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Remove scipy (#139)

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* Remove scipy

* No longer need to replace T

* Update README.md

* Remove numpy version lock

* Update README.md

* Update uv.lock
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hexgrad
2025-03-18 11:16:34 -07:00
committed by GitHub
parent 3f9dd88d6f
commit e43d62643e
7 changed files with 62 additions and 71 deletions
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README.md
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@@ -5,10 +5,31 @@ An inference library for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M)
> **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 cell on [Google Colab](https://colab.research.google.com/). [Listen to samples](https://huggingface.co/hexgrad/Kokoro-82M/blob/main/SAMPLES.md).
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).
```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
### Advanced Usage
You can run this advanced cell on [Google Colab](https://colab.research.google.com/).
```py
# 1️⃣ Install kokoro
!pip install -q kokoro>=0.8.4 soundfile
!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
@@ -50,13 +71,12 @@ 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+'
)
# 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
@@ -66,10 +86,7 @@ for i, (gs, ps, audio) in enumerate(generator):
sf.write(f'{i}.wav', audio, 24000) # save each audio file
```
Under the hood, `kokoro` uses [`misaki`](https://pypi.org/project/misaki/), a G2P library at https://github.com/hexgrad/misaki
### Conda Environment
Use the following conda `environment.yml` if you're facing any dependency issues.
```yaml
name: kokoro
@@ -85,7 +102,6 @@ dependencies:
```
### 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.