News: We are now on the mainnet with uid 3! Please join the Bittensor Discord and see us at Channel γ·gamma·3! Also, please check our X (Twitter) for our vision of creating a collaborative environment where everyone can contribute, benefit, and engage with open-source models, ultimately empowering millions. 03/24
Update: We are now in Phase 2 of the subnet. Our goal is to provide a more diversified and exciting voice dataset for miners to train and develop state-of-the-art efficient TTS models. We have observed that miners are making significant improvements in current metrics, which is encouraging. However, we need to be cautious about potential overfitting to these metrics. Our development team is working diligently on an adversarial and highly complex research study to develop an automatic system to address this issue. Have fun! 06/23
Note: The following documentation assumes you are familiar with basic Bittensor concepts: Miners, Validators, and incentives. If you need a primer, please check out https://docs.bittensor.com/learn/bittensor-building-blocks.
Text-to-Speech (TTS) models serve as the backbone for a wide range of applications, from virtual assistants to audiobooks. MyShell pioneers this field by open-sourcing the best TTS models, OpenVoice and MeloTTS. We believe that open-source TTS models are essential for the future of AI and are committed to building a decentralized ecosystem to support their development.
Yet, the TTS landscape is still in its infancy. In this subnet, we aim to gather the wisdom of the whole open-source community to create the best open-source TTS models.
As building a TTS model is a complex task, we will divide the development into several phases.
- Phase 1: Initial release of the subnet, including miner and validator functionality. This phase aim to build a comprehensive pipeline for TTS model training and evaluation. We will begin with a fixed speaker.
- Phase 2: Increase the coverage of speaker and conversation pool. We will recurrently update the speaker and language to cover more scenarios.
- Phase 3: More generally, we can have fast-clone models that can be adapted to new speakers with a small amount of data, e.g., OpenVoice. We will move to fast-clone models in this phase.
We are currently in Phase 2. To start, we utilize the AniSpeech dataset as the source of our speaker data. We randomly select 1 speaker from the dataset and the goal is to build a TTS model that can perfectly mimic this speaker's voice.
Please refer to tts_rater folder for audio samples from the speaker and the text used for evaluation.
Please refer to preprocess folder for options to download and preprocess the dataset for training.
Our subnet operates as follows:
- Miners: Train TTS model. Once a model is ready, the miner submits it to a Hugging Face 🤗 model pool, with metadata committed to the Bittensor chain.
- Validators: Evaluate submitted models. Validators generate test cases and assess model performance, assigning weights based on results.
- Bittensor Blockchain: Aggregates weights using Yuma Consensus to determine final weights and emissions for each miner.
At the core of our subnet is the evaluation protocol. Unlike other modalities, TTS models are hard to evaluate with traditional metrics. We, therefore, propose a set of evaluation metrics that are more relevant to TTS models based on our experience:
Please install this package first. We recommend using a virtual environment or conda environment.
git clone https://github.com/myshell-ai/MyShell-TTS-Subnet
cd MyShell-TTS-Subnet
pip install -e .
# The following command is required by the MeloTTS model
python -m unidic downloadPlease also install ffmpeg for audio processing.
# Ubuntu
sudo apt-get install ffmpegSee Miner README for more details.
See Validator README for more details.
Our codebase is built upon Nous Research's Subnet.
This repository is licensed under the MIT License.
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