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# How to Set the Language

> Defines the language for text normalization before speech synthesis.

Bulbul v3 supports **11 languages (10 Indian + English)**. It is a **required** parameter, always specify it using a valid BCP-47 language code.

The language\_code parameter is required for every TTS request. It is primarily effective for handling language-specific processing of numbers, abbreviations, and special characters.

### Supported Languages:

| Language  | Code    |
| --------- | ------- |
| English   | `en-IN` |
| Hindi     | `hi-IN` |
| Bengali   | `bn-IN` |
| Tamil     | `ta-IN` |
| Telugu    | `te-IN` |
| Kannada   | `kn-IN` |
| Malayalam | `ml-IN` |
| Marathi   | `mr-IN` |
| Gujarati  | `gu-IN` |
| Punjabi   | `pa-IN` |
| Odia      | `od-IN` |

### Example Code

#### Rest API

```python
from sarvamai import SarvamAI
from sarvamai.play import save

# Initialize the REST client
client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")

# Generate speech using REST
audio = client.text_to_speech.convert(
    text="Welcome to Sarvam AI!",
    model="bulbul:v3",
    language_code="en-IN" #Generating speech in English
)
save(audio, "output1.wav")

```

#### Streaming API

```python
import asyncio
import base64
from sarvamai import AsyncSarvamAI, AudioOutput
import websockets

async def tts_stream():
    client = AsyncSarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")

    async with client.text_to_speech_streaming.connect(model="bulbul:v3") as ws:
        await ws.configure(
            language_code="hi-IN", #Generating speech in Hindi
            speaker="shubh"
        )
        print("Sent configuration")

        text = (
            "भारत की संस्कृति विश्व की सबसे प्राचीन और समृद्ध संस्कृतियों में से एक है।"
            "यह विविधता, सहिष्णुता और परंपराओं का अद्भुत संगम है, "
            "जिसमें विभिन्न धर्म, भाषाएं, त्योहार, संगीत, नृत्य, वास्तुकला और जीवनशैली शामिल हैं।"
        )

        await ws.convert(text)
        print("Sent text message")

        await ws.flush()
        print("Flushed buffer")

        chunk_count = 0
        with open("output.mp3", "wb") as f:
            async for message in ws:
                if isinstance(message, AudioOutput):
                    chunk_count += 1
                    audio_chunk = base64.b64decode(message.data.audio)
                    f.write(audio_chunk)
                    f.flush()

        print(f"All {chunk_count} chunks saved to output.mp3")
        print("Audio generation complete")

        
        if hasattr(ws, "_websocket") and not ws._websocket.closed:
            await ws._websocket.close()
            print("WebSocket connection closed.")


if __name__ == "__main__":
    asyncio.run(tts_stream())

# --- Notebook/Colab usage ---
# await tts_stream()

```

> Defines the language for text normalization before speech synthesis.