NVIDIA/tacotron2
Tacotron2
This project is a neural text-to-speech framework and PyTorch model designed to synthesize human speech. It converts written text into synthetic audio by predicting mel spectrograms, which serve as an intermediate representation for voice generation.
The system includes a conditioning model for WaveNet to ensure natural-sounding audio output. It provides a distributed training framework that utilizes multi-GPU processing and automatic mixed precision to optimize training speed and reduce memory usage.
The project covers the full pipeline of neural speech synthesis, from model training using text and audio datasets to the generation of artificial voices. It employs a convolutional encoder-decoder and sequence-to-sequence attention to map linguistic features to acoustic frames.
Features
- Speech Synthesis - Provides a complete system for generating natural-sounding human speech from text input.
- Input Sequence Attentions - Implements a learned alignment mechanism to predict sequences of mel spectrogram frames from text characters.
- Mel-Spectrogram Processing - Uses mel spectrograms as a frequency-domain intermediate representation between the encoder and the vocoder.
- TTS Engines - Provides a comprehensive neural engine for training speech models and generating synthetic audio.
- Convolutional Encoder-Decoders - Uses a convolutional encoder-decoder architecture to map linguistic features to acoustic frames.
- Text-to-Speech Model Training - Provides a PyTorch-based framework for training generative speech models using audio-text datasets.
- Synthetic Speech Generation - Generates natural-sounding synthetic speech by replicating vocal characteristics from text input.
- Text-to-Speech - Synthesizes high-fidelity human speech from written text using deep learning and mel spectrograms.
- Text-To-Speech Models - Implements a PyTorch neural network that predicts mel spectrograms to synthesize human speech.
- Auto-Regressive Decoding - Implements an auto-regressive decoder that feeds previously predicted frames back into the network for speech synthesis.
- Distributed GPU Training - Includes a distributed processing system to scale the training of speech models across multiple GPUs.
- Vocoder Conditioning - Conditions WaveNet audio generation on predicted mel spectrograms to ensure natural-sounding speech.
- Mixed Precision Training - Employs mixed precision training using 16-bit and 32-bit floating point types to optimize VRAM usage and training speed.
- Distributed Mixed-Precision Training - Optimizes the training of large neural networks using distributed multi-GPU processing and mixed precision.
- Multi-GPU Distribution - Splits heavy training workloads across multiple graphics cards to handle large datasets and memory requirements.
- Audio and Speech - Neural text-to-speech synthesis conditioned on mel spectrograms.
- Model Implementations - Tacotron 2 speech synthesis implementation.
- Multimodal Translation - Natural speech synthesis conditioned on mel spectrograms.
- Speech Synthesis - Neural network architecture for end-to-end speech synthesis.
- Text to speech - Listed in the “Text to speech” section of the Ailia Models awesome list.
