3 个仓库
Awesome GitHub RepositoriesAudio Machine Learning Frameworks
Comprehensive libraries for training and evaluating generative audio and sound synthesis models.
Distinct from Machine Learning Training: Shortlist contains malware or scientific ML; this is specifically for generative audio/music synthesis.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Audio Machine Learning Frameworks. Refine with filters or upvote what's useful.
Awesome Audio Machine Learning Frameworks GitHub Repositories
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- facebookresearch/audiocraft
facebookresearch/audiocraft
23,379在 GitHub 上查看Audiocraft is a deep learning audio library and machine learning framework designed for training, fine-tuning, and evaluating generative models for music and sound effects. It functions as a text-to-music generative model and a neural audio codec, providing the tools necessary to compress audio signals into discrete representations and synthesize high-fidelity waveforms from textual descriptions. The framework is distinguished by its ability to combine multiple conditioning signals, allowing for the generation of audio based on text prompts, melodic excerpts, or style-based audio clips. It al
Provides a complete framework for training, fine-tuning, and evaluating generative models for music and sound effects.
Jupyter Notebook - tyiannak/pyaudioanalysis
tyiannak/pyAudioAnalysis
6,242在 GitHub 上查看pyAudioAnalysis 是一个用于音频信号处理和分析的 Python 库和框架。它提供了提取声音数学表示（如频谱图）的工具，并实现了一个用于训练和评估机器学习模型的系统，以根据声学模式对音频片段进行分类。 该项目包括专门的音频分割工具，允许删除静音并检测特定的音频事件，从而将录音划分为有意义的部分。它还提供了数据可视化功能，使用降维技术来映射内容相似性并识别声音数据中的聚类。 该库涵盖了广泛的信号处理功能，包括频谱域特征提取、时间分析和用于估计连续值的音频回归。这些功能既可以作为可编程库使用，也可以通过命令行界面进行音频文件的批处理。
Ships a framework for training and evaluating machine learning models to categorize sound recordings based on acoustic patterns.
Python - stability-ai/stable-audio-tools
Stability-AI/stable-audio-tools
3,790在 GitHub 上查看Stable-audio-tools is a toolkit for training and deploying latent diffusion models for high-fidelity audio synthesis. It provides a framework for generating audio by iteratively refining noise within a compressed latent space, using specialized encoders to preserve temporal and spectral features of the audio signal. The project features a system for adapting pre-trained audio checkpoints to new datasets through modular initialization and configuration files. It includes utilities for weight extraction and inference model export, which remove training metadata and optimizer states to create li
Provides a comprehensive framework for training and evaluating generative audio and sound synthesis models.
Python
