NVIDIA/tacotron2
Tacotron2
Dieses Projekt ist ein neuronales Text-to-Speech-Framework und ein PyTorch-Modell, das darauf ausgelegt ist, menschliche Sprache zu synthetisieren. Es konvertiert geschriebenen Text in synthetisches Audio durch die Vorhersage von Mel-Spektrogrammen, die als Zwischenrepräsentation für die Stimmgenerierung dienen.
Das System enthält ein Konditionierungsmodell für WaveNet, um eine natürlich klingende Audioausgabe sicherzustellen. Es bietet ein verteiltes Trainings-Framework, das Multi-GPU-Verarbeitung und automatische Mixed-Precision nutzt, um die Trainingsgeschwindigkeit zu optimieren und den Speicherverbrauch zu reduzieren.
Das Projekt deckt die gesamte Pipeline der neuronalen Sprachsynthese ab, vom Modelltraining unter Verwendung von Text- und Audiodatensätzen bis zur Generierung künstlicher Stimmen. Es verwendet einen konvolutionalen Encoder-Decoder und Sequence-to-Sequence-Attention, um sprachliche Merkmale auf akustische Frames abzubilden.
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.
