> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/QwenLM/Qwen3-TTS/llms.txt
> Use this file to discover all available pages before exploring further.

# Language Support

> Multilingual capabilities and language-specific features of Qwen3-TTS

## Overview

Qwen3-TTS provides comprehensive multilingual support, covering **10 major languages** with native-quality synthesis. The models are trained on diverse multilingual data and support cross-lingual voice cloning and generation.

## Supported Languages

<CardGroup cols={3}>
  <Card title="Chinese" icon="language">
    普通话, Beijing Dialect, Sichuan Dialect
  </Card>

  <Card title="English" icon="language">
    American, British, and neutral accents
  </Card>

  <Card title="Japanese" icon="language">
    Standard Japanese (標準語)
  </Card>

  <Card title="Korean" icon="language">
    Standard Korean (표준어)
  </Card>

  <Card title="German" icon="language">
    Standard German (Hochdeutsch)
  </Card>

  <Card title="French" icon="language">
    European French (français européen)
  </Card>

  <Card title="Russian" icon="language">
    Standard Russian (русский)
  </Card>

  <Card title="Portuguese" icon="language">
    Brazilian and European Portuguese
  </Card>

  <Card title="Spanish" icon="language">
    European and Latin American Spanish
  </Card>

  <Card title="Italian" icon="language">
    Standard Italian (italiano)
  </Card>
</CardGroup>

## Language Quality Comparison

### Content Consistency (WER/CER ↓)

Word Error Rate (WER) or Character Error Rate (CER) on multilingual test set - **lower is better**:

| Language       | 1.7B-Base | 0.6B-Base | Quality Tier  |
| -------------- | --------- | --------- | ------------- |
| **Chinese**    | 0.928     | 1.145     | ⭐⭐⭐ Excellent |
| **English**    | 0.934     | 0.836     | ⭐⭐⭐ Excellent |
| **Korean**     | 1.755     | 1.741     | ⭐⭐⭐ Excellent |
| **German**     | 1.235     | 1.089     | ⭐⭐ Very Good  |
| **Italian**    | 0.948     | 1.534     | ⭐⭐ Very Good  |
| **Portuguese** | 1.526     | 2.254     | ⭐⭐ Very Good  |
| **Spanish**    | 1.126     | 1.491     | ⭐⭐ Very Good  |
| **French**     | 2.858     | 2.931     | ⭐ Good        |
| **Russian**    | 3.212     | 4.458     | ⭐ Good        |
| **Japanese**   | 3.823     | 6.404     | ⭐ Good        |

<Note>
  Chinese, English, and Korean achieve the best content accuracy, making them ideal for production applications requiring high precision.
</Note>

### Speaker Similarity (Cosine Similarity ↑)

Speaker embedding similarity on voice cloning tasks - **higher is better**:

| Language       | 1.7B-Base | 0.6B-Base | Quality Tier  |
| -------------- | --------- | --------- | ------------- |
| **English**    | 0.775     | **0.829** | ⭐⭐⭐ Excellent |
| **Portuguese** | **0.817** | 0.794     | ⭐⭐⭐ Excellent |
| **Spanish**    | **0.814** | 0.812     | ⭐⭐⭐ Excellent |
| **Italian**    | **0.817** | 0.792     | ⭐⭐⭐ Excellent |
| **Chinese**    | 0.799     | **0.811** | ⭐⭐⭐ Excellent |
| **Korean**     | 0.799     | **0.812** | ⭐⭐⭐ Excellent |
| **Russian**    | **0.792** | 0.781     | ⭐⭐⭐ Excellent |
| **Japanese**   | **0.788** | 0.798     | ⭐⭐ Very Good  |
| **German**     | **0.775** | 0.769     | ⭐⭐ Very Good  |
| **French**     | **0.714** | 0.700     | ⭐⭐ Very Good  |

<Info>
  All languages achieve strong speaker similarity (>0.70), indicating excellent voice cloning capabilities across the board.
</Info>

## Speaker Native Languages

For **CustomVoice models**, the following 9 premium speakers are available:

| Speaker       | Voice Description                     | Native Language   | Recommended Languages |
| ------------- | ------------------------------------- | ----------------- | --------------------- |
| **Vivian**    | Bright, slightly edgy young female    | Chinese           | Chinese, English      |
| **Serena**    | Warm, gentle young female             | Chinese           | Chinese, English      |
| **Uncle\_Fu** | Seasoned male, low mellow timbre      | Chinese           | Chinese               |
| **Dylan**     | Youthful Beijing male, clear natural  | Chinese (Beijing) | Chinese, English      |
| **Eric**      | Lively Chengdu male, slightly husky   | Chinese (Sichuan) | Chinese               |
| **Ryan**      | Dynamic male, strong rhythmic drive   | English           | English, Chinese      |
| **Aiden**     | Sunny American male, clear midrange   | English           | English               |
| **Ono\_Anna** | Playful Japanese female, light nimble | Japanese          | Japanese, English     |
| **Sohee**     | Warm Korean female, rich emotion      | Korean            | Korean, English       |

<Tip>
  For best quality, use each speaker's native language. However, all speakers can speak any of the 10 supported languages with reasonable quality.
</Tip>

## Cross-Lingual Capabilities

Qwen3-TTS supports **cross-lingual voice cloning**, allowing you to clone a voice in one language and generate speech in another.

### Cross-Lingual Performance

Mixed Error Rate (WER for English, CER for others) on cross-lingual benchmark - **lower is better**:

| Task                   | 1.7B-Base | 0.6B-Base | Quality       |
| ---------------------- | --------- | --------- | ------------- |
| **Korean → English**   | **3.09**  | 3.48      | ⭐⭐⭐ Excellent |
| **Japanese → English** | **3.04**  | 3.95      | ⭐⭐⭐ Excellent |
| **English → Chinese**  | **4.77**  | 5.66      | ⭐⭐ Very Good  |
| **Korean → Japanese**  | **3.67**  | 4.17      | ⭐⭐ Very Good  |
| **Korean → Chinese**   | **4.82**  | 8.12      | ⭐⭐ Very Good  |
| **English → Korean**   | **5.14**  | 6.83      | ⭐⭐ Very Good  |
| **Japanese → Korean**  | **5.59**  | 6.86      | ⭐⭐ Very Good  |
| **English → Japanese** | 7.21      | 7.74      | ⭐ Good        |
| **Chinese → Japanese** | 8.40      | 9.29      | ⭐ Good        |

<Info>
  The 1.7B model generally performs better on cross-lingual tasks, especially for Korean and Japanese target languages.
</Info>

### Cross-Lingual Example

```python theme={null}
from qwen_tts import Qwen3TTSModel

model = Qwen3TTSModel.from_pretrained(
    "Qwen/Qwen3-TTS-12Hz-1.7B-Base"
)

# Clone English voice and speak Chinese
wavs, sr = model.generate_voice_clone(
    text="你好，很高兴见到你。",  # Chinese text
    language="Chinese",
    ref_audio="english_speaker.wav",  # English reference
    ref_text="Hello, nice to meet you."
)
```

## Language-Specific Considerations

<AccordionGroup>
  <Accordion title="Chinese (中文)">
    **Strengths**:

    * Excellent accuracy (WER \~0.93 for 1.7B)
    * Strong dialect support (Beijing, Sichuan)
    * Native speakers available (Vivian, Serena, Uncle\_Fu, Dylan, Eric)

    **Considerations**:

    * Tone accuracy is critical; may occasionally flatten in complex prosody
    * Text input should use simplified or traditional Chinese consistently
    * Pinyin input not officially supported

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_custom_voice(
        text="这是一段测试文本，包含了复杂的语调和情感。",
        language="Chinese",  # Always specify
        speaker="Vivian",
        instruct="温柔地说，带有一点兴奋的语气"  # Chinese instructions work best
    )
    ```
  </Accordion>

  <Accordion title="English">
    **Strengths**:

    * Excellent accuracy (WER \~0.93 for 1.7B)
    * Multiple native speakers (Ryan, Aiden)
    * Strong cross-lingual source language

    **Considerations**:

    * Accents: models default to neutral/American accent
    * British English: supported but may sound slightly American-influenced
    * Contractions and informal speech handled well

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_custom_voice(
        text="I'm really excited to show you what we've built!",
        language="English",
        speaker="Ryan",
        instruct="enthusiastic and energetic, slightly faster pace"
    )
    ```
  </Accordion>

  <Accordion title="Japanese (日本語)">
    **Strengths**:

    * Native speaker available (Ono\_Anna)
    * Good speaker similarity (0.788)

    **Considerations**:

    * Higher character error rate (\~3.8-6.4%)
    * Pitch accent may not always be perfect
    * Kanji, hiragana, and katakana all supported

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_custom_voice(
        text="こんにちは、お元気ですか？今日はいい天気ですね。",
        language="Japanese",
        speaker="Ono_Anna",  # Use native speaker for best quality
        instruct="明るく元気な声で"  # Japanese instructions recommended
    )
    ```
  </Accordion>

  <Accordion title="Korean (한국어)">
    **Strengths**:

    * Excellent accuracy (WER \~1.75)
    * Native speaker available (Sohee)
    * Strong cross-lingual capabilities

    **Considerations**:

    * Hangul input only (no romanization)
    * Handles formal and informal speech

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_custom_voice(
        text="안녕하세요, 만나서 반갑습니다.",
        language="Korean",
        speaker="Sohee",
        instruct="따뜻하고 친근한 목소리로"  # Korean instructions
    )
    ```
  </Accordion>

  <Accordion title="German (Deutsch)">
    **Strengths**:

    * Good accuracy (WER \~1.09-1.24)
    * Good speaker similarity (0.77)

    **Considerations**:

    * Compound words handled well
    * Umlauts (ä, ö, ü) supported
    * May occasionally anglicize pronunciation

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_voice_clone(
        text="Guten Tag! Wie geht es Ihnen heute?",
        language="German",
        ref_audio="german_speaker.wav",
        ref_text="Hallo, schön Sie kennenzulernen."
    )
    ```
  </Accordion>

  <Accordion title="French (Français)">
    **Strengths**:

    * Handles liaison and elision
    * Reasonable accuracy for European languages

    **Considerations**:

    * Moderate error rate (\~2.86-2.93)
    * Nasal vowels may be approximated
    * Accents (é, è, ê, etc.) should be included

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_voice_clone(
        text="Bonjour ! Comment allez-vous aujourd'hui ?",
        language="French",
        ref_audio="french_speaker.wav",
        ref_text="Enchanté de faire votre connaissance."
    )
    ```
  </Accordion>

  <Accordion title="Russian (Русский)">
    **Strengths**:

    * Strong speaker similarity (0.79)
    * Handles Cyrillic script

    **Considerations**:

    * Moderate error rate (\~3.2-4.5)
    * Stress patterns may not always be perfect
    * Cyrillic input required

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_voice_clone(
        text="Привет! Как дела?",
        language="Russian",
        ref_audio="russian_speaker.wav",
        ref_text="Здравствуйте, рад познакомиться."
    )
    ```
  </Accordion>

  <Accordion title="Portuguese (Português)">
    **Strengths**:

    * Excellent speaker similarity (0.817)
    * Supports both Brazilian and European variants

    **Considerations**:

    * Error rate \~1.5-2.3
    * Diacritics (ã, õ, ç) should be included
    * May default to Brazilian pronunciation

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_voice_clone(
        text="Olá! Como você está hoje?",
        language="Portuguese",
        ref_audio="portuguese_speaker.wav",
        ref_text="Prazer em conhecê-lo."
    )
    ```
  </Accordion>

  <Accordion title="Spanish (Español)">
    **Strengths**:

    * Excellent speaker similarity (0.814)
    * Good accuracy (WER \~1.13-1.49)
    * Supports European and Latin American variants

    **Considerations**:

    * Accent marks (á, é, í, ó, ú) should be included
    * ñ character supported
    * May default to Castilian pronunciation

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_voice_clone(
        text="¡Hola! ¿Cómo estás hoy?",
        language="Spanish",
        ref_audio="spanish_speaker.wav",
        ref_text="Mucho gusto en conocerte."
    )
    ```
  </Accordion>

  <Accordion title="Italian (Italiano)">
    **Strengths**:

    * Excellent speaker similarity (0.817)
    * Good accuracy (WER \~0.95-1.53)
    * Handles double consonants well

    **Considerations**:

    * Accent marks (à, è, é, ì, ò, ù) should be included
    * Regional accents not explicitly supported

    **Best Practices**:

    ```python theme={null}
    wavs, sr = model.generate_voice_clone(
        text="Ciao! Come stai oggi?",
        language="Italian",
        ref_audio="italian_speaker.wav",
        ref_text="Piacere di conoscerti."
    )
    ```
  </Accordion>
</AccordionGroup>

## Automatic Language Detection

Qwen3-TTS supports automatic language detection when `language="Auto"` is specified:

```python theme={null}
# Automatic language detection
wavs, sr = model.generate_custom_voice(
    text="Hello, 你好, こんにちは",
    language="Auto",  # Detects mixed languages
    speaker="Vivian"
)
```

<Warning>
  **Limitations of Auto mode**:

  * May misdetect short phrases or ambiguous text
  * Mixed-language text (code-switching) may be inconsistent
  * For best quality, explicitly specify the language when known
</Warning>

## Multilingual Generation Tips

<Steps>
  <Step title="Choose the Right Speaker">
    Use native speakers for best quality:

    * Chinese text → Vivian, Serena, Uncle\_Fu, Dylan, Eric
    * English text → Ryan, Aiden
    * Japanese text → Ono\_Anna
    * Korean text → Sohee
  </Step>

  <Step title="Specify Language Explicitly">
    Always specify language when known to avoid detection errors:

    ```python theme={null}
    language="Chinese"  # Better than language="Auto"
    ```
  </Step>

  <Step title="Use Native-Language Instructions">
    Instructions work best in the same language as the content:

    ```python theme={null}
    # Chinese text with Chinese instruction
    text="你好", instruct="温柔的语气"

    # English text with English instruction  
    text="Hello", instruct="gentle tone"
    ```
  </Step>

  <Step title="Test Cross-Lingual Carefully">
    Cross-lingual quality varies by language pair. Test thoroughly:

    * English → Chinese: Very good
    * Japanese → Chinese: Good
    * Chinese → Japanese: Moderate
  </Step>
</Steps>

## Language Roadmap

<Info>
  **Future language support** (tentative):

  * Arabic (العربية)
  * Hindi (हिन्दी)
  * Turkish (Türkçe)
  * Vietnamese (Tiếng Việt)
  * Thai (ไทย)

  Check the [GitHub repository](https://github.com/QwenLM/Qwen3-TTS) for updates.
</Info>

## Next Steps

<CardGroup cols={2}>
  <Card title="Voice Cloning" icon="clone" href="/guides/voice-cloning">
    Learn how to clone voices across languages
  </Card>

  <Card title="Custom Voice" icon="microphone" href="/guides/custom-voice">
    Use premium speakers in different languages
  </Card>

  <Card title="Voice Design" icon="palette" href="/guides/voice-design">
    Create voices with language-specific characteristics
  </Card>

  <Card title="Examples" icon="code" href="/examples/multilingual">
    See multilingual code examples
  </Card>
</CardGroup>