> For clean Markdown of any page, append `.md` to the page URL.
> For a complete documentation index, see https://docs.sarvam.ai/llms.txt.
> For full documentation content in one file, see https://docs.sarvam.ai/llms-full.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.sarvam.ai/_mcp/server.

# How to set `output_audio_bitrate`

> Control the quality and size of the synthesized audio output.

The `output_audio_bitrate` parameter defines the **bitrate** of the audio stream in kilobits per second. It controls the **audio quality**, **file size**, and **streaming performance**.

> **Note**
>
> This parameter is available only in the **WebSocket Streaming API**. It is not supported in the REST API.

This parameter is optional. If not provided, the system uses the **default value `128k`**.

### Supported Bitrates

| Value  | Description                                   |
| ------ | --------------------------------------------- |
| `32k`  | Very low bitrate, smaller file, lower quality |
| `64k`  | Low bitrate, suitable for speech              |
| `96k`  | Medium quality                                |
| `128k` | Default – good balance of quality and size    |
| `192k` | High quality, larger audio files              |

---

### **Example Streaming API code**

```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", 
            speaker="shubh",
            output_audio_bitrate= "128k"
        )
        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()

```

> Control the quality and size of the synthesized audio output.