Keyboard shortcuts

Press or to navigate between chapters

Press S or / to search in the book

Press ? to show this help

Press Esc to hide this help

supertonic-3-en

Info about this model

This model is supertonic 3 from https://huggingface.co/Supertone/supertonic-3

It supports 31 languages: en, ko, ja, ar, bg, cs, da, de, el, es, et, fi, fr, hi, hr, hu, id, it, lt, lv, nl, pl, pt, ro, ru, sk, sl, sv, tr, uk, vi.

This page shows samples for English (en).

Number of speakersSample rate
1024000

Speaker IDs

sid0123456789

Download the model

Click to expand

Model download address

https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/sherpa-onnx-supertonic-3-tts-int8-2026-05-11.tar.bz2

Android APK

Click to expand

The following table shows the Android TTS Engine APK with this model for sherpa-onnx v1.13.2

ABIURL中国镜像
arm64-v8aDownload下载
armeabi-v7aDownload下载
x86_64Download下载
x86Download下载

If you don’t know what ABI is, you probably need to select arm64-v8a.

The source code for the APK can be found at

https://github.com/k2-fsa/sherpa-onnx/tree/master/android/SherpaOnnxTtsEngine

Please refer to the documentation for how to build the APK from source code.

More Android APKs can be found at

https://k2-fsa.github.io/sherpa/onnx/tts/apk-engine.html

Python API

Click to expand

Assume you have installed sherpa-onnx via

pip install sherpa-onnx

and you have downloaded the model from

https://github.com/k2-fsa/sherpa-onnx/releases/download/tts-models/sherpa-onnx-supertonic-3-tts-int8-2026-05-11.tar.bz2

You can use the following code to play with supertonic-3

import sherpa_onnx
import soundfile as sf

tts_config = sherpa_onnx.OfflineTtsConfig(
    model=sherpa_onnx.OfflineTtsModelConfig(
        supertonic=sherpa_onnx.OfflineTtsSupertonicModelConfig(
            duration_predictor="sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx",
            text_encoder="sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx",
            vector_estimator="sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx",
            vocoder="sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx",
            tts_json="sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json",
            unicode_indexer="sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin",
            voice_style="sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin",
        ),
        debug=False,
        num_threads=2,
        provider="cpu",
    ),
)

tts = sherpa_onnx.OfflineTts(tts_config)

gen_config = sherpa_onnx.GenerationConfig()
gen_config.sid = 0
gen_config.num_steps = 8
gen_config.speed = 1.0
gen_config.extra["lang"] = "en"

audio = tts.generate("How are you doing today? This is a text-to-speech engine using next generation Kaldi.", gen_config)

sf.write("test.wav", audio.samples, samplerate=audio.sample_rate)

C API

Click to expand

You can use the following code to play with supertonic-3 with C API.

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#include "sherpa-onnx/c-api/c-api.h"

static int32_t ProgressCallback(const float *samples, int32_t num_samples,
                                float progress, void *arg) {
  fprintf(stderr, "Progress: %.3f%%\n", progress * 100);
  // return 1 to continue generating
  // return 0 to stop generating
  return 1;
}

int32_t main(int32_t argc, char *argv[]) {
  SherpaOnnxOfflineTtsConfig config;
  memset(&config, 0, sizeof(config));
  config.model.supertonic.duration_predictor = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx";
  config.model.supertonic.text_encoder = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx";
  config.model.supertonic.vector_estimator = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx";
  config.model.supertonic.vocoder = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx";
  config.model.supertonic.tts_json = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json";
  config.model.supertonic.unicode_indexer = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin";
  config.model.supertonic.voice_style = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin";

  config.model.num_threads = 2;

  // If you don't want to see debug messages, please set it to 0
  config.model.debug = 1;

  const char *text = "How are you doing today? This is a text-to-speech engine using next generation Kaldi.";

  const SherpaOnnxOfflineTts *tts = SherpaOnnxCreateOfflineTts(&config);

  SherpaOnnxGenerationConfig gen_cfg;
  memset(&gen_cfg, 0, sizeof(gen_cfg));
  gen_cfg.sid = 0;
  gen_cfg.num_steps = 8;
  gen_cfg.speed = 1.0;
  gen_cfg.extra = "{\"lang\": \"en\"}";

  const SherpaOnnxGeneratedAudio *audio =
      SherpaOnnxOfflineTtsGenerateWithConfig(tts, text, &gen_cfg,
                                             ProgressCallback, NULL);

  SherpaOnnxWriteWave(audio->samples, audio->n, audio->sample_rate,
                      "./test.wav");

  // You need to free the pointers to avoid memory leak in your app
  SherpaOnnxDestroyOfflineTtsGeneratedAudio(audio);
  SherpaOnnxDestroyOfflineTts(tts);

  printf("Saved to ./test.wav\n");

  return 0;
}
cd /tmp
git clone https://github.com/k2-fsa/sherpa-onnx
cd sherpa-onnx
mkdir build-shared
cd build-shared

cmake \
 -DSHERPA_ONNX_ENABLE_C_API=ON \
 -DCMAKE_BUILD_TYPE=Release \
 -DBUILD_SHARED_LIBS=ON \
 -DCMAKE_INSTALL_PREFIX=/tmp/sherpa-onnx/shared \
 ..

make
make install

You can find required header file and library files inside /tmp/sherpa-onnx/shared.

Assume you have saved the above example file as /tmp/test-supertonic.c. Then you can compile it with the following command:

gcc \
  -I /tmp/sherpa-onnx/shared/include \
  -L /tmp/sherpa-onnx/shared/lib \
  -lsherpa-onnx-c-api \
  -lonnxruntime \
  -o /tmp/test-supertonic \
  /tmp/test-supertonic.c

Now you can run

cd /tmp

# Assume you have downloaded the model and extracted it to /tmp
./test-supertonic

You probably need to run

# For Linux
export LD_LIBRARY_PATH=/tmp/sherpa-onnx/shared/lib:$LD_LIBRARY_PATH

# For macOS
export DYLD_LIBRARY_PATH=/tmp/sherpa-onnx/shared/lib:$DYLD_LIBRARY_PATH

before you run /tmp/test-supertonic.

Please see the documentation at

https://k2-fsa.github.io/sherpa/onnx/c-api/index.html

C++ API

Click to expand

You can use the following code to play with supertonic-3 with C++ API.

#include <cstdint>
#include <cstdio>
#include <string>

#include "sherpa-onnx/c-api/cxx-api.h"

static int32_t ProgressCallback(const float *samples, int32_t num_samples,
                                float progress, void *arg) {
  fprintf(stderr, "Progress: %.3f%%\n", progress * 100);
  // return 1 to continue generating
  // return 0 to stop generating
  return 1;
}

int32_t main(int32_t argc, char *argv[]) {
  using namespace sherpa_onnx::cxx; // NOLINT
  OfflineTtsConfig config;

  config.model.supertonic.duration_predictor = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx";
  config.model.supertonic.text_encoder = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx";
  config.model.supertonic.vector_estimator = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx";
  config.model.supertonic.vocoder = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx";
  config.model.supertonic.tts_json = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json";
  config.model.supertonic.unicode_indexer = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin";
  config.model.supertonic.voice_style = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin";

  config.model.num_threads = 2;

  // If you don't want to see debug messages, please set it to 0
  config.model.debug = 1;

  std::string filename = "./test.wav";
  std::string text = "How are you doing today? This is a text-to-speech engine using next generation Kaldi.";

  auto tts = OfflineTts::Create(config);

  GenerationConfig gen_cfg;
  gen_cfg.sid = 0;
  gen_cfg.num_steps = 8;
  gen_cfg.speed = 1.0; // larger -> faster in speech speed
  gen_cfg.extra = R"({"lang": "en"})";

  GeneratedAudio audio = tts.Generate(text, gen_cfg, ProgressCallback);

  WriteWave(filename, {audio.samples, audio.sample_rate});

  fprintf(stderr, "Input text is: %s\n", text.c_str());
  fprintf(stderr, "Speaker ID is: %d\n", gen_cfg.sid);
  fprintf(stderr, "Saved to: %s\n", filename.c_str());

  return 0;
}
cd /tmp
git clone https://github.com/k2-fsa/sherpa-onnx
cd sherpa-onnx
mkdir build-shared
cd build-shared

cmake \
 -DSHERPA_ONNX_ENABLE_C_API=ON \
 -DCMAKE_BUILD_TYPE=Release \
 -DBUILD_SHARED_LIBS=ON \
 -DCMAKE_INSTALL_PREFIX=/tmp/sherpa-onnx/shared \
 ..

make
make install

You can find required header file and library files inside /tmp/sherpa-onnx/shared.

Assume you have saved the above example file as /tmp/test-supertonic.cc. Then you can compile it with the following command:

g++ \
  -std=c++17 \
  -I /tmp/sherpa-onnx/shared/include \
  -L /tmp/sherpa-onnx/shared/lib \
  -lsherpa-onnx-cxx-api \
  -lsherpa-onnx-c-api \
  -lonnxruntime \
  -o /tmp/test-supertonic \
  /tmp/test-supertonic.cc

Now you can run

cd /tmp

# Assume you have downloaded the model and extracted it to /tmp
./test-supertonic

You probably need to run

# For Linux
export LD_LIBRARY_PATH=/tmp/sherpa-onnx/shared/lib:$LD_LIBRARY_PATH

# For macOS
export DYLD_LIBRARY_PATH=/tmp/sherpa-onnx/shared/lib:$DYLD_LIBRARY_PATH

before you run /tmp/test-supertonic.

Please see the documentation at

https://k2-fsa.github.io/sherpa/onnx/c-api/index.html

Rust API

Click to expand

You can use the following code to play with supertonic-3 with Rust API.

use sherpa_onnx::{
    GenerationConfig, OfflineTts, OfflineTtsConfig, OfflineTtsSupertonicModelConfig,
};

fn main() {
    let config = OfflineTtsConfig {
        model: sherpa_onnx::OfflineTtsModelConfig {
            supertonic: OfflineTtsSupertonicModelConfig {
                duration_predictor: Some("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx".into()),
                text_encoder: Some("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx".into()),
                vector_estimator: Some("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx".into()),
                vocoder: Some("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx".into()),
                tts_json: Some("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json".into()),
                unicode_indexer: Some("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin".into()),
                voice_style: Some("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin".into()),
                ..Default::default()
            },
            num_threads: 2,
            debug: false,
            ..Default::default()
        },
        ..Default::default()
    };

    let tts = OfflineTts::create(&config).expect("Failed to create OfflineTts");

    println!("Sample rate: {}", tts.sample_rate());
    println!("Num speakers: {}", tts.num_speakers());

    let text = "How are you doing today? This is a text-to-speech engine using next generation Kaldi.";

    let gen_config = GenerationConfig {
        sid: 0,
        num_steps: 8,
        speed: 1.0,
        extra: Some(r#"{"lang": "en"}"#.into()),
        ..Default::default()
    };

    let audio = tts
        .generate_with_config(
            text,
            &gen_config,
            Some(|_samples: &[f32], progress: f32| -> bool {
                println!("Progress: {:.1}%", progress * 100.0);
                true
            }),
        )
        .expect("Generation failed");

    let filename = "./test.wav";
    if audio.save(filename) {
        println!("Saved to: {}", filename);
    } else {
        eprintln!("Failed to save {}", filename);
    }
}

Please refer to the Rust API documentation for how to build and run the above Rust example.

Node.js (addon) API

Click to expand

You need to install the sherpa-onnx-node npm package first:

npm install sherpa-onnx-node

You can use the following code to play with supertonic-3 with the Node.js addon API.

const sherpa_onnx = require('sherpa-onnx-node');

function createOfflineTts() {
  const config = {
    model: {
      supertonic: {
        durationPredictor: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx',
        textEncoder: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx',
        vectorEstimator: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx',
        vocoder: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx',
        ttsJson: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json',
        unicodeIndexer: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin',
        voiceStyle: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin',
      },
      debug: true,
      numThreads: 2,
      provider: 'cpu',
    },
  };
  return new sherpa_onnx.OfflineTts(config);
}

const tts = createOfflineTts();

const text = 'How are you doing today? This is a text-to-speech engine using next generation Kaldi.';

const generationConfig = new sherpa_onnx.GenerationConfig({
  sid: 0,
  numSteps: 8,
  speed: 1.0,
  extra: {lang: 'en'},
});

let start = Date.now();
const audio = tts.generate({text, generationConfig});
let stop = Date.now();
const elapsed_seconds = (stop - start) / 1000;
const duration = audio.samples.length / audio.sampleRate;
const real_time_factor = elapsed_seconds / duration;
console.log('Wave duration', duration.toFixed(3), 'seconds');
console.log('Elapsed', elapsed_seconds.toFixed(3), 'seconds');
console.log(
    `RTF = ${elapsed_seconds.toFixed(3)}/${duration.toFixed(3)} =`,
    real_time_factor.toFixed(3));

const filename = 'test.wav';
sherpa_onnx.writeWave(
    filename, {samples: audio.samples, sampleRate: audio.sampleRate});

console.log(`Saved to ${filename}`);

Please refer to the Node.js addon API documentation for more details.

Dart API

Click to expand

You can use the following code to play with supertonic-3 with Dart API.

import 'package:sherpa_onnx/sherpa_onnx.dart' as sherpa_onnx;

void main() {
  final supertonic = sherpa_onnx.OfflineTtsSupertonicModelConfig(
    durationPredictor: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx',
    textEncoder: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx',
    vectorEstimator: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx',
    vocoder: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx',
    ttsJson: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json',
    unicodeIndexer: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin',
    voiceStyle: 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin',
  );

  final modelConfig = sherpa_onnx.OfflineTtsModelConfig(
    supertonic: supertonic,
    numThreads: 2,
    debug: true,
  );
  final config = sherpa_onnx.OfflineTtsConfig(
    model: modelConfig,
  );

  final tts = sherpa_onnx.OfflineTts(config);
  final genConfig = sherpa_onnx.OfflineTtsGenerationConfig(
    sid: 0,
    numSteps: 8,
    speed: 1.0,
    extra: {'lang': 'en'},
  );
  final audio = tts.generateWithConfig(text: 'How are you doing today? This is a text-to-speech engine using next generation Kaldi.', config: genConfig);
  tts.free();

  sherpa_onnx.writeWave(
    filename: 'test.wav',
    samples: audio.samples,
    sampleRate: audio.sampleRate,
  );
  print('Saved to test.wav');
}

Please refer to the Dart API documentation for more details.

Swift API

Click to expand

You can use the following code to play with supertonic-3 with Swift API.

func run() {
  let supertonic = sherpaOnnxOfflineTtsSupertonicModelConfig(
    durationPredictor: "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx",
    textEncoder: "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx",
    vectorEstimator: "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx",
    vocoder: "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx",
    ttsJson: "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json",
    unicodeIndexer: "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin",
    voiceStyle: "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin"
  )
  let modelConfig = sherpaOnnxOfflineTtsModelConfig(supertonic: supertonic)
  var ttsConfig = sherpaOnnxOfflineTtsConfig(model: modelConfig)

  let tts = SherpaOnnxOfflineTtsWrapper(config: &ttsConfig)

  let text = "How are you doing today? This is a text-to-speech engine using next generation Kaldi."
  var genConfig = SherpaOnnxGenerationConfigSwift()
  genConfig.sid = 0
  genConfig.numSteps = 8
  genConfig.speed = 1.0
  genConfig.extra = ["lang": "en"]

  let audio = tts.generateWithConfig(text: text, config: genConfig, callback: nil, arg: nil)
  let filename = "test.wav"
  let ok = audio.save(filename: filename)
  if ok == 1 {
    print("Saved to \(filename)")
  } else {
    print("Failed to save \(filename)")
  }
}

@main
struct App {
  static func main() {
    run()
  }
}

Please refer to the Swift API documentation for more details.

C# API

Click to expand

You can use the following code to play with supertonic-3 with C# API.

using SherpaOnnx;

var config = new OfflineTtsConfig();
config.Model.Supertonic.DurationPredictor = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx";
config.Model.Supertonic.TextEncoder = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx";
config.Model.Supertonic.VectorEstimator = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx";
config.Model.Supertonic.Vocoder = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx";
config.Model.Supertonic.TtsJson = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json";
config.Model.Supertonic.UnicodeIndexer = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin";
config.Model.Supertonic.VoiceStyle = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin";

config.Model.NumThreads = 2;
config.Model.Debug = 1;
config.Model.Provider = "cpu";

var tts = new OfflineTts(config);
var text = "How are you doing today? This is a text-to-speech engine using next generation Kaldi.";

OfflineTtsGenerationConfig genConfig = new OfflineTtsGenerationConfig();
genConfig.Sid = 0;
genConfig.NumSteps = 8;
genConfig.Speed = 1.0f;
genConfig.Extra = "{\"lang\": \"en\"}";

var audio = tts.GenerateWithConfig(text, genConfig, null);
var ok = audio.SaveToWaveFile("./test.wav");

if (ok)
{
  Console.WriteLine("Saved to ./test.wav");
}
else
{
  Console.WriteLine("Failed to save ./test.wav");
}

Please refer to the C# API documentation for more details.

Kotlin API

Click to expand

You can use the following code to play with supertonic-3 with Kotlin API.

package com.k2fsa.sherpa.onnx

fun main() {
  var config = OfflineTtsConfig(
    model = OfflineTtsModelConfig(
      supertonic = OfflineTtsSupertonicModelConfig(
        durationPredictor = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx",
        textEncoder = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx",
        vectorEstimator = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx",
        vocoder = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx",
        ttsJson = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json",
        unicodeIndexer = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin",
        voiceStyle = "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin",
      ),
      numThreads = 2,
      debug = true,
    ),
  )
  val tts = OfflineTts(config = config)
  val genConfig = GenerationConfig(
    sid = 0,
    numSteps = 8,
    speed = 1.0f,
    extra = mapOf("lang" to "en"),
  )
  val audio = tts.generateWithConfigAndCallback(
    text = "How are you doing today? This is a text-to-speech engine using next generation Kaldi.",
    config = genConfig,
    callback = ::callback,
  )
  audio.save(filename = "test.wav")
  tts.release()
  println("Saved to test.wav")
}

fun callback(samples: FloatArray): Int {
  // 1 means to continue
  // 0 means to stop
  return 1
}

Please refer to the Kotlin API documentation for more details.

Java API

Click to expand

You can use the following code to play with supertonic-3 with Java API.

import com.k2fsa.sherpa.onnx.*;

public class TtsDemo {
  public static void main(String[] args) {
    var supertonic = new OfflineTtsSupertonicModelConfig();
    supertonic.setDurationPredictor("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx");
    supertonic.setTextEncoder("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx");
    supertonic.setVectorEstimator("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx");
    supertonic.setVocoder("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx");
    supertonic.setTtsJson("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json");
    supertonic.setUnicodeIndexer("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin");
    supertonic.setVoiceStyle("sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin");

    var modelConfig = new OfflineTtsModelConfig();
    modelConfig.setSupertonic(supertonic);
    modelConfig.setNumThreads(2);
    modelConfig.setDebug(true);

    var config = new OfflineTtsConfig();
    config.setModel(modelConfig);

    var tts = new OfflineTts(config);
    var text = "How are you doing today? This is a text-to-speech engine using next generation Kaldi.";

    var genConfig = new GenerationConfig();
    genConfig.setSid(0);
    genConfig.setNumSteps(8);
    genConfig.setSpeed(1.0f);
    genConfig.setExtra("{\"lang\": \"en\"}");

    var audio = tts.generateWithConfigAndCallback(text, genConfig, (samples) -> {
      // 1 means to continue, 0 means to stop
      return 1;
    });

    audio.save("test.wav");
    tts.release();
    System.out.println("Saved to test.wav");
  }
}

Please refer to the Java API documentation for more details.

Pascal API

Click to expand

You can use the following code to play with supertonic-3 with Pascal API.

program test_supertonic;

{$mode objfpc}

uses
  SysUtils,
  sherpa_onnx;

var
  Config: TSherpaOnnxOfflineTtsConfig;
  Tts: TSherpaOnnxOfflineTts;
  Audio: TSherpaOnnxGeneratedAudio;
  GenConfig: TSherpaOnnxGenerationConfig;

begin
  FillChar(Config, SizeOf(Config), 0);

  Config.Model.Supertonic.DurationPredictor := 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx';
  Config.Model.Supertonic.TextEncoder := 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx';
  Config.Model.Supertonic.VectorEstimator := 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx';
  Config.Model.Supertonic.Vocoder := 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx';
  Config.Model.Supertonic.TtsJson := 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json';
  Config.Model.Supertonic.UnicodeIndexer := 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin';
  Config.Model.Supertonic.VoiceStyle := 'sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin';

  Config.Model.NumThreads := 2;
  Config.Model.Debug := True;

  Tts := TSherpaOnnxOfflineTts.Create(@Config);

  GenConfig.Sid := 0;
  GenConfig.NumSteps := 8;
  GenConfig.Speed := 1.0;
  GenConfig.Extra := '{"lang": "en"}';

  Audio := Tts.GenerateWithConfig('How are you doing today? This is a text-to-speech engine using next generation Kaldi.', @GenConfig, nil);

  WriteWave('./test.wav', Audio.Samples, Audio.N, Audio.SampleRate);

  WriteLn('Saved to ./test.wav');

  Audio.Free;
  Tts.Free;
end.

Please refer to the Pascal API documentation for more details.

Go API

Click to expand

You can use the following code to play with supertonic-3 with Go API.

package main

import (
	"fmt"
	sherpa "github.com/k2-fsa/sherpa-onnx-go/sherpa_onnx"
)

func main() {
	config := sherpa.OfflineTtsConfig{
		Model: sherpa.OfflineTtsModelConfig{
			Supertonic: sherpa.OfflineTtsSupertonicModelConfig{
				DurationPredictor: "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/duration_predictor.int8.onnx",
				TextEncoder:       "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/text_encoder.int8.onnx",
				VectorEstimator:   "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vector_estimator.int8.onnx",
				Vocoder:           "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/vocoder.int8.onnx",
				TtsJson:           "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/tts.json",
				UnicodeIndexer:    "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/unicode_indexer.bin",
				VoiceStyle:        "sherpa-onnx-supertonic-3-tts-int8-2026-05-11/voice.bin",
			},
			NumThreads: 2,
			Debug:      true,
		},
	}

	tts := sherpa.NewOfflineTts(&config)
	defer tts.Delete()

	text := "How are you doing today? This is a text-to-speech engine using next generation Kaldi."

	genConfig := sherpa.GenerationConfig{
		Sid:       0,
		NumSteps:  8,
		Speed:     1.0,
		Extra:     `{"lang": "en"}`,
	}

	audio := tts.GenerateWithConfig(text, &genConfig, nil)

	filename := "./test.wav"
	sherpa.WriteWave(filename, audio.Samples, audio.SampleRate)

	fmt.Printf("Saved to %s\n", filename)
}

Please refer to the Go API documentation for more details.

Samples

sample audios for different speakers are listed below:

Speaker 0

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 1

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 2

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 3

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 4

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 5

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 6

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 7

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 8

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.

Speaker 9

0

Hello world.

1

How are you today?

2

The sky is blue.

3

I love machine learning.

4

Python is awesome.

5

Good morning everyone.

6

Artificial intelligence is growing.

7

Speech synthesis is fascinating.

8

Neural networks are powerful.

9

Text to speech converts text to audio.

10

The quick brown fox jumps over the lazy dog.

11

Machine learning enables computers to learn from data.

12

Natural language processing helps machines understand text.

13

Deep learning has revolutionized artificial intelligence.

14

Speech synthesis technology has advanced significantly.

15

Neural voice cloning can replicate speaking styles.

16

Text normalization is important for proper pronunciation.

17

Voice assistants help us interact with technology naturally.

18

Modern TTS systems use deep learning for high-quality speech.

19

Human computer interaction has become more intuitive.