Awesome GitHub RepositoriesMel-Spectrogram Processing

This project is a collection of deep learning tools for image classification and audio tagging, providing a repository of pre-trained model weights and architectures. It serves as a Keras model zoo that enables the immediate use of established neural networks for inference and transfer learning. The library includes a music tagging framework that classifies audio recordings using convolutional recurrent neural networks and mel-spectrograms. For visual data, it provides implementations of architectures such as ResNet, VGG, and Xception, alongside a repository of weights trained on large datase

Parler-TTS is a library for generating high-quality speech from text, supporting both inference and model training. It combines a transformer-based text-to-speech generator with a mel-spectrogram decoder to convert written text into natural-sounding audio. The project distinguishes itself through text-conditioned voice control, which allows speaker attributes like gender, pitch, speaking rate, and style to be adjusted via a natural-language description. It also includes speaker embedding selection for maintaining voice identity across multiple generations, and a fine-tuning recipe system that

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。

DiffSinger is an AI vocal synthesizer and neural audio generator designed to produce high-fidelity singing and speech. It functions as a text-to-speech system and a diffusion-based singing voice synthesis tool that transforms text and pitch into audible audio. The system utilizes a shallow diffusion mechanism and iterative noise refinement to generate realistic vocal performances. It incorporates specialized sampling plugins and numerical solvers to accelerate inference and reduce the time required to generate synthetic voices. The project covers acoustic modeling, mel-spectrogram synthesis,

TensorFlowTTS 是一个神经语音合成框架，用于将文本转换为高保真音频波形。它提供了一个用于训练和微调序列到序列或生成对抗网络架构的工具包，以产生自然听感的语音。 该系统包括将中间声学表示转换为最终音频波形的神经声码器实现。它还具有播放速度控制功能，以调整合成语音输出的速率。 该框架涵盖了语音合成的端到端流水线，包括用于创建归一化梅尔频谱图的音频数据预处理，以及用于管理 GPU 加速模型训练的训练流水线。它利用自定义训练器框架在训练过程中处理损失函数和优化逻辑。

Aubio is an audio analysis and digital signal processing library designed for music information retrieval. It provides a suite of tools for extracting musical features, estimating fundamental frequencies, and tracking rhythmic pulses in audio streams. The library specializes in the detection of pitch and beat, enabling the extraction of musical notes and the estimation of overall tempo. It also includes capabilities for automatic onset detection to identify the start of sonic events and the separation of audio signals into percussive transients and steady-state tonal components. The system c

Vocal Remover is a deep learning application designed for audio source separation. It functions as a command-line utility that decomposes complex audio signals into individual components, specifically isolating vocals and instrumental tracks from mixed recordings. The software utilizes a symmetric encoder-decoder neural network architecture to process audio spectrograms. By applying learned magnitude masks to the original signal phase, the system reconstructs output audio while maintaining temporal coherence. It supports both the execution of pre-trained models for track extraction and the tr

This application is a platform for AI voice synthesis and neural voice cloning. It provides a comprehensive toolkit for converting text into natural-sounding human speech by applying custom-trained neural network models to specific audio samples. The system facilitates the entire lifecycle of voice model development, including the preparation of raw audiobooks and video transcriptions into structured training datasets. It supports the training of these models on local or remote hardware, utilizing multi-GPU distributed processing to handle large-scale data and accelerate model convergence. B

该项目是一个全面的工具包，用于设备端语音识别、合成和音频处理，专为 Apple Silicon 工程设计。它提供了一个框架，用于构建完全离线运行的实时、全双工语音代理，利用原生硬件加速来保持性能和隐私。通过利用优化的机器学习模型，该库实现了复杂音频任务的本地执行，而无需依赖外部云服务。 该库通过其对本地、高性能语音交互的专门关注脱颖而出。它包括用于流式音频流水线的复杂编排，允许以低延迟进行实时转录、语音合成和语音克隆。该系统旨在处理持续的、交互式的对话，具有内置机制来防止音频反馈循环并管理持久的流会话。 除了核心交互外，该项目还提供了一套广泛的音频增强和管理功能。它支持高级信号处理，包括源分离、降噪和音频上采样，以及用于说话人日志记录和嵌入提取的工具。该框架还提供广泛的模型管理工具，例如量化控制、内存管理和对自定义模型权重加载的支持，确保开发者能够在本地硬件上平衡处理速度和资源消耗。 该项目包含一个用于执行音频任务和将模型权重转换为优化格式的命令行接口。它还暴露了 HTTP 和 WebSocket 端点，以促进与标准行业接口的集成。