README.md

---
license: apache-2.0
datasets:
- amphion/Emilia-Dataset
language:
- en
base_model:
- Marvis-AI/marvis-tts-250m-v0.1-base
library_name: transformers
tags:
- mlx
- mlx-audio
- transformers
---

# Introduction
[[code](https://github.com/Marvis-Labs/marvis-tts)]

Marvis is a cutting-edge conversational speech model designed to enable real-time streaming text-to-speech synthesis. Built with efficiency and accessibility in mind, Marvis addresses the growing need for high-quality, real-time voice synthesis that can run on consumer devices such as Apple Silicon, iPhones, iPads, Macs and others.

## Key Features

- **Real-time Streaming**: Stream audio chunks as text is processed, enabling natural conversational flow
- **Compact Size**: Only 500MB when quantized, enabling on-device inference
- **Edge deployment**: Optimized for real-time Speech-to-Speech (STS) on mobile devices (i.e., iPad, iPhone and etc)
- **Natural Audio Flow**: Process entire text context for coherent speech synthesis without chunking artifacts
- **Multimodal Architecture**: Seamlessly handles interleaved text and audio tokens

## Supported Languages

Currently optimized for English with support for expressive speech synthesis with additional languages such as German, Portuguese, French and Mandarin coming soon.

# Quick Start

## Using MLX

```bash
pip install -U mlx-audio
python -m mlx_audio.tts.generate --model Marvis-AI/marvis-tts-250m-v0.1  --stream \
 --text "Marvis TTS is a new text-to-speech model that provides fast streaming on edge devices."
```

## Using transformers

**Without Voice Cloning**
```python
import torch
from transformers import AutoTokenizer, AutoProcessor, CsmForConditionalGeneration
from tokenizers.processors import TemplateProcessing
import soundfile as sf

model_id = "Marvis-AI/marvis-tts-250m-v0.1-transformers"
device = "cuda"if torch.cuda.is_available() else "cpu"

# load the model and the processor
processor = AutoProcessor.from_pretrained(model_id)
model = CsmForConditionalGeneration.from_pretrained(model_id, device_map=device)

# prepare the inputs
text = "[0]Marvis TTS is a new text-to-speech model that provides fast streaming on edge devices." # `[0]` for speaker id 0
inputs = processor(text, add_special_tokens=True, return_tensors="pt").to(device).pop("token_type_ids")
# infer the model
audio = model.generate(**inputs, output_audio=True)
sf.write("example_without_context.wav", audio[0].cpu(), samplerate=24_000, subtype="PCM_16")

```


# Model Description

Marvis is built on the [Sesame CSM-1B](https://huggingface.co/sesame/csm-1b) (Conversational Speech Model) architecture, a multimodal transformer that operates directly on Residual Vector Quantization (RVQ) tokens and uses [Kyutai's mimi codec](https://huggingface.co/kyutai/mimi). The architecture enables end-to-end training while maintaining low-latency generation and employs a dual-transformer approach:

- **Multimodal Backbone (250M parameters)**: Processes interleaved text and audio sequences to model the zeroth codebook level, providing semantic understanding and context.

- **Audio Decoder (60M parameters)**: A smaller, specialized transformer that models the remaining 31 codebook levels to reconstruct high-quality speech from the backbone's representations.


Unlike models that require text chunking based on regex patterns, Marvis processes entire text sequences contextually, resulting in more natural speech flow and intonation.

# Training Details

**Pretraining**: 
- Dataset: Emilia-YODAS 
- Training Steps: 2M steps
- Hardware: 1x NVIDIA GH200 96GB
- Precision: bfloat16
- Learning Rate: 3e-4
- Batch Size: 64

**Post-training**: 
- Dataset: Expressive Speech
- Training Steps: 200K steps
- Expressiveness Setting: 0.5
- Hardware: 1x NVIDIA GH200 96GB
- Precision: bfloat16
- Learning Rate: 1e-4
- Batch Size: 64

**Total Training Cost**: ~$2,000 
- Pretraining and fine-tuning: $246.69 (1x GH200)
- Post-training data generation: $167.94 (RTX6000 Ada)
- Additional experimentation: ~$1,500 across various GPU configurations
- Platforms: Prime-Intellect and Jarvis-Labs

## Use Cases

- **Real-time Voice Assistants**: Deploy natural-sounding voice interfaces with custom voices
- **Content Creation**: Generate voiceovers and narration with personalized voices
- **Accessibility Tools**: Create personalized speech synthesis for communication aids
- **Interactive Applications**: Build conversational AI with consistent voice identity
- **Podcast & Media**: Generate natural-sounding speech for automated content

### Local & Cloud Deployment

**Local Deployment:**
- Minimum Requirements: 1GB RAM, GPU recommended for real-time inference
- Quantized Model: 500MB download
- Platforms: iOS, Android, Windows, macOS, Linux

**Cloud Deployment:**
- API-ready architecture
- Scalable inference pipeline
- Low-latency streaming support

### Technical Limitations

- Language Support: Currently optimized primarily for English. Performance on other languages may be suboptimal
- Audio Quality Dependency: Voice cloning quality is dependent on the clarity and quality of the 10-second reference audio
- Background Noise: Performance degrades with noisy reference audio or inference environments
- Hallucinations: The model might hallucinate words specially for new words or short sentences.

### Legal and Ethical Considerations:

- Users are responsible for complying with local laws regarding voice synthesis and impersonation
- Consider intellectual property rights when cloning voices of public figures
- Respect privacy laws and regulations in your jurisdiction
- Obtain appropriate consent and permissions before deployment

## License & Agreement

* Apache 2.0

## Citation

If you use Marvis in your research or applications, please cite:

```bibtex
@misc{marvis-tts-2025,
  title={Marvis-TTS: Efficient Real-time Voice Cloning with Streaming Speech Synthesis},
  author={Prince Canuma and Lucas Newman},
  year={2025}
}
```

## Acknowledgments

Special thanks to Sesame and Kyutai for their groundbreaking open-source contributions that inspired our work, and to the broader open-source community for their unwavering support and collaboration.

---

**Version**: 0.1 

**Release Date**: 26/08/2025  

**Creators**: Prince Canuma & Lucas Newman