Rápido TTS
Tiny, fast Brazilian Portuguese text-to-speech. 21M parameters, a 42 MB checkpoint, 48 kHz audio and Apache 2.0. It runs offline on your own GPU or CPU, so no text or audio leaves the machine.
- Reads Brazil right: numbers, money, dates, times, ordinals, units and phone numbers come out the way a Brazilian says them, with 2.94% WER on our numbers & pronunciation test. That is the lowest of the small open pt-BR TTS models we tested.
- Small: 21M parameters and a 42 MB checkpoint, trained from scratch for Brazilian Portuguese.
- Fast: 76× real time on one GPU, with first audio in 129 ms and only 1.2 GB of GPU memory.
- Private: it runs fully offline on your GPU or CPU.
Roadmap
Rápido v1 is the first of a family of small, fast speech models.
- Rápido v1: Brazilian Portuguese TTS, 21M parameters, one built-in voice
- Rápido Clone: zero-shot voice cloning from a few seconds of reference audio
- Rápido Multilingual: one compact model for many languages
Code: github.com/anurag12-webster/rapido-tts
Listen
"Olá! Tudo bem com você? Hoje o dia está lindo em São Paulo."
"O boleto de R$ 1.299,90 vence no dia 05/11/2026, às 14h30."
"Você vem com a gente ou vai ficar em casa? Já são quase oito horas!"
Each clip was made with the default settings (8 steps, guidance 4.0, seed 0), straight from the text shown.
Install
git clone https://github.com/anurag12-webster/rapido-tts && cd rapido-tts
pip install -r requirements.txt
Requires Python 3.10+. For a smaller CPU-only install, install PyTorch first:
pip install torch --index-url https://download.pytorch.org/whl/cpu, then pip install -r requirements.txt.
Use
from rapido import Rapido
tts = Rapido.from_pretrained() # downloads the weights once; GPU if available
result = tts.synthesize("Olá! Tudo bem com você?")
result.save("ola.wav")
synthesize() returns a Result:
result.output["audio"]is float32 mono PCM.result.output["sample_rate"]is 48000.result.output["text"]is the spoken form the model read.result.metricsholds the audio length, the time taken and the speed relative to real time.
Streaming
for chunk in tts.synthesize(long_text, stream=True):
play(chunk.output["audio"], chunk.output["sample_rate"])
Each chunk is one sentence, returned as soon as it is ready. Joined together, the chunks are the full text.
Settings
from rapido import Rapido, Config
tts = Rapido(Config(device="cpu", steps=8, guidance=4.0, speed=1.0, seed=0, cpu_threads=8))
tts.synthesize("Bom dia!", speed=1.1, seed=7) # per-call overrides
| Setting | Default | |
|---|---|---|
steps | 8 | sampling steps; 8 scores the same as 16 |
guidance | 4.0 | classifier-free guidance; 3 to 4 works best |
speed | 1.0 | speaking rate |
seed | 0 | the same seed gives the same audio; None means random |
device | "auto" | "cuda", "cpu" or "auto" |
Command line
python -m rapido "O boleto vence no dia 05/11/2026." -o boleto.wav
python -m rapido -f texto.txt -o texto.wav
Portuguese, as written
Write normally. Rápido expands the written form into words before speaking, and tts.normalize(text) shows you exactly what it will say.
| Written | Spoken |
|---|---|
R$ 1.299,90 | mil duzentos e noventa e nove reais e noventa centavos |
05/11/2026 | cinco de novembro de dois mil e vinte e seis |
às 14h30 | às catorze horas e trinta minutos |
4,5% | quatro vírgula cinco por cento |
200 pessoas · 2 crianças | duzentas pessoas · duas crianças |
1ª colocada, 3º lugar | primeira colocada, terceiro lugar |
século XVIII · Dom Pedro II | século dezoito · dom pedro segundo |
95 km · 16 °C | noventa e cinco quilômetros · dezesseis graus celsius |
(11) 98765-4321 | um um, nove oito sete seis cinco, quatro três dois um |
Sr. Silva, Av. Paulista, nº 1000 | senhor silva, avenida paulista, número mil |
Benchmarks
Brazilian text is full of things written one way and spoken another: prices, dates, times, percentages, ordinals and phone numbers. We built a 40-sentence test of exactly that, gave every model the same raw text, transcribed the audio with Whisper large-v3, and compared the result word by word (numbers expanded to words before scoring).
| Model | Size | WER on numbers, money & dates ↓ |
|---|---|---|
| Rápido TTS | 21M | 2.94% |
| Piper pt-BR (faber, medium) | ~16M | 3.31% |
| Kokoro-82M (pf_dora) | 82M | 4.68% |
| MMS-TTS Portuguese | 36M | 14.88% |
Speed
| One request | First audio | GPU memory | |
|---|---|---|---|
| NVIDIA H100 | 76× real time | 129 ms | 1.2 GB |
| NVIDIA L4 | 29× real time | 273 ms | 1.2 GB |
| Laptop CPU (8 threads) | 1.6× real time | 0.6 s | — |
Measured with plain PyTorch at 8 steps, one request at a time, taking the middle of 3 passes after a warm-up.
How it works
- A convolutional text encoder reads Portuguese letters.
- A duration predictor places each letter on a word–letter timeline.
- A windowed Gaussian aligner gives every audio frame a soft mix of the letters near it.
- A flow-matching diffusion transformer generates 64-dimensional latents at 25 Hz in 8 steps.
- The frozen VoxCPM2 AudioVAE decodes the latents to 48 kHz.
Limitations
- One voice. There is no voice cloning and no emotion control.
- Portuguese only. Foreign names, English words and acronyms are read with Portuguese spelling rules.
- Narration style. The delivery is calm and read-aloud rather than conversational.
- Synthetic speech. Tell listeners when they are hearing an AI voice, and never use it to impersonate anyone.
Acknowledgements
The 48 kHz decoder is the AudioVAE from VoxCPM2 by OpenBMB (Apache 2.0).
License
Citation
@misc{rapido2026,
title = {Rápido: Tiny, Fast Brazilian Portuguese Text-to-Speech},
author = {Kanade, Anurag},
year = {2026},
howpublished = {\url{https://github.com/anurag12-webster/rapido-tts}}
}