Search Results for "data processing"

...Training Data is the art of supervising machines through data. This includes the activities of annotation, which produces structured data; ready to be consumed by a machine learning model. Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition.

NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. ...

....** It turns speech into text locally and uses AI to polish, translate, organize, and transform your words into usable content, letting you replace keyboard input with your voice in any application. No audio uploads. No cloud dependency. Your voice stays on your device. Unlike cloud-based voice input software, voconly keeps all audio data local—no uploads, no internet required, complete privacy. Powered by local AI, it combines real-time speech recognition with on-device LLM post-processing. Supports multiple ASR models (Qwen-ASR, SenseVoice, Whisper, Parakeet) and offers customizable refinement modes: auto-polishing, professional formatting, cross-language translation. ...

...Uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not offer the data processing flexibility needed in research. Tensorpack squeezes the most performance out of pure Python with various auto parallelization strategies. ...

Speechalyzer: a tool for the daily work of a 'speech worker' It is optimized to process large speech data sets with respect to transcription, labeling and annotation. It is implemented as a client server based framework in Java and interfaces software for speech recognition, synthesis, speech classification and quality evaluation. The application is mainly the processing of training data for speech recognition and classification models and performing benchmarking tests on speech-to-text, text-to-speech and speech classification software systems.

The Open Mind Speech project is part of the Open Mind Initiative and aims to develop free(GPL) speech recognition and signal processing (DSP) tools and applications, as well as collect speech data from "e-citizens" using the Internet.