camb-sdk 1.5.13

The official Python SDK for the Camb.ai API

Camb.ai Python SDK

Camb AI Website

The official Python SDK for interacting with Camb AI's powerful voice and audio generation APIs. Create expressive speech, unique voices, and rich soundscapes with just a few lines of Python.

✨ Features

• Dubbing: Dub your videos into multiple languages with voice cloning!
• Expressive Text-to-Speech: Convert text into natural-sounding speech using a wide range of pre-existing voices.
• Generative Voices: Create entirely new, unique voices from text prompts and descriptions.
• Soundscapes from Text: Generate ambient audio and sound effects from textual descriptions.
• Live Transcription: Stream microphone or file audio over a WebSocket and receive cumulative interim transcripts, word-level timing, and typed events.
• Access to voice cloning, translation, and more (refer to full API documentation).

📦 Installation

Install the SDK using pip, ensure Python 3.9+:

pip install camb-sdk

Or through

pip install git+https://github.com/Camb-ai/cambai-python-sdk

🔑 Authentication & Accessing Clients

To use the Camb AI SDK, you'll need an API key. You can authenticate it by:

from camb.client import CambAI, AsyncCambAI

# Synchronous Client
client = CambAI(api_key="YOUR_CAMB_API_KEY")

# Asynchronous Client
async_client = AsyncCambAI(api_key="YOUR_CAMB_API_KEY")

Client with Specific MARS Pro Provider (e.g. Vertex, Baseten)

Baseten

To deploy the model go to models from baseten example: https://app.baseten.co/deploy/mars8-flash and deploy then perform setup like below

client_baseten = CambAI(
    tts_provider="baseten",
    provider_params={
        "api_key": "YOUR_BASETEN_API_KEY",
        "mars_url": "YOUR_BASETEN_URL"
    }
)

# Call TTS with Baseten
client_baseten.text_to_speech.tts(
    text="Hello World and my dear friends",
    language="en-us",
    speech_model="mars-flash",
    request_options={
        "additional_body_parameters": {
            "reference_audio": base64.b64encode(open("audio.wav", "rb").read()).decode('utf-8'),  # also support public/signed urls
            "reference_language": "en-us"  # required
        },
        "timeout_in_seconds": 300
    }
)

Vertex Support (In Progress)

client_with_provider = CambAI(
    tts_provider="vertex",
    provider_params={"project_id": "my-project", "location": "us-central1"}
)

🚀 Getting Started: Examples

NOTE: For more examples and full ready to run files refer to the examples/ directory.

1. Text-to-Speech (TTS)

Convert text into spoken audio using one of Camb AI's high-quality voices.

Supported Models & Sample Rates

Model Name	Sample Rate	Description
mars-pro	48kHz	High-fidelity, professional-grade speech synthesis. Ideal for long-form content and dubbing.
mars-8.1-pro-beta	48kHz	Beta MARS Pro model. Try this model with the same source references, as it may perform much better for pronunciation, expressiveness with high-pitch references, overall prosody, accent control, and coverage.
mars-8.1-flash-beta	48kHz	Beta MARS Pro model with faster speed. Try this model with the same source references, as it may perform much better for pronunciation, expressiveness with high-pitch references, overall prosody, accent control, and coverage.
mars-instruct	22.05kHz	optimized for instruction-following and nuance control.
mars-flash	22.05kHz	Low-latency model optimized for real-time applications and conversational AI.

a) Get an Audio URL or Save to File

from camb.client import CambAI, save_stream_to_file
from camb.types.stream_tts_output_configuration import StreamTtsOutputConfiguration

# Initialize client (ensure API key is set)
client = CambAI(api_key="YOUR_CAMB_API_KEY")

response = client.text_to_speech.tts(
    text="Hello from Camb AI! This is a test of our Text-to-Speech API.",
    voice_id=20303,  # Example voice ID, get from client.voice_cloning.list_voices()
    language="en-us",
    speech_model="mars-8.1-flash-beta",  # options: mars-pro, mars-8.1-pro-beta, mars-flash, mars-instruct, auto
    output_configuration=StreamTtsOutputConfiguration(
        format="mp3"
    )
)

save_stream_to_file(response, "tts_output.mp3")
print("Success! Audio saved to tts_output.mp3")

b) Async Text-to-Speech

You can also stream audio asynchronously using AsyncCambAI.

import asyncio
from camb.client import AsyncCambAI, save_async_stream_to_file
from camb.types.stream_tts_output_configuration import StreamTtsOutputConfiguration

async_client = AsyncCambAI(api_key="YOUR_CAMB_API_KEY")

async def main():
    response = async_client.text_to_speech.tts(
        text="Hello, this is a test of the text to audio streaming capabilities.",
        language="en-us",
        speech_model="mars-8.1-flash-beta",  # options: mars-pro, mars-8.1-pro-beta, mars-flash, mars-instruct, auto
        voice_id=147319,
        output_configuration=StreamTtsOutputConfiguration(
            format="mp3"
        )
    )
    await save_async_stream_to_file(response, "text_to_audio_output.mp3")
    print("Success! Audio saved to text_to_audio_output.mp3")

asyncio.run(main())

c) Using Mars Flash (Low Latency)

For applications requiring faster responses, make sure you're using mars-flash (22.05kHz).

response = client.text_to_speech.tts(
    text="Hey! I can respond much faster.",
    language="en-us",
    speech_model="mars-flash",
    voice_id=<id>,
    output_configuration=StreamTtsOutputConfiguration(
        format="wav"
    )
)

d) List Available Voices

You can list available voices to find a voice_id that suits your needs:

voices = client.voice_cloning.list_voices()
print(f"Found {len(voices)} voices:")
for voice in voices[:5]:  # Print first 5 as an example
    print(f"  - ID: {voice["id"]}, Name: {voice["voice_name"]}, Gender: {voice["gender"]}, Language: {voice["language"]}")

2. Text-to-Voice (Generative Voice)

Create completely new and unique voices from a textual description of the desired voice characteristics.

from camb.client import CambAI

# Initialize client
client = CambAI(api_key="YOUR_CAMB_API_KEY")

try:
    print("Generating a new voice and speech...")
    # Returns 3 sample URLs
    result = client.text_to_voice.create_text_to_voice(
        text="Crafting a truly unique and captivating voice that carries a subtle air of mystery, depth, and gentle warmth.",
        voice_description="A smooth, rich baritone voice layered with a soft echo, ideal for immersive storytelling and emotional depth.",
    )
    print(result)

except Exception as e:
    print(f"Exception when calling text_to_voice: {e}\n")

3. Text-to-Audio (Sound Generation)

Generate sound effects or ambient audio from a descriptive prompt.

from camb.client save_stream_to_file
import time

response = client.text_to_audio.create_text_to_audio(
    prompt="A gentle breeze rustling through autumn leaves in a quiet forest.",
    duration=10,
    audio_type="sound"
)
task_id = response.task_id
if task_id:
    while True:
        status = client.text_to_audio.get_text_to_audio_status(task_id=task_id)
        if status.status == "SUCCESS":
            result = client.text_to_audio.get_text_to_audio_result(status.run_id)
            save_stream_to_file(result, "sound_effect.mp3")
            print("Success! Sound effect saved to sound_effect.mp3")
            break
        time.sleep(2)

4. End-to-End Dubbing

Dub videos into different languages with voice cloning and translation capabilities.

from camb.types.language_enums import Languages

result = client.dub.create_dub(
    video_url="your_accessible_video_url",
    source_language=Languages.EN_US,  # English (Or Check client.languages.get_source_languages())
    target_languages=[Languages.HI_IN],  # list of Languages like [Languages.HI_IN, Languages.FR_FR] or if you want single language then can use target_language=Languages.HI_IN
)
task_id = response.task_id
print(f"Dub Task created with ID: {task_id}")
while True:
    status_response = client.dub.get_dubbing_status(task_id=task_id)
    print(f"Current Status: {status_response.status}")
    if status_response.status == "SUCCESS":
        dubbed_run_info = client.dub.get_dubbed_run_info(status_response.run_id)
        print(f"Dubbed Video URL: {dubbed_run_info.audio_url}")
        print(f"Dubbed Video URL: {dubbed_run_info.transcript}")
        print(f"Dubbed Video URL: {dubbed_run_info.video_url}")
        break
    time.sleep(5)

5. Live Transcription (Streaming WebSocket)

Stream audio over a single WebSocket and receive cumulative interim transcripts, word-level timing, and typed events. The session exposes a microphone helper, a file source for tests, and the same on(event) dispatcher in both SDKs.

import asyncio
import os

from camb.client import CambAI
from camb.live_transcription import Microphone, ServerMessageType


async def main():
    client = CambAI(api_key=os.environ["CAMB_API_KEY"])
    session = await client.live_transcription.connect(
        model="boli-v5",
        language="en-us",
        sample_rate=16000,
    )

    @session.on(ServerMessageType.RESULTS)
    def _(msg):
        # Cumulative transcript: replace the previous interim rather
        # than concatenating successive Results events.
        print(f"\r{msg.transcript}", end="", flush=True)

    @session.on(ServerMessageType.CLOSED)
    def _(info):
        print(f"\nClosed: code={info.code} reason={info.reason!r}")

    async with session:
        mic = Microphone(sample_rate=16000, chunk_size=1600)
        await session.stream_audio(mic)


asyncio.run(main())

Prefer streaming a file (no audio device dependency)? See examples/live_transcription_file.py. For the full event catalog (Ready, Results, Final, Error, Closed), configuration options, and extensibility notes, see the Live Transcription tutorial and SDK guide.

⚙️ Advanced Usage & Other Features

The Camb AI SDK offers a wide range of capabilities beyond these examples, including:

• Voice Cloning
• Translations
• Translated TTS
• Audio Dubbing
• Transcription (async file/URL jobs)
• Live Transcription (streaming WebSocket — see Example 5 above)
• And more!

Please refer to examples for direct runnable examples and Official Camb AI API Documentation for a comprehensive list of features and advanced usage patterns.

License

This project is licensed under the MIT License - see the LICENSE file for details.