NVIDIA/tacotron2
Tacotron2
这是一个神经文本转语音（TTS）框架和 PyTorch 模型，旨在合成人类语音。它通过预测梅尔频谱图（mel spectrograms）将书面文本转换为合成音频，梅尔频谱图作为语音生成的中间表示。
该系统包括一个用于 WaveNet 的条件模型，以确保自然的音频输出。它提供了一个分布式训练框架，利用多 GPU 处理和自动混合精度来优化训练速度并减少内存使用。
该项目涵盖了神经语音合成的完整流水线，从使用文本和音频数据集的模型训练到人工语音的生成。它采用卷积编码器-解码器和序列到序列注意力机制，将语言特征映射到声学帧。
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.
