---
license: mit
language:
- en
pipeline_tag: text-to-speech
---

# wfloat-tts

`wfloat-tts` is a lightweight multi-speaker English VITS text-to-speech model with explicit speaker, emotion, and intensity control.

This repo includes:

- `model.safetensors`: inference weights
- `config.json`: model config and token mapping
- `src/wfloat_tts/`: a small Python inference helper

The repo is set up for standalone inference from the released model files. You do not need the original training codebase to synthesize speech with it.

## Inputs

The intended inference inputs are:

- `text`: the utterance to synthesize
- `sid`: numeric speaker id
- `emotion`: emotion label
- `intensity`: value from `0.0` to `1.0`

You do not need to pass raw control symbols. The Python helper converts `emotion` and `intensity` into the control tokens the model was trained on.

## Install

```bash
pip install -e .
pip install "piper-phonemize==1.3.0" -f https://k2-fsa.github.io/icefall/piper_phonemize
```

Runtime dependencies:

- `torch`
- `numpy`
- `safetensors`
- `piper-phonemize`

`piper-phonemize` is installed separately because the current recommended wheels are hosted here:

- https://k2-fsa.github.io/icefall/piper_phonemize

## Python Example

```python
from wfloat_tts import load_generator, write_wave

generator = load_generator(
    checkpoint_path="model.safetensors",
    config_path="config.json",
)

audio = generator.generate(
    text="Hey there, how are you today?",
    sid=11,
    emotion="neutral",
    intensity=0.5,
)

write_wave("out.wav", audio.samples, audio.sample_rate)
```

## How It Is Conditioned

This model was trained to condition on:

- speaker id
- one emotion control token
- one intensity control token

The reference inference path processes a full utterance, appends one emotion token and one intensity token for the whole utterance, and runs synthesis over that full sequence.

## Speaker IDs

Use numeric `sid` values:

| Speaker | SID |
| --- | ---: |
| `skilled_hero_man` | 0 |
| `skilled_hero_woman` | 1 |
| `fun_hero_man` | 2 |
| `fun_hero_woman` | 3 |
| `strong_hero_man` | 4 |
| `strong_hero_woman` | 5 |
| `mad_scientist_man` | 6 |
| `mad_scientist_woman` | 7 |
| `clever_villain_man` | 8 |
| `clever_villain_woman` | 9 |
| `narrator_man` | 10 |
| `narrator_woman` | 11 |
| `wise_elder_man` | 12 |
| `wise_elder_woman` | 13 |
| `outgoing_anime_man` | 14 |
| `outgoing_anime_woman` | 15 |
| `scary_villain_man` | 16 |
| `scary_villain_woman` | 17 |
| `news_reporter_man` | 18 |
| `news_reporter_woman` | 19 |

## Emotions

Supported emotion labels:

- `neutral`
- `joy`
- `sadness`
- `anger`
- `fear`
- `surprise`
- `dismissive`
- `confusion`

`intensity` is clamped to the range `[0.0, 1.0]` and mapped to one of ten discrete intensity levels.

## Notes

- `model.safetensors` is the main inference artifact in this repo.
- `config.json` includes the token mapping needed by the processor.
- The current release uses a multi-speaker model with 20 speakers.