---
language:
  - en
  - es
  - fr
  - hi
  - it
  - pt
  - zh
license: apache-2.0
library_name: transformers
pipeline_tag: text-to-speech
tags:
  - text-to-speech
  - tts
  - multilingual
  - speecht5
  - kokoro
  - sol
---

# Sol

**Sol** is an open text-to-speech engine: type text, get clear speech.

It comes in two parts:

1. **The live engine** — a multilingual, multi-voice TTS service running on
   [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) with **49 voices across
   8 languages**. Self-hosted, CPU-only, and reachable 24/7.
2. **Sol v1** — a `SpeechT5` acoustic model we fine-tuned on public
   LibriSpeech voice data, whose weights live in **this repository**.

**Live demo:** <https://sol-tts.vercel.app> — type a prompt and hear it speak.

## Voices & languages

| Language | Code | Voices |
|---|---|---|
| English (US) | `en-us` | 20 |
| English (UK) | `en-gb` | 8 |
| Chinese (Mandarin) | `zh` | 8 |
| Hindi | `hi` | 4 |
| Spanish | `es` | 3 |
| Portuguese (Brazil) | `pt-br` | 3 |
| Italian | `it` | 2 |
| French | `fr-fr` | 1 |

**49 voices total.** Voice ids look like `af_heart` (American female), `am_michael`
(American male), `bf_emma` (British female), `ef_dora` (Spanish female), and so on.

## Use the live API

```bash
curl -X POST "$SOL_API/api/tts" \
  -H "Content-Type: application/json" \
  -d '{"text":"hello from sol","voice":"af_heart","speed":1.0}' \
  --output sol.wav
```

| Endpoint | Method | Purpose |
|---|---|---|
| `/api/health` | GET | service status |
| `/api/languages` | GET | supported languages + voice counts |
| `/api/voices` | GET | every voice (optional `?lang=en-us`) |
| `/api/tts` | POST | `{text, voice, speed}` → `audio/wav` (24 kHz) |

Audio is silence-trimmed and peak-normalised, so output is clean with no static.

### Multilingual samples

A sample in each language lives in
[`examples/multilingual/`](./examples/multilingual):

<audio controls src="examples/multilingual/en-us.wav"></audio>
<audio controls src="examples/multilingual/es.wav"></audio>
<audio controls src="examples/multilingual/zh.wav"></audio>

## Sol v1 — the fine-tuned SpeechT5 model

This repository also ships **Sol v1**, a `SpeechT5` model fine-tuned on a
single-speaker subset of [LibriSpeech](https://www.openslr.org/12) (87 utterances,
light fine-tune, lr 2e-6) and paired with the `speecht5_hifigan` vocoder.

```python
import numpy as np, soundfile as sf, torch
from transformers import SpeechT5ForTextToSpeech, SpeechT5HifiGan, SpeechT5Processor

repo = "itriedcoding/Sol"

processor = SpeechT5Processor.from_pretrained(repo)
model     = SpeechT5ForTextToSpeech.from_pretrained(repo)
vocoder   = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

speaker = torch.tensor(np.load("speaker_embedding.npy")).unsqueeze(0)

inputs = processor(text="hello, i am sol.", return_tensors="pt")
with torch.no_grad():
    speech = model.generate_speech(inputs["input_ids"], speaker, vocoder=vocoder)

sf.write("sol.wav", speech.numpy(), samplerate=16000)
```

> Keep the speaker embedding at its original scale — it is a raw x-vector with
> norm ≈ 320. Do **not** unit-normalise it, or the voice degrades.

## Self-hosting

The engine is served from a FastAPI app (`server/app.py`) that loads Kokoro-82M
locally and exposes the endpoints above on `127.0.0.1:8000`, then published with a
Cloudflare tunnel. It starts automatically at logon and runs without a GPU.

## Intended use & limitations

- **Intended use:** research, prototyping, accessibility and demos.
- **Limitations:** 8 languages; may mispronounce rare names, numbers and unusual
  punctuation; Japanese is not currently enabled (G2P dependency).
- Output is not a human voice and must not be used for impersonation or deceptive
  audio.

## Credits

- Engine: [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M) by hexgrad.
- SpeechT5 v1 voice data: [LibriSpeech](https://www.openslr.org/12), CC BY 4.0.
- Base model & vocoder: Microsoft Research Asia, via 🤗 Transformers.
- Fine-tuned, hosted and packaged by the **Sol** project.

## License

Apache-2.0. Please respect the original dataset, base-model and engine licenses.