Skip to main content

TTS

Project description

kokoro

An inference library for Kokoro-82M. You can pip install kokoro.

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.

Usage

You can run this basic cell on Google Colab. Listen to samples.

!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, a G2P library at https://github.com/hexgrad/misaki

Advanced Usage

You can run this advanced cell on Google Colab.

# 1️⃣ Install kokoro
!pip install -q kokoro>=0.9.2 soundfile
# 2️⃣ Install espeak, used for English OOD fallback and some non-English languages
!apt-get -qq -y install espeak-ng > /dev/null 2>&1
# 🇪🇸 'e' => Spanish es
# 🇫🇷 'f' => French fr-fr
# 🇮🇳 'h' => Hindi hi
# 🇮🇹 'i' => Italian it
# 🇧🇷 'p' => Brazilian Portuguese pt-br

# 3️⃣ Initalize a pipeline
from kokoro import KPipeline
from IPython.display import display, Audio
import soundfile as sf
import torch
# 🇺🇸 'a' => American English, 🇬🇧 'b' => British English
# 🇯🇵 'j' => Japanese: pip install misaki[ja]
# 🇨🇳 'z' => Mandarin Chinese: pip install misaki[zh]
pipeline = KPipeline(lang_code='a') # <= make sure lang_code matches voice

# This text is for demonstration purposes only, unseen during training
text = '''
The sky above the port was the color of television, tuned to a dead channel.
"It's not like I'm using," Case heard someone say, as he shouldered his way through the crowd around the door of the Chat. "It's like my body's developed this massive drug deficiency."
It was a Sprawl voice and a Sprawl joke. The Chatsubo was a bar for professional expatriates; you could drink there for a week and never hear two words in Japanese.

These were to have an enormous impact, not only because they were associated with Constantine, but also because, as in so many other areas, the decisions taken by Constantine (or in his name) were to have great significance for centuries to come. One of the main issues was the shape that Christian churches were to take, since there was not, apparently, a tradition of monumental church buildings when Constantine decided to help the Christian church build a series of truly spectacular structures. The main form that these churches took was that of the basilica, a multipurpose rectangular structure, based ultimately on the earlier Greek stoa, which could be found in most of the great cities of the empire. Christianity, unlike classical polytheism, needed a large interior space for the celebration of its religious services, and the basilica aptly filled that need. We naturally do not know the degree to which the emperor was involved in the design of new churches, but it is tempting to connect this with the secular basilica that Constantine completed in the Roman forum (the so-called Basilica of Maxentius) and the one he probably built in Trier, in connection with his residence in the city at a time when he was still caesar.

[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.
'''
# text = '「もしおれがただ偶然、そしてこうしようというつもりでなくここに立っているのなら、ちょっとばかり絶望するところだな」と、そんなことが彼の頭に思い浮かんだ。'
# text = '中國人民不信邪也不怕邪,不惹事也不怕事,任何外國不要指望我們會拿自己的核心利益做交易,不要指望我們會吞下損害我國主權、安全、發展利益的苦果!'
# text = 'Los partidos políticos tradicionales compiten con los populismos y los movimientos asamblearios.'
# text = 'Le dromadaire resplendissant déambulait tranquillement dans les méandres en mastiquant de petites feuilles vernissées.'
# text = 'ट्रांसपोर्टरों की हड़ताल लगातार पांचवें दिन जारी, दिसंबर से इलेक्ट्रॉनिक टोल कलेक्शनल सिस्टम'
# text = "Allora cominciava l'insonnia, o un dormiveglia peggiore dell'insonnia, che talvolta assumeva i caratteri dell'incubo."
# text = 'Elabora relatórios de acompanhamento cronológico para as diferentes unidades do Departamento que propõem contratos.'

# 4️⃣ Generate, display, and save audio files in a loop.
generator = pipeline(
    text, voice='af_heart', # <= change voice here
    speed=1, split_pattern=r'\n+'
)
# Alternatively, load voice tensor directly:
# voice_tensor = torch.load('path/to/voice.pt', weights_only=True)
# generator = pipeline(
#     text, voice=voice_tensor,
#     speed=1, split_pattern=r'\n+'
# )

for i, (gs, ps, audio) in enumerate(generator):
    print(i)  # i => index
    print(gs) # gs => graphemes/text
    print(ps) # ps => phonemes
    display(Audio(data=audio, rate=24000, autoplay=i==0))
    sf.write(f'{i}.wav', audio, 24000) # save each audio file

Conda Environment

Use the following conda environment.yml if you're facing any dependency issues.

name: kokoro
channels:
  - defaults
dependencies:
  - python==3.9       
  - libstdcxx~=12.4.0 # Needed to load espeak correctly. Try removing this if you're facing issues with Espeak fallback. 
  - pip:
      - kokoro>=0.3.1
      - soundfile
      - misaki[en]

Acknowledgements

kokoro

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

kokoro-0.9.2.tar.gz (26.2 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

kokoro-0.9.2-py3-none-any.whl (32.4 kB view details)

Uploaded Python 3

File details

Details for the file kokoro-0.9.2.tar.gz.

File metadata

  • Download URL: kokoro-0.9.2.tar.gz
  • Upload date:
  • Size: 26.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.5

File hashes

Hashes for kokoro-0.9.2.tar.gz
Algorithm Hash digest
SHA256 46be6378aec9996a07975d4fdd2c4fa1a3436af9002348c3f4e66cdba40a0186
MD5 1bef1424838e0e9997fdd5bed940aee2
BLAKE2b-256 cf5361d249ab5e1c884f148e1e62e139ea7cd3627e1b51fbaace8d035f3b5bd6

See more details on using hashes here.

File details

Details for the file kokoro-0.9.2-py3-none-any.whl.

File metadata

  • Download URL: kokoro-0.9.2-py3-none-any.whl
  • Upload date:
  • Size: 32.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.5

File hashes

Hashes for kokoro-0.9.2-py3-none-any.whl
Algorithm Hash digest
SHA256 ad505af44ec0c091f23ac11b9b08bee76584e5ac38bde3a3f070be40be0cdb14
MD5 8c2485a7b2ac8928089bbfbd4a7a8010
BLAKE2b-256 57d18500d3c05b7f658a3d71faafec245a5fd806534cebc02b3020e9b95009c0

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page