Search Results for "audio recognition"
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Showing 144 open source projects for "audio recognition"
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Kimi-Audio
Audio foundation model excelling in audio understanding
Kimi-Audio is an ambitious open-source audio foundation model designed to unify a wide array of audio processing tasks — from speech recognition and audio understanding to generative conversation and sound event classification — within a single cohesive architecture. Instead of fragmenting work across specialized models, Kimi-Audio handles automatic speech recognition (ASR), audio question answering, automatic audio captioning, speech emotion recognition, and audio-to-text chat in one system, enabling developers to build rich, multimodal audio applications without stitching together disparate components. ... -
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Qwen2-Audio
Repo of Qwen2-Audio chat & pretrained large audio language model
Qwen2-Audio is a large audio-language model by Alibaba Cloud, part of the Qwen series. It is trained to accept various audio signal inputs (including speech, sounds, etc.) and perform both voice chat and audio analysis, producing textual responses. It supports two major modes: Voice Chat (interactive voice only input) and Audio Analysis (audio + text instructions), with both base and instruction-tuned models. -
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Fun Audio Chat
Large Audio Language Model built for natural interactions
Fun Audio Chat is an interactive voice-first conversational AI platform designed to let users engage in natural spoken dialogue with large language models in real time, turning speech into context-aware responses while maintaining a smooth back-and-forth experience. It combines speech recognition, audio processing, and AI generation so users can speak simply and receive spoken replies, enabling applications such as virtual assistants, voice bots, and hands-free chat interfaces. ... -
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SpeechRecognition
Speech recognition module for Python
Library for performing speech recognition, with support for several engines and APIs, online and offline. Recognize speech input from the microphone, transcribe an audio file, save audio data to an audio file. Show extended recognition results, calibrate the recognizer energy threshold for ambient noise levels (see recognizer_instance.energy_threshold for details). Listening to a microphone in the background, various other useful recognizer features. ... -
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Whisper
Robust Speech Recognition via Large-Scale Weak Supervision
OpenAI Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. ... -
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Step-Audio 2
Multi-modal large language model designed for audio understanding
Step-Audio2 is an advanced, end-to-end multimodal large language model designed for high-fidelity audio understanding and natural speech conversation: unlike many pipelines that separate speech recognition, processing, and synthesis, Step-Audio2 processes raw audio, reasons about semantic and paralinguistic content (like emotion, speaker characteristics, non-verbal cues), and can generate contextually appropriate responses — including potentially generating or transforming audio output. ... -
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sherpa-onnx
Speech-to-text, text-to-speech, and speaker recognition
Speech-to-text, text-to-speech, and speaker recognition using next-gen Kaldi with onnxruntime without an Internet connection. Support embedded systems, Android, iOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, C/C++, Python, Kotlin, C#, Go, NodeJS, Java, Swift, Dart, JavaScript, Flutter. -
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Moonshine Voice
Fast and accurate automatic speech recognition (ASR) for edge devices
moonshine is an open-source automatic speech recognition toolkit optimized for fast and accurate transcription on edge devices and local environments. The project is designed to enable real-time voice applications such as live transcription, voice commands, and embedded speech interfaces without requiring heavy cloud infrastructure. Its architecture emphasizes low latency and flexible input handling, allowing audio streams of varying durations rather than relying on fixed processing windows. ... -
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Buster
Captcha solver extension for humans
Save time by asking Buster to solve captchas for you. Buster is a Firefox extension which helps you to solve difficult captchas by completing reCAPTCHA audio challenges using speech recognition. Challenges are solved by clicking on the extension button at the bottom of the reCAPTCHA widget. It is not guaranteed that challenges are always solved, the limitations of the technology need to be considered. The continued development of Buster is made possible thanks to the support of awesome backers. ... -
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WhisperJAV
Uses Qwen3-ASR, local LLM, Whisper, TEN-VAD
WhisperJAV is an open-source speech transcription pipeline designed specifically for generating subtitles for Japanese adult video content. The project addresses challenges that standard speech recognition models face when transcribing this type of audio, which often includes low signal-to-noise ratios and large numbers of non-verbal vocalizations. Traditional automatic speech recognition systems can misinterpret these sounds as words, leading to inaccurate transcripts. WhisperJAV introduces a specialized pipeline that separates text generation from timestamp alignment, allowing the system to generate transcripts and then align them with audio using forced alignment techniques. ... -
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Recorder
HTML5 js recording mp3 wav ogg webm amr format
Supports microphone recording and real-time processing in most of the implemented getUserMediamobile and PC browsers, mainly including Chrome, Firefox, Safari, iOS 14.3+, Android WebView, Tencent Android X5 kernel (QQ, WeChat, Mini Program WebView) , uni-app (App, H5), and most Android phones updated after 2021 have their own browsers; do not support: UC-based kernel (typical Alipay), most of the old domestic mobile phones that have not been updated have their own browsers and any other form of browser (including PWA, WebClip, any App) on low-version iOS (11.0-14.2) except Safari inside page). Provides multiple plug-in function support. Rich audio visualization, variable speed and pitch processing, speech recognition, audio stream playback, etc.; with powerful real-time processing support, it can be used in various web applications: from simple recording to complex real-time voice Recognition (ASR), and even audio-related games, are handled with ease. -
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annyang!
Speech recognition for your site
annyang is a tiny javascript library that lets your visitors control your site with voice commands. annyang supports multiple languages, has no dependencies, weighs just 2kb and is free to use. annyang understands commands with named variables, splats, and optional words. Use named variables for one word arguments in your command. Use splats to capture multi-word text at the end of your command (greedy). Use optional words or phrases to define a part of the command as optional. annyang plays... -
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Handy STT
A free, open source, and extensible speech-to-text application
Handy is a free, open-source, offline speech-to-text application built for privacy, accessibility, and extensibility. Developed using Tauri (Rust + React/TypeScript), it runs natively across Windows, macOS, and Linux while performing local speech recognition without sending any audio to cloud servers. Handy allows users to start transcription instantly using a configurable keyboard shortcut—press to record, release to transcribe—and automatically pastes the resulting text into any active text field. Its backend leverages OpenAI’s Whisper models for GPU-accelerated speech recognition and Parakeet V3 for efficient CPU-only transcription with automatic language detection. ... -
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stt
Voice Recognition to Text Tool
stt is a standalone speech recognition tool that locally converts spoken content in audio or video files into textual formats without requiring internet access, giving users control over their data and reducing reliance on external APIs. It leverages open-source speech models such as Faster-Whisper to recognize and transcribe human speech into plain text, structured JSON objects, or subtitle files with time codes, making it suitable for both personal and professional transcription tasks. ... -
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Qwen2.5-Omni
Capable of understanding text, audio, vision, video
...Very strong benchmark performance across modalities (audio understanding, speech recognition, image/video reasoning) and often outperforming or matching single-modality models at a similar scale. Real-time streaming responses, including natural speech synthesis (text-to-speech) and chunked inputs for low latency interaction. -
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Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
...Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. Images, for tasks like image classification, object detection, and segmentation. Audio, for tasks like speech recognition and audio classification. Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. -
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pyAudioAnalysis
Python Audio Analysis Library: Feature Extraction, Classification
...It also includes utilities for visualizing audio features and analyzing patterns within sound recordings, which can be useful in applications such as speech recognition, music classification, and acoustic event detection. Because the library integrates machine learning algorithms with signal processing tools, it enables researchers to develop complete audio analysis pipelines using a single framework. -
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Omnilingual ASR
Omnilingual ASR Open-Source Multilingual SpeechRecognition
Omnilingual-ASR is a research codebase exploring automatic speech recognition that generalizes across a very large number of languages using shared modeling and training recipes. It focuses on leveraging self-supervised audio pretraining and scalable fine-tuning so low-resource languages can benefit from high-resource data. The project provides data preparation pipelines, training scripts, decoding utilities, and evaluation tools so researchers can reproduce results and extend to new language sets. ... -
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audioFlux
A library for audio and music analysis, feature extraction
A library for audio and music analysis, and feature extraction. Can be used for deep learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. audioflux is a deep learning tool library for audio and music analysis, feature extraction. It supports dozens of time-frequency analysis transformation methods and hundreds of corresponding time-domain and frequency-domain feature combinations. -
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Polyglot
Cross-platform AI language practice app
...Users can define custom AI personas, choose languages, and configure their own OpenAI and Azure keys so they retain control over which backends they use. The app supports speech recognition with quick keyboard shortcuts, allowing learners to hold down a key to speak and release it to submit for recognition and response. It includes translation features, dark mode, playback of the user’s own recorded speech, and word highlighting that tracks the progress of synthesized audio to make following along easier. Polyglot also integrates additional AI providers, supports configurable conversation scenarios, and lets users personalize avatars, making the experience more engaging and flexible. -
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KrillinAI
Video translation and dubbing tool powered by LLMs
...The tool offers “one-click” workflows and desktop versions, lowering the barrier for users who may not be familiar with video editing or audio processing pipelines. -
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TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch... -
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StreamSpeech
StreamSpeech is a seamless model for offline speech recognition
StreamSpeech is an “all-in-one” speech model designed to perform offline and simultaneous speech recognition, speech translation, and speech synthesis within a single unified architecture. Developed as part of an ACL 2024 paper, it targets streaming and low-latency scenarios where intermediate results and final translations or synthetic speech must be produced continuously as audio is being received. The model supports eight tasks: offline ASR, speech-to-text translation, speech-to-speech translation, and TTS, as well as their streaming or simultaneous counterparts, all handled by the same underlying system. ... -
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Seamless Communication
Foundational Models for State-of-the-Art Speech and Text Translation
...The motivation is to move beyond “text in, text out” and enable direct, live, multi-turn exchange involving language, gesture, gaze, vision, and modality switching without user friction. The system architecture includes a real-time multimodal signal pipeline for audio, video, and sensor data, a dialog manager that can decide when to act (speak, gesture, point) or query, and a cross-modal reasoning layer that fuses perception with semantic context. The research prototype includes components for visual grounding (understanding when a user references something in view), gesture recognition and synthesis, and turn-taking mechanisms that mirror human conversational timing. ... -
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Qwen3-Omni
Qwen3-omni is a natively end-to-end, omni-modal LLM
Qwen3-Omni is a natively end-to-end multilingual omni-modal foundation model that processes text, images, audio, and video and delivers real-time streaming responses in text and natural speech. It uses a Thinker-Talker architecture with a Mixture-of-Experts (MoE) design, early text-first pretraining, and mixed multimodal training to support strong performance across all modalities without sacrificing text or image quality. The model supports 119 text languages, 19 speech input languages, and...
