This narrated lesson adapts Corequisite Skills for Introductory Statistics, a custom course edition compiled by XYZ Homework from OpenStax Contemporary Mathematics, Elementary Algebra 2e, Intermediate Algebra 2e, and Prealgebra 2e. OpenStax, Rice University; complete originals: https://openstax.org/ . The course outline is adapted from Larry Green, Support Course for Elementary Statistics, LibreTexts, https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Support_Course_for_Elementary_Statistics , under Creative Commons Attribution 4.0, https://creativecommons.org/licenses/by/4.0/ . Lessons 2.4 and 6.7 are authored by XYZ Homework. This free lesson adaptation is distributed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0, https://creativecommons.org/licenses/by-nc-sa/4.0/ . XYZ Homework added the narrated explanation, diagrams, captions, transcript, and thinking pause; the course edition records its source corrections.
Narration generated locally using Kokoro-82M v1.0, voice af_heart. Model and inference code: Apache-2.0. Retain and link the official model card, its training acknowledgements, and Apache-2.0 licence.
Primary sources:
https://huggingface.co/hexgrad/Kokoro-82M
https://huggingface.co/hexgrad/Kokoro-82M/blob/f3ff3571791e39611d31c381e3a41a3af07b4987/VOICES.md
https://github.com/hexgrad/kokoro/blob/main/LICENSE
Official model card and training acknowledgements:
---
license: apache-2.0
language:
- en
base_model:
- yl4579/StyleTTS2-LJSpeech
pipeline_tag: text-to-speech
---
**Kokoro** is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects.
🐈 **GitHub**: https://github.com/hexgrad/kokoro
🚀 **Demo**: https://hf.co/spaces/hexgrad/Kokoro-TTS
> [!NOTE]
> As of April 2025, the market rate of Kokoro served over API is **under $1 per million characters of text input**, or under $0.06 per hour of audio output. (On average, 1000 characters of input is about 1 minute of output.) Sources: [ArtificialAnalysis/Replicate at 65 cents per M chars](https://artificialanalysis.ai/text-to-speech/model-family/kokoro#price) and [DeepInfra at 80 cents per M chars](https://deepinfra.com/hexgrad/Kokoro-82M).
>
> This is an Apache-licensed model, and Kokoro has been deployed in numerous projects and commercial APIs. We welcome the deployment of the model in real use cases.
> [!CAUTION]
> Fake websites like kokorottsai_com (snapshot: https://archive.ph/nRRnk) and kokorotts_net (snapshot: https://archive.ph/60opa) are likely scams masquerading under the banner of a popular model.
>
> Any website containing "kokoro" in its root domain (e.g. kokorottsai_com, kokorotts_net) is **NOT owned by and NOT affiliated with this model page or its author**, and attempts to imply otherwise are red flags.
- [Releases](#releases)
- [Usage](#usage)
- [EVAL.md](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/EVAL.md) ↗️
- [SAMPLES.md](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/SAMPLES.md) ↗️
- [VOICES.md](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md) ↗️
- [Model Facts](#model-facts)
- [Training Details](#training-details)
- [Creative Commons Attribution](#creative-commons-attribution)
- [Acknowledgements](#acknowledgements)
### Releases
| Model | Published | Training Data | Langs & Voices | SHA256 |
| ----- | --------- | ------------- | -------------- | ------ |
| **v1.0** | **2025 Jan 27** | **Few hundred hrs** | [**8 & 54**](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/VOICES.md) | `496dba11` |
| [v0.19](https://huggingface.co/hexgrad/kLegacy/tree/main/v0.19) | 2024 Dec 25 | <100 hrs | 1 & 10 | `3b0c392f` |
| Training Costs | v0.19 | v1.0 | **Total** |
| -------------- | ----- | ---- | ----- |
| in A100 80GB GPU hours | 500 | 500 | **1000** |
| average hourly rate | $0.80/h | $1.20/h | **$1/h** |
| in USD | $400 | $600 | **$1000** |
### Usage
You can run this basic cell on [Google Colab](https://colab.research.google.com/). [Listen to samples](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/SAMPLES.md). For more languages and details, see [Advanced Usage](https://github.com/hexgrad/kokoro?tab=readme-ov-file#advanced-usage).
```py
!pip install -q kokoro>=0.9.2 soundfile
!apt-get -qq -y install espeak-ng > /dev/null 2>&1
from kokoro import KPipeline
from IPython.display import display, Audio
import soundfile as sf
import torch
pipeline = KPipeline(lang_code='a')
text = '''
[Kokoro](/kˈOkəɹO/) is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, [Kokoro](/kˈOkəɹO/) can be deployed anywhere from production environments to personal projects.
'''
generator = pipeline(text, voice='af_heart')
for i, (gs, ps, audio) in enumerate(generator):
print(i, gs, ps)
display(Audio(data=audio, rate=24000, autoplay=i==0))
sf.write(f'{i}.wav', audio, 24000)
```
Under the hood, `kokoro` uses [`misaki`](https://pypi.org/project/misaki/), a G2P library at https://github.com/hexgrad/misaki
### Model Facts
**Architecture:**
- StyleTTS 2: https://arxiv.org/abs/2306.07691
- ISTFTNet: https://arxiv.org/abs/2203.02395
- Decoder only: no diffusion, no encoder release
**Architected by:** Li et al @ https://github.com/yl4579/StyleTTS2
**Trained by**: `@rzvzn` on Discord
**Languages:** Multiple
**Model SHA256 Hash:** `496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4`
### Training Details
**Data:** Kokoro was trained exclusively on **permissive/non-copyrighted audio data** and IPA phoneme labels. Examples of permissive/non-copyrighted audio include:
- Public domain audio
- Audio licensed under Apache, MIT, etc
- Synthetic audio[1] generated by closed[2] TTS models from large providers
[1] https://copyright.gov/ai/ai_policy_guidance.pdf
[2] No synthetic audio from open TTS models or "custom voice clones"
**Total Dataset Size:** A few hundred hours of audio
**Total Training Cost:** About $1000 for 1000 hours of A100 80GB vRAM
### Creative Commons Attribution
The following CC BY audio was part of the dataset used to train Kokoro v1.0.
| Audio Data | Duration Used | License | Added to Training Set After |
| ---------- | ------------- | ------- | --------------------------- |
| [Koniwa](https://github.com/koniwa/koniwa) `tnc` | <1h | [CC BY 3.0](https://creativecommons.org/licenses/by/3.0/deed.ja) | v0.19 / 22 Nov 2024 |
| [SIWIS](https://datashare.ed.ac.uk/handle/10283/2353) | <11h | [CC BY 4.0](https://datashare.ed.ac.uk/bitstream/handle/10283/2353/license_text) | v0.19 / 22 Nov 2024 |
### Acknowledgements
- 🛠️ [@yl4579](https://huggingface.co/yl4579) for architecting StyleTTS 2.
- 🏆 [@Pendrokar](https://huggingface.co/Pendrokar) for adding Kokoro as a contender in the TTS Spaces Arena.
- 📊 Thank you to everyone who contributed synthetic training data.
- ❤️ Special thanks to all compute sponsors.
- 👾 Discord server: https://discord.gg/QuGxSWBfQy
- 🪽 Kokoro is a Japanese word that translates to "heart" or "spirit". It is also the name of an [AI in the Terminator franchise](https://terminator.fandom.com/wiki/Kokoro).
Official voice information:
# Voices
- 🇺🇸 [American English](#american-english): 11F 9M
- 🇬🇧 [British English](#british-english): 4F 4M
- 🇯🇵 [Japanese](#japanese): 4F 1M
- 🇨🇳 [Mandarin Chinese](#mandarin-chinese): 4F 4M
- 🇪🇸 [Spanish](#spanish): 1F 2M
- 🇫🇷 [French](#french): 1F
- 🇮🇳 [Hindi](#hindi): 2F 2M
- 🇮🇹 [Italian](#italian): 1F 1M
- 🇧🇷 [Brazilian Portuguese](#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]`](https://github.com/hexgrad/misaki)
- espeak-ng `en-us` fallback
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
| ---- | ------ | -------------- | ----------------- | ------------- | ------ |
| **af\_heart** | 🚺❤️ | | | **A** | `0ab5709b` |
| 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]`](https://github.com/hexgrad/misaki)
- 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]`](https://github.com/hexgrad/misaki)
- 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](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__gongitsune.txt) |
| jf_nezumi | 🚺 | B | _M minutes_ 🤏 | C- | `d83f007a` | [nezuminoyomeiri](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__nezuminoyomeiri.txt) |
| jf_tebukuro | 🚺 | B | MM minutes | C | `0d691790` | [tebukurowokaini](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__tebukurowokaini.txt) |
| jm_kumo | 🚹 | B | _M minutes_ 🤏 | C- | `98340afd` | [kumonoito](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__kumonoito.txt) |
### Mandarin Chinese
- `lang_code='z'` in [`misaki[zh]`](https://github.com/hexgrad/misaki)
- 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]`](https://github.com/hexgrad/misaki)
- espeak-ng `es`
| Name | Traits | SHA256 |
| ---- | ------ | ------ |
| ef_dora | 🚺 | `d9d69b0f` |
| em_alex | 🚹 | `5eac53f7` |
| em_santa | 🚹 | `aa8620cb` |
### French
- `lang_code='f'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
- 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](https://datashare.ed.ac.uk/handle/10283/2353) |
### Hindi
- `lang_code='h'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
- 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]`](https://github.com/hexgrad/misaki)
- 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]`](https://github.com/hexgrad/misaki)
- espeak-ng `pt-br`
| Name | Traits | SHA256 |
| ---- | ------ | ------ |
| pf_dora | 🚺 | `07e4ff98` |
| pm_alex | 🚹 | `cf0ba8c5` |
| pm_santa | 🚹 | `d4210316` |
kokoro licence / source notices:
Apache License
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http://www.apache.org/licenses/
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en-core-web-sm licence / source notices:
# OntoNotes 5
* Author: Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston
* URL: https://catalog.ldc.upenn.edu/LDC2013T19
* License: commercial (licensed by Explosion)
```
```
# ClearNLP Constituent-to-Dependency Conversion
* Author: Emory University
* URL: https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md
* License: Citation provided for reference, no code packaged with model
```
```
# WordNet 3.0
* Author: Princeton University
* URL: https://wordnet.princeton.edu/
* License: WordNet 3.0 License
```
WordNet Release 3.0
This software and database is being provided to you, the LICENSEE, by
Princeton University under the following license. By obtaining, using
and/or copying this software and database, you agree that you have
read, understood, and will comply with these terms and conditions.:
Permission to use, copy, modify and distribute this software and
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WordNet 3.0 Copyright 2006 by Princeton University. All rights reserved.
THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON
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INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR
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The name of Princeton University or Princeton may not be used in
advertising or publicity pertaining to distribution of the software
and/or database. Title to copyright in this software, database and
any associated documentation shall at all times remain with
Princeton University and LICENSEE agrees to preserve same.```
Official voice information:
# Voices
- 🇺🇸 [American English](#american-english): 11F 9M
- 🇬🇧 [British English](#british-english): 4F 4M
- 🇯🇵 [Japanese](#japanese): 4F 1M
- 🇨🇳 [Mandarin Chinese](#mandarin-chinese): 4F 4M
- 🇪🇸 [Spanish](#spanish): 1F 2M
- 🇫🇷 [French](#french): 1F
- 🇮🇳 [Hindi](#hindi): 2F 2M
- 🇮🇹 [Italian](#italian): 1F 1M
- 🇧🇷 [Brazilian Portuguese](#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]`](https://github.com/hexgrad/misaki)
- espeak-ng `en-us` fallback
| Name | Traits | Target Quality | Training Duration | Overall Grade | SHA256 |
| ---- | ------ | -------------- | ----------------- | ------------- | ------ |
| **af\_heart** | 🚺❤️ | | | **A** | `0ab5709b` |
| 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]`](https://github.com/hexgrad/misaki)
- 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]`](https://github.com/hexgrad/misaki)
- 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](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__gongitsune.txt) |
| jf_nezumi | 🚺 | B | _M minutes_ 🤏 | C- | `d83f007a` | [nezuminoyomeiri](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__nezuminoyomeiri.txt) |
| jf_tebukuro | 🚺 | B | MM minutes | C | `0d691790` | [tebukurowokaini](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__tebukurowokaini.txt) |
| jm_kumo | 🚹 | B | _M minutes_ 🤏 | C- | `98340afd` | [kumonoito](https://github.com/koniwa/koniwa/blob/master/source/tnc/tnc__kumonoito.txt) |
### Mandarin Chinese
- `lang_code='z'` in [`misaki[zh]`](https://github.com/hexgrad/misaki)
- 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]`](https://github.com/hexgrad/misaki)
- espeak-ng `es`
| Name | Traits | SHA256 |
| ---- | ------ | ------ |
| ef_dora | 🚺 | `d9d69b0f` |
| em_alex | 🚹 | `5eac53f7` |
| em_santa | 🚹 | `aa8620cb` |
### French
- `lang_code='f'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
- 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](https://datashare.ed.ac.uk/handle/10283/2353) |
### Hindi
- `lang_code='h'` in [`misaki[en]`](https://github.com/hexgrad/misaki)
- 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]`](https://github.com/hexgrad/misaki)
- 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]`](https://github.com/hexgrad/misaki)
- espeak-ng `pt-br`
| Name | Traits | SHA256 |
| ---- | ------ | ------ |
| pf_dora | 🚺 | `07e4ff98` |
| pm_alex | 🚹 | `cf0ba8c5` |
| pm_santa | 🚹 | `d4210316` |
kokoro licence / source notices:
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en-core-web-sm licence / source notices:
Copyright 2021 ExplosionAI GmbH
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en-core-web-sm licence / source notices:
# OntoNotes 5
* Author: Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston
* URL: https://catalog.ldc.upenn.edu/LDC2013T19
* License: commercial (licensed by Explosion)
```
```
# ClearNLP Constituent-to-Dependency Conversion
* Author: Emory University
* URL: https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md
* License: Citation provided for reference, no code packaged with model
```
```
# WordNet 3.0
* Author: Princeton University
* URL: https://wordnet.princeton.edu/
* License: WordNet 3.0 License
```
WordNet Release 3.0
This software and database is being provided to you, the LICENSEE, by
Princeton University under the following license. By obtaining, using
and/or copying this software and database, you agree that you have
read, understood, and will comply with these terms and conditions.:
Permission to use, copy, modify and distribute this software and
database and its documentation for any purpose and without fee or
royalty is hereby granted, provided that you agree to comply with
the following copyright notice and statements, including the disclaimer,
and that the same appear on ALL copies of the software, database and
documentation, including modifications that you make for internal
use or for distribution.
WordNet 3.0 Copyright 2006 by Princeton University. All rights reserved.
THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON
UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR
IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON
UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT-
ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE
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OTHER RIGHTS.
The name of Princeton University or Princeton may not be used in
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and/or database. Title to copyright in this software, database and
any associated documentation shall at all times remain with
Princeton University and LICENSEE agrees to preserve same.```