README.md

---
license: cc-by-4.0
datasets:
- speechcolab/gigaspeech
- parler-tts/mls_eng_10k
- reach-vb/jenny_tts_dataset
- MikhailT/hifi-tts
- ylacombe/expresso
- keithito/lj_speech
- collabora/ai4bharat-shrutilipi
language:
- en
- hi
base_model:
- openai-community/gpt2
pipeline_tag: text-to-speech
---

# Model Card for indri-0.1-125m-tts

Indri is a series of audio models that can do TTS, ASR, and audio continuation. This is the smallest model (125M) in our series and supports TTS tasks in 2 languages:

1. English
2. Hindi

We have open-sourced our training scripts, inference, and other details.

- **Repository:** [GitHub](https://github.com/cmeraki/indri)
- **Demo:** [Website](https://www.indrivoice.ai/)
- **Implementation details**: [Release Blog](#TODO)

## Model Details

### Model Description

`indri-0.1-125m-tts` is a novel, ultra-small, and lightweight TTS model based on the transformer architecture.
It models audio as tokens and can generate high-quality audio with consistent style cloning of the speaker.

### Key features

1. Based on GPT-2 architecture. The methodology can be extended to any transformer-based architecture.
2. Supports voice cloning with small prompts (<5s).
3. Code mixing text input in 2 languages - English and Hindi.
4. Ultra-fast. Can generate 5 seconds of audio per second on Amphere generation NVIDIA GPUs, and up to 10 seconds of audio per second on Ada generation NVIDIA GPUs.

### Details

1. Model Type: GPT-2 based language model
2. Size: 125M parameters
3. Language Support: English, Hindi
4. License: CC BY 4.0

## Technical details

Here's a brief of how the model works:

1. Converts input text into tokens
2. Runs autoregressive decoding on GPT-2 based transformer model and generates audio tokens
3. Decodes audio tokens (from [Kyutai/mimi](https://huggingface.co/kyutai/mimi)) to audio

Please read our blog [here](#TODO) for more technical details on how it was built.

## How to Get Started with the Model

Use the code below to get started with the model. Pipelines are the best way to get started with the model.

```python
import torch
import torchaudio
from transformers import pipeline

task = 'indri-tts'
model_id = '11mlabs/indri-0.1-125m-tts'

pipe = pipeline(
 task,
    model=model_id,
    device=torch.device('cuda:0'), # Update this based on your hardware,
    trust_remote_code=True
)

output = pipe(['Hi, my name is Indri and I like to talk.'])

torchaudio.save('output.wav', output[0]['audio'][0], sample_rate=24000)
```

## Credits

1. [Kyutai/mimi](https://huggingface.co/kyutai/mimi)
2. [nanoGPT](https://github.com/karpathy/nanoGPT)

## Citation

To cite our work

```
@misc{indri-0.1-125m-tts,
  author       = {11mlabs},
  title        = {indri-0.1-125m-tts},
  year         = 2024,
  publisher    = {Hugging Face},
  journal      = {GitHub Repository},
  howpublished = {\url{https://github.com/cmeraki/indri}},
}
```