Projects sharing features with Step Audio2

MegaTTS3 is a bilingual speech synthesis system that generates natural-sounding speech in Chinese and English, including seamless code-switching within a single utterance. It functions as a text-to-speech engine, voice cloning system, and speech-to-text alignment tool, built around an acoustic latent compression model that encodes high-resolution audio into compact representations for efficient processing. The system distinguishes itself through accent intensity control, allowing adjustment of a speaker's accent strength in generated speech, and voice cloning from short audio samples for pers

Buzz is a desktop application that provides a local speech-to-text engine for transcribing and translating audio and video files. By leveraging local machine inference, the software ensures data privacy and offline performance, removing the need for cloud connectivity during media processing. The application distinguishes itself through a modular plugin architecture that allows for the integration of custom functionality, such as content summarization and automated text formatting, without modifying the core codebase. It also features a speaker diarization pipeline that identifies and labels

Whisper is a high-performance speech-to-text inference engine that uses graphics hardware shaders to accelerate the transcription of spoken audio into written text. It implements a GPU-accelerated automatic speech recognition framework specifically designed to run Whisper models. The system focuses on high-speed processing for both recorded audio files and live microphone streams. It utilizes voice activity detection to analyze raw audio in real time, triggering the inference engine only when human speech is detected. The engine covers a broad range of capabilities including real-time audio

:computer: :robot: A summary on our attempts at using Deep Learning approaches for Emotional Text to Speech :speaker:

CoVoST: A Large-Scale Multilingual Speech-To-Text Translation Corpus (CC0 Licensed)

Convert your voice to favorite voice

This project is a generative speech synthesis engine that converts text into high-fidelity human speech. It utilizes a two-stage autoregressive transformer architecture that separates semantic token prediction from acoustic detail reconstruction to balance linguistic accuracy with audio quality. The system is designed to support multilingual output and conversational AI development, enabling the generation of context-aware speech that maintains flow across multiple dialogue turns. The platform distinguishes itself through a production-ready inference server that employs continuous batching to

CosyVoice is a speech synthesis framework that utilizes large language models to generate expressive, multilingual audio. The system functions as an audio generation engine capable of producing natural-sounding speech across multiple languages while preserving regional dialects and specific emotional tones. The platform distinguishes itself through its zero-shot voice cloning capabilities, which allow for the creation of synthetic voice profiles from short audio samples without requiring additional model training. It provides fine-grained control over vocal attributes, enabling users to adjus

Whisper.cpp is a high-performance, local-first speech recognition engine designed to run large-scale machine learning models on consumer hardware. It functions as a portable library that converts audio into text, supporting both static file transcription and real-time stream processing. By utilizing a lightweight inference engine and weight quantization, the project minimizes memory and compute overhead, allowing for efficient execution without reliance on external cloud APIs or internet connectivity. The project distinguishes itself through a hardware-agnostic compute abstraction that offloa

Perceptual Quality Estimator for speech and audio

Kokoro is a lightweight neural text-to-speech engine that converts written text into spoken audio using a compact model designed for fast inference. It supports multiple languages through language-specific grapheme-to-phoneme conversion pipelines, and offers voice profile selection to change the character of the generated speech. The engine provides GPU acceleration on Apple Silicon hardware by setting a single environment variable, enabling faster inference on Mac M-series machines. It also includes pattern-based text segmentation, allowing input text to be split at user-defined delimiters t

Index-tts is a neural audio generation engine designed to convert written text into high-fidelity human speech. By utilizing deep learning models and phoneme-based sequence modeling, the system transforms text into natural-sounding audio waveforms suitable for a variety of accessibility and media applications. The platform functions as a server-side inference pipeline that provides a programmatic interface for integrating voice generation into external applications. It distinguishes itself through asynchronous audio streaming, which buffers and delivers generated speech chunks in real time to

A Python library for audio data augmentation. Useful for making audio ML models work well in the real world, not just in the lab.

OmniVoice is a Python-based open-source project hosted under the k2-fsa organization. The repository currently does not contain any documented features or structured documentation, making it difficult to determine its specific purpose or capabilities at this time.

KittenTTS is a neural text-to-speech engine and text-to-audio synthesis tool that converts written text into spoken audio using lightweight neural network models. It functions as both a speech synthesizer and an audio file generator, producing spoken audio for offline playback. The system includes a text normalization processor that expands numbers and abbreviations into full spoken words to improve the naturalness of the synthesized speech. It supports diverse voice options and provides the ability to adjust playback speed.

中文语音识别; Mandarin Automatic Speech Recognition;

WhisperX is an automated speech recognition toolkit designed to convert spoken audio into text while maintaining precise synchronization with the original media. It functions as an integrated pipeline that combines transcription, phoneme-based alignment, and speaker diarization to produce structured, attributed transcripts. The project distinguishes itself through its use of forced alignment, which matches existing text to audio signals at the phoneme level to generate accurate word-level timestamps. It also incorporates speaker diarization to identify and label unique voices within a recordi