MiraTTS is a finetune of the excellent Spark-TTS model for enhanced realism and stability performing on par with closed source models. This repository also heavily optimizes Mira with Lmdeploy and boosts quality by using FlashSR to generate high quality audio at over 100x realtime!

• Incredibly fast: Over 100x realtime by using Lmdeploy and batching.
• High quality: Generates clear and crisp 48khz audio outputs which is much higher quality then most models.
• Memory efficient: Works within 6gb vram.
• Low latency: Latency can be low as 100ms.

Simple 1 line installation:

uv pip install git+https://github.com/ysharma3501/MiraTTS.git

Running the model(bs=1):

from mira.model import MiraTTS
from IPython.display import Audio
mira_tts = MiraTTS('YatharthS/MiraTTS') ## downloads model from huggingface

file = "reference_file.wav" ## can be mp3/wav/ogg or anything that librosa supports
text = "Alright, so have you ever heard of a little thing named text to speech? Well, it allows you to convert text into speech! I know, that's super cool, isn't it?"

context_tokens = mira_tts.encode_audio(file)
audio = mira_tts.generate(text, context_tokens)

Audio(audio, rate=48000)

Running the model using batching:

file = "reference_file.wav" ## can be mp3/wav/ogg or anything that librosa supports
text = ["Hey, what's up! I am feeling SO happy!", "Honestly, this is really interesting, isn't it?"]

context_tokens = [mira_tts.encode_audio(file)]

audio = mira_tts.batch_generate(text, context_tokens)

Audio(audio, rate=48000)

Examples can be seen in the huggingface model

I recommend reading these 2 blogs to better easily understand LLM tts models and how I optimize them

• How they work: https://huggingface.co/blog/YatharthS/llm-tts-models
• How to optimize them: https://huggingface.co/blog/YatharthS/making-neutts-200x-realtime

Released training code! You can now train the model to be multilingual, multi-speaker, or support audio events on any local or cloud gpu!

Kaggle notebook: https://www.kaggle.com/code/yatharthsharma888/miratts-training

Colab notebook: https://colab.research.google.com/drive/1IprDyaMKaZrIvykMfNrxWFeuvj-DQPII?usp=sharing

• Release code and model
• Release training code
• Support low latency streaming
• Release native 48khz bicodec

Thanks very much to the authors of Spark-TTS and unsloth. Thanks for checking out this repository as well.

Stars would be well appreciated, thank you.

Email: [email protected]