: <11 hours

Name	Traits	Target Quality	Training Duration	Overall Grade	SHA256	CC BY
ff_siwis	🚺	B	<11 hours	B-	`8073bf2d`	SIWIS

Hindi

lang_code='h' in misaki[en]
espeak-ng hi
Total Hindi training data: H hours

Name	Traits	Target Quality	Training Duration	Overall Grade	SHA256
hf_alpha	🚺	B	MM minutes	C	`06906fe0`
hf_beta	🚺	B	MM minutes	C	`63c0a1a6`
hm_omega	🚹	B	MM minutes	C	`b55f02a8`
hm_psi	🚹	B	MM minutes	C	`2f0f055c`

Italian

lang_code='i' in misaki[en]
espeak-ng it
Total Italian training data: H hours

Name	Traits	Target Quality	Training Duration	Overall Grade	SHA256
if_sara	🚺	B	MM minutes	C	`6c0b253b`
im_nicola	🚹	B	MM minutes	C	`234ed066`

Brazilian Portuguese

lang_code='p' in misaki[en]
espeak-ng pt-br

Name	Traits	SHA256
pf_dora	🚺	`07e4ff98`
pm_alex	🚹	`cf0ba8c5`
pm_santa	🚹	`d4210316`