> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/XDcobra/react-native-sherpa-onnx/llms.txt
> Use this file to discover all available pages before exploring further.

# Other STT Models

> WeNet, SenseVoice, FunASR, Moonshine, and specialized speech recognition models

# Other STT Models

This page covers additional STT model types supported by react-native-sherpa-onnx, including specialized and emerging architectures.

## Overview

<CardGroup cols={2}>
  <Card title="WeNet CTC" icon="cube">
    Compact CTC models from WeNet framework
  </Card>

  <Card title="SenseVoice" icon="heart">
    Multilingual with emotion detection and punctuation
  </Card>

  <Card title="FunASR Nano" icon="brain">
    LLM-based ASR with prompt customization
  </Card>

  <Card title="Moonshine" icon="moon">
    Modern streaming-capable lightweight ASR
  </Card>

  <Card title="Fire Red ASR" icon="fire">
    Encoder-decoder ASR models
  </Card>

  <Card title="Dolphin" icon="fish">
    Single-model CTC for compact deployment
  </Card>

  <Card title="Canary" icon="bird">
    NeMo multilingual model
  </Card>

  <Card title="Omnilingual" icon="globe">
    Wide language coverage CTC model
  </Card>

  <Card title="MedASR" icon="stethoscope">
    Medical ASR for healthcare applications
  </Card>

  <Card title="Telespeech CTC" icon="phone">
    Telephony-optimized CTC model
  </Card>

  <Card title="Tone CTC" icon="gauge">
    Ultra-lightweight streaming CTC (t-one)
  </Card>
</CardGroup>

***

## WeNet CTC

**`modelType: 'wenet_ctc'`**

### Description

CTC models from the WeNet framework, designed for compact deployment.

### Characteristics

* **Streaming**: ❌ No (offline only)
* **Speed**: ⭐⭐⭐⭐⭐ Very Fast
* **Size**: Small (compact models)
* **Languages**: Limited (depends on model variant)

### Configuration

```typescript theme={null}
import { createSTT } from 'react-native-sherpa-onnx/stt';

const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-wenet-chinese' },
  modelType: 'wenet_ctc',
  preferInt8: true,
});
```

### Download

[WeNet CTC Models](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/wenet/index.html)

### Model Detection

* Folder name should contain `wenet`
* Files: `model.onnx`, `tokens.txt`

***

## SenseVoice

**`modelType: 'sense_voice'`**

### Description

Multilingual model with **emotion detection** and automatic punctuation. Excellent for applications requiring sentiment analysis.

### Characteristics

* **Streaming**: ❌ No
* **Accuracy**: ⭐⭐⭐⭐
* **Languages**: Chinese, English, Cantonese, Japanese, Korean
* **Special**: Emotion labels + punctuation

### Configuration

```typescript theme={null}
import { createSTT, getSenseVoiceLanguages } from 'react-native-sherpa-onnx/stt';

const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-sense-voice-zh-en' },
  modelType: 'sense_voice',
  modelOptions: {
    senseVoice: {
      language: 'auto', // 'auto', 'zh', 'en', 'yue', 'ja', 'ko'
      useItn: true,     // Inverse text normalization
    }
  },
});

const result = await stt.transcribeFile('/path/to/audio.wav');
console.log('Text:', result.text);
console.log('Emotion:', result.emotion); // e.g. 'happy', 'neutral'
```

### Language Helpers

```typescript theme={null}
const languages = getSenseVoiceLanguages();
// [{ id: 'auto', name: 'Auto' }, { id: 'zh', name: 'Chinese' }, ...]
```

### Download

[SenseVoice Models](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/sense-voice/index.html)

### Model Detection

* Folder name should contain `sense` or `sensevoice`

***

## FunASR Nano

**`modelType: 'funasr_nano'`**

### Description

Lightweight **LLM-based ASR** with customizable system/user prompts. Supports advanced decoding options.

### Characteristics

* **Streaming**: ❌ No
* **Special**: LLM-based with prompt engineering
* **Languages**: Chinese, English, Japanese (depends on variant)

### Configuration

```typescript theme={null}
import { createSTT, getFunasrNanoLanguages } from 'react-native-sherpa-onnx/stt';

const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-funasr-nano-zh' },
  modelType: 'funasr_nano',
  modelOptions: {
    funasrNano: {
      systemPrompt: 'You are a speech recognition system.',
      userPrompt: 'Transcribe the following audio.',
      language: '中文',        // Chinese labels: '中文', '英文', '日文'
      itn: true,              // Inverse text normalization
      hotwords: 'React Native:2.5,Sherpa ONNX:3.0',
      maxNewTokens: 512,
      temperature: 0.8,
      topP: 0.95,
      seed: 42,
    }
  },
});
```

### Language Helpers

```typescript theme={null}
const languages = getFunasrNanoLanguages();
// [{ id: '中文', name: 'Chinese' }, { id: '英文', name: 'English' }, ...]
```

### Download

[FunASR Nano Models](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/funasr-nano/index.html)

### Model Detection

* Folder name should contain `funasr` or `funasr-nano`
* Files: `encoder_adaptor`, `llm`, `embedding`, tokenizer directory

***

## Moonshine

**`modelType: 'moonshine'` (v1) or `'moonshine_v2'` (v2)**

### Description

Modern streaming-capable ASR with two architecture versions.

**Moonshine v1**: Four-part architecture (preprocess, encode, uncached/cached decode)\
**Moonshine v2**: Two-part architecture (encoder + merged decoder)

### Characteristics

* **Streaming**: ✅ Yes (both v1 and v2)
* **Speed**: ⭐⭐⭐⭐
* **Languages**: Limited (check model variant)

### Configuration

```typescript theme={null}
import { createStreamingSTT } from 'react-native-sherpa-onnx/stt';

// Moonshine v2 (recommended)
const engine = await createStreamingSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-moonshine-v2' },
  modelType: 'auto', // Detects v2 if both present
});

// Moonshine v1
const engineV1 = await createStreamingSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-moonshine-v1' },
  modelType: 'moonshine',
});
```

### Download

[Moonshine Models](https://k2-fsa.github.io/sherpa/onnx/moonshine/index.html)

### Model Detection

* Folder name should contain `moonshine`
* V1: `preprocess.onnx`, `encode.onnx`, `uncached_decode.onnx`, `cached_decode.onnx`
* V2: `encoder.onnx` or `encoder.ort`, merged decoder

***

## Fire Red ASR

**`modelType: 'fire_red_asr'`**

### Description

Encoder-decoder ASR models from the Fire Red project.

### Characteristics

* **Streaming**: ❌ No
* **Speed**: ⭐⭐⭐
* **Languages**: Limited (depends on variant)

### Configuration

```typescript theme={null}
const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-fire-red-asr' },
  modelType: 'fire_red_asr',
});
```

### Download

[Fire Red ASR Models](https://k2-fsa.github.io/sherpa/onnx/FireRedAsr/index.html)

### Model Detection

* Folder name should contain `fire_red` or `fire-red`
* Files: encoder, decoder directories

***

## Dolphin

**`modelType: 'dolphin'`**

### Description

Single-model CTC for compact deployment.

### Characteristics

* **Streaming**: ❌ No
* **Speed**: ⭐⭐⭐⭐⭐
* **Size**: Very Small
* **Languages**: Limited

### Configuration

```typescript theme={null}
const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-dolphin' },
  modelType: 'dolphin',
  preferInt8: true,
});
```

### Download

[Dolphin Models](https://k2-fsa.github.io/sherpa/onnx/Dolphin/index.html)

### Model Detection

* Folder name should contain `dolphin`
* Files: `model.onnx`, `tokens.txt`

***

## Canary

**`modelType: 'canary'`**

### Description

NeMo Canary multilingual model with source/target language configuration.

### Characteristics

* **Streaming**: ❌ No
* **Multilingual**: ✅ Yes (English, Spanish, German, French)
* **Accuracy**: ⭐⭐⭐⭐

### Configuration

```typescript theme={null}
import { createSTT, getCanaryLanguages } from 'react-native-sherpa-onnx/stt';

const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-nemo-canary' },
  modelType: 'canary',
  modelOptions: {
    canary: {
      srcLang: 'en',    // Source: English, Spanish, German, French
      tgtLang: 'en',    // Target (typically 'en')
      usePnc: true,     // Use punctuation
    }
  },
});
```

### Language Helpers

```typescript theme={null}
const languages = getCanaryLanguages();
// [{ id: 'en', name: 'English' }, { id: 'es', name: 'Spanish' }, ...]
```

### Download

[Canary Models](https://k2-fsa.github.io/sherpa/onnx/nemo/canary.html)

### Model Detection

* Folder name should contain `canary`

***

## Omnilingual

**`modelType: 'omnilingual'`**

### Description

Omnilingual CTC model with wide language coverage.

### Characteristics

* **Streaming**: ❌ No
* **Multilingual**: ✅ Yes (many languages)
* **Speed**: ⭐⭐⭐

### Configuration

```typescript theme={null}
const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-omnilingual' },
  modelType: 'omnilingual',
});
```

### Download

[Omnilingual Models](https://k2-fsa.github.io/sherpa/onnx/omnilingual-asr/index.html)

### Model Detection

* Folder name should contain `omnilingual`

***

## MedASR

**`modelType: 'medasr'`**

### Description

Medical ASR CTC model optimized for healthcare terminology.

### Characteristics

* **Streaming**: ❌ No
* **Domain**: Medical/Healthcare
* **Speed**: ⭐⭐⭐⭐

### Configuration

```typescript theme={null}
const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-medasr' },
  modelType: 'medasr',
});
```

### Model Detection

* Folder name should contain `medasr`

***

## Telespeech CTC

**`modelType: 'telespeech_ctc'`**

### Description

Telespeech CTC model optimized for telephony audio.

### Characteristics

* **Streaming**: ❌ No
* **Domain**: Telephony (8kHz audio)
* **Speed**: ⭐⭐⭐⭐

### Configuration

```typescript theme={null}
const stt = await createSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-telespeech' },
  modelType: 'telespeech_ctc',
});
```

### Download

[Telespeech Models](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/telespeech/index.html)

### Model Detection

* Folder name should contain `telespeech`

***

## Tone CTC (t-one)

**`modelType: 'tone_ctc'`**

### Description

Ultra-lightweight **streaming CTC** model (t-one). Excellent for resource-constrained devices.

### Characteristics

* **Streaming**: ✅ Yes
* **Speed**: ⭐⭐⭐⭐⭐ Very Fast
* **Size**: Very Small
* **Memory**: ⭐⭐⭐⭐⭐ Very Low

### Configuration

```typescript theme={null}
import { createStreamingSTT } from 'react-native-sherpa-onnx/stt';

const engine = await createStreamingSTT({
  modelPath: { type: 'asset', path: 'models/sherpa-onnx-streaming-t-one-russian' },
  modelType: 'tone_ctc',
  numThreads: 2,
});
```

### Download

[Tone CTC Models](https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-ctc/index.html)

### Model Detection

* Folder name should contain `t-one`, `t_one`, or the word `tone` (as standalone word)
* Files: `model.onnx`, `tokens.txt`

***

## Comparison Table

| Model            | Streaming | Multilingual | Speed     | Special Feature        |
| ---------------- | --------- | ------------ | --------- | ---------------------- |
| **WeNet CTC**    | ❌         | Limited      | Very Fast | Compact                |
| **SenseVoice**   | ❌         | 5 langs      | Medium    | Emotion + punctuation  |
| **FunASR Nano**  | ❌         | Limited      | Medium    | LLM-based with prompts |
| **Moonshine**    | ✅         | Limited      | Fast      | Modern streaming       |
| **Fire Red ASR** | ❌         | Limited      | Medium    | Encoder-decoder        |
| **Dolphin**      | ❌         | Limited      | Very Fast | Ultra-compact          |
| **Canary**       | ❌         | 4 langs      | Medium    | NeMo multilingual      |
| **Omnilingual**  | ❌         | Many         | Medium    | Wide coverage          |
| **MedASR**       | ❌         | English      | Fast      | Medical domain         |
| **Telespeech**   | ❌         | Limited      | Fast      | Telephony (8kHz)       |
| **Tone CTC**     | ✅         | Limited      | Very Fast | Ultra-lightweight      |

***

## Choosing a Specialized Model

### For Emotion Detection

* **SenseVoice** – Provides emotion labels in result

### For Medical/Healthcare

* **MedASR** – Optimized for medical terminology

### For Telephony

* **Telespeech CTC** – Designed for 8kHz phone audio

### For Low-End Devices

* **Tone CTC** – Ultra-lightweight streaming
* **Dolphin** – Very small offline model
* **WeNet CTC** – Compact deployment

### For LLM-Based Flexibility

* **FunASR Nano** – Prompt engineering for ASR

### For Modern Streaming

* **Moonshine** – Latest streaming architecture
* **Tone CTC** – Lightweight streaming

***

## Next Steps

<CardGroup cols={2}>
  <Card title="STT Overview" icon="list" href="/models/stt/overview">
    Compare all STT model types
  </Card>

  <Card title="STT API" icon="code" href="/api/stt">
    Detailed API documentation
  </Card>

  <Card title="Streaming STT" icon="waveform" href="/api/streaming-stt">
    Real-time recognition guide
  </Card>

  <Card title="Model Setup" icon="download" href="/features/model-setup">
    How to download and bundle models
  </Card>
</CardGroup>