# AI use cases for Kokoro (hexgrad/kokoro)

Repo: [hexgrad/kokoro](https://github.com/hexgrad/kokoro) | Use cases: 3 | Page: https://dedupedai.com/use-cases/hexgrad-kokoro/

kokoro is the inference library for Kokoro-82M, an open-weight text-to-speech model with 82 million parameters that rivals larger models while being faster and cheaper to run. A KPipeline turns text into 24 kHz audio with voices like af_heart, and supports American and British English, Spanish, French, Hindi, Italian, Japanese, Brazilian Portuguese and Mandarin.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Turn a script into WAV files in a few lines: https://dedupedai.com/use-cases/hexgrad-kokoro/turn-text-into-speech-with-kokoro/
- Generate speech in Spanish, French, Hindi or Japanese: https://dedupedai.com/use-cases/hexgrad-kokoro/generate-speech-in-other-languages-with-kokoro/
- Speed up Kokoro on an Apple Silicon Mac: https://dedupedai.com/use-cases/hexgrad-kokoro/speed-up-kokoro-on-apple-silicon/

## How to turn a script into WAV files in a few lines with Kokoro

Repo: [hexgrad/kokoro](https://github.com/hexgrad/kokoro) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README

Run a KPipeline with the af_heart voice and save each generated chunk as audio.

**Who it is for:** A creator who needs voiceovers without a paid TTS service.

**The problem:** Cloud TTS costs add up and needs an account.

**The outcome:** Numbered WAV files at 24000 Hz for your text.

### Steps

1. Install with `!pip install -q kokoro>=0.9.4 soundfile` in Colab, or the same pip install locally
2. Install espeak-ng with `!apt-get -qq -y install espeak-ng > /dev/null 2>&1` on Colab or Debian
3. Create KPipeline(lang_code='a') and call it with your text and voice='af_heart'
4. Write each audio chunk with sf.write at 24000

### Prompt to paste into your agent

```text
Using hexgrad/kokoro, install it with pip and espeak-ng, then write a script that reads <my text file> with KPipeline(lang_code='a') and voice af_heart and saves numbered WAV files.
```

### Caveat

This card is written from the hexgrad/kokoro README, not from a test run by Deduped. Before you start, make sure you have Python with pip. Check the repo for newer instructions before you rely on these steps.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/hexgrad-kokoro/turn-text-into-speech-with-kokoro/

## How to generate speech in Spanish, French, Hindi or Japanese with Kokoro

Repo: [hexgrad/kokoro](https://github.com/hexgrad/kokoro) | Difficulty: Beginner | Time: 20 minutes | Basis: From the README

Switch the pipeline's lang_code to voice text in other supported languages.

**Who it is for:** A teacher or creator making audio in several languages.

**The problem:** Many open TTS models only speak English well.

**The outcome:** Audio in the language you pick.

### Steps

1. Install Kokoro and espeak-ng as in the README
2. Pick a lang_code: e for Spanish, f for French, h for Hindi, j for Japanese, z for Mandarin
3. For Japanese or Mandarin install `pip install misaki[ja]` or `pip install misaki[zh]`
4. Create the KPipeline with that code and generate as usual

### Prompt to paste into your agent

```text
With hexgrad/kokoro, generate <my paragraph> in Spanish and French using KPipeline with lang_code e and f, and save each language to its own WAV.
```

### Caveat

This card is written from the hexgrad/kokoro README, not from a test run by Deduped. Before you start, make sure you have Python with pip. Check the repo for newer instructions before you rely on these steps.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/hexgrad-kokoro/generate-speech-in-other-languages-with-kokoro/

## How to speed up Kokoro on an Apple Silicon Mac with Kokoro

Repo: [hexgrad/kokoro](https://github.com/hexgrad/kokoro) | Difficulty: Beginner | Time: 15 minutes | Basis: From the README

Enable MPS fallback so Kokoro uses the Mac GPU.

**Who it is for:** A Mac user generating long audio locally.

**The problem:** CPU-only generation is slow for long scripts.

**The outcome:** Faster local generation on M1 to M4 Macs.

### Steps

1. Install Kokoro with pip
2. Write your Kokoro script, for example run-your-kokoro-script.py
3. Run it with `PYTORCH_ENABLE_MPS_FALLBACK=1 python run-your-kokoro-script.py`
4. If dependencies conflict, use the conda environment.yml from the README

### Prompt to paste into your agent

```text
Using hexgrad/kokoro on my Mac, set up a script and run it with `PYTORCH_ENABLE_MPS_FALLBACK=1 python run-your-kokoro-script.py`, then compare timing with and without the variable.
```

### Caveat

This card is written from the hexgrad/kokoro README, not from a test run by Deduped. Before you start, make sure you have Python with pip. Check the repo for newer instructions before you rely on these steps.

Risk, platform: This tip applies only to Apple Silicon Macs.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/hexgrad-kokoro/speed-up-kokoro-on-apple-silicon/