naturalspeech2-pytorch

NaturalSpeech 2 - Pytorch: A Detailed Introduction

NaturalSpeech 2 is a text-to-speech (TTS) system implemented in PyTorch, which promises to revolutionize how speech and singing synthesis are approached. This project is based on the research paper "NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers," which introduces a novel way of synthesizing speech, accommodating zero-shot capabilities.

Key Features and Components

Neural Audio Codec: NaturalSpeech 2 uses continuous latent vectors and a neural audio codec. This component is crucial in translating raw audio into a format that the model can effectively process and learn from.
Latent Diffusion Model: The project employs a latent diffusion model to generate speech non-autoregressively. Unlike traditional methods, this approach allows for the synthesis of natural-sounding speech in a single step.
Denoising Diffusion: The implementation opts for denoising diffusion rather than score-based Stochastic Differential Equations (SDE). This choice simplifies the model and enhances performance.

Community and Contributions

The project has received generous sponsorships from prominent AI entities such as Stability and Huggingface. Notably, these organizations are known for supporting open-source AI innovations and providing key resources like the 'accelerate' library. Special thanks go to contributors like Manmay Nakhashi, who played a vital role in developing critical encoders and conditioning the diffusion network.

Getting Started

To begin using NaturalSpeech 2, simply install it via pip:

$ pip install naturalspeech2-pytorch

Basic Usage

Here's a brief example of how to use NaturalSpeech 2 in a Python environment:

import torch
from naturalspeech2_pytorch import EncodecWrapper, Model, NaturalSpeech2

codec = EncodecWrapper()

model = Model(dim=128, depth=6)

diffusion = NaturalSpeech2(model=model, codec=codec, timesteps=1000).cuda()

# Let's assume we have some raw audio data
raw_audio = torch.randn(4, 327680).cuda()

loss = diffusion(raw_audio)
loss.backward()

# After training, generate audio samples
generated_audio = diffusion.sample(length=1024)

Advanced Features: Conditioning

NaturalSpeech 2 also supports advanced conditioning techniques, allowing for more controlled synthesis:

import torch
from naturalspeech2_pytorch import EncodecWrapper, Model, NaturalSpeech2, SpeechPromptEncoder

codec = EncodecWrapper()

model = Model(dim=128, depth=6, dim_prompt=512, cond_drop_prob=0.25, condition_on_prompt=True)

diffusion = NaturalSpeech2(model=model, codec=codec, timesteps=1000)

# Mock data for conditioning
raw_audio = torch.randn(4, 327680)
prompt = torch.randn(4, 32768)
text = torch.randint(0, 100, (4, 100))
text_lens = torch.tensor([100, 50, 80, 100])

loss = diffusion(audio=raw_audio, text=text, text_lens=text_lens, prompt=prompt)
loss.backward()

# Generate conditioned audio
generated_audio = diffusion.sample(length=1024, text=text, prompt=prompt)

Training with a `Trainer` Class

If you prefer a structured approach for training and sampling, the Trainer class is available:

from naturalspeech2_pytorch import Trainer

trainer = Trainer(
    diffusion_model=diffusion,
    folder='/path/to/speech',
    train_batch_size=16,
    gradient_accumulate_every=2,
)

trainer.train()

Future Tasks and Enhancements

NaturalSpeech 2 is continuously evolving with several goals set for future development:

Integrating classifier-free guidance.
Enhancing duration and pitch prediction algorithms.
Consulting TTS experts for improved methods and practices.
Exploring direct summation conditioning with additional text-to-semantic modules.

Citations

The project cites several significant papers that contribute to its foundation, including advancements in diffusion models and novel architectural improvements for transformers.

NaturalSpeech 2 in PyTorch offers a promising new toolkit for TTS researchers and developers, driving forward the capabilities of natural, flexible, and responsive audio synthesis.