Search Results for "whisper"
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Showing 98 open source projects for "whisper"
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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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Faster Whisper
Faster Whisper transcription with CTranslate2
Faster Whisper is an optimized implementation of the Whisper speech recognition model designed to deliver significantly faster inference while maintaining comparable accuracy. It leverages efficient inference engines and optimized computation strategies to reduce latency and resource consumption. The system is particularly useful for real-time or large-scale transcription tasks where performance is critical. -
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Whisper-WebUI
A Web UI for easy subtitle using whisper model
Whisper WebUI is an open-source browser-based interface that simplifies the use of Whisper speech recognition models by providing an intuitive graphical environment for transcription, translation, and subtitle generation. Built with Gradio, it allows users to upload audio or video files, process them locally, and generate accurate text outputs without relying on command-line tools. -
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whisper.cpp
Port of OpenAI's Whisper model in C/C++
whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. ... -
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whisper-timestamped
Multilingual Automatic Speech Recognition with word-level timestamps
Multilingual Automatic Speech Recognition with word-level timestamps and confidence. Whisper is a set of multi-lingual, robust speech recognition models trained by OpenAI that achieve state-of-the-art results in many languages. Whisper models were trained to predict approximate timestamps on speech segments (most of the time with 1-second accuracy), but they cannot originally predict word timestamps. This repository proposes an implementation to predict word timestamps and provide a more accurate estimation of speech segments when transcribing with Whisper models. ... -
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Buzz
Buzz transcribes and translates audio offline
...Multiple Whisper backends, Transformer models, and GPU acceleration options provide flexibility across different computers. Transcripts can be searched, reviewed alongside playback, exported in common subtitle formats, automated through a CLI, or extended with plugins. -
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WhisperLive
A nearly-live implementation of OpenAI's Whisper
WhisperLive is a “nearly live” implementation of OpenAI’s Whisper model focused on real-time transcription. It runs as a server–client system in which the server hosts a Whisper backend and clients stream audio to be transcribed with very low delay. The project supports multiple inference backends, including Faster-Whisper, NVIDIA TensorRT, and OpenVINO, allowing you to target GPUs and different CPU architectures efficiently. -
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Go OpenAI
OpenAI ChatGPT, GPT-3, GPT-4, DALL·E, Whisper API wrapper for Go
This library provides Go clients for OpenAI API. OpenAI ChatGPT, GPT-3, GPT-4, DALL·E, Whisper API wrapper for Go. -
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Voice-Pro
Comprehensive Gradio WebUI for audio processing
Voice-Pro is the best gradio WebUI for transcription, translation and text-to-speech. It can be easily installed with one click. Create a virtual environment using Miniconda, running completely separate from the Windows system (fully portable). Supports real-time transcription and translation, as well as batch mode. -
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WhisperKit
On-device Speech Recognition for Apple Silicon
WhisperKit is a Swift package that integrates OpenAI's popular Whisper speech recognition model with Apple's CoreML framework for efficient, local inference on Apple devices. Whisper has pulled the future forward when fast, free and virtually error-free translation and transcription will be ubiquitous. It inspired numerous developers to improve and deploy it with minimal friction and maximum performance. -
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Meetily
Privacy first, AI meeting assistant with 4x faster Parakeet/Whisper
...It’s built for organizations that want meeting intelligence without sending recordings or transcripts to third-party cloud services, which helps address compliance and data sovereignty requirements. The app supports live transcription with local model options (including Whisper- and Parakeet-based workflows) and presents the transcript as the meeting happens, making it useful both for note-taking and accessibility. After or during the session, it can produce structured, AI-generated summaries, and it’s designed to be flexible about where that summarization comes from, supporting local providers as well as external endpoints when allowed by policy. -
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WhisperJAV
Uses Qwen3-ASR, local LLM, Whisper, TEN-VAD
...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. The framework supports several speech recognition models, including Qwen-based ASR systems and fine-tuned Whisper models trained on domain-specific dialogue. -
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WhisperSpeech
An Open Source text-to-speech system built by inverting Whisper
WhisperSpeech is an open-source text-to-speech system created by “inverting” OpenAI’s Whisper, reusing its strengths as a semantic audio model to generate speech instead of only transcribing it. The project aims to be for speech what Stable Diffusion is for images: powerful, hackable, and safe for commercial use, with code under Apache-2.0/MIT and models trained only on properly licensed data. Its architecture follows a token-based, multi-stage pipeline inspired by AudioLM and SPEAR-TTS: Whisper is used to produce semantic tokens, EnCodec compresses the waveform into acoustic tokens, and Vocos reconstructs high-fidelity audio from those tokens. ... -
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WhisperX
Automatic Speech Recognition with Word-level Timestamps
WhisperX is an advanced speech recognition system built on top of OpenAI’s Whisper model, designed to improve transcription accuracy and timing precision for long-form audio. It addresses key limitations of standard Whisper implementations by introducing voice activity detection and forced alignment techniques to produce word-level timestamps. The system enables batched inference, significantly increasing transcription speed while maintaining high accuracy. -
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Buzz
Transcribe and translate audio offline on your personal computer
Buzz transcribes and translates audio to text offline using OpenAI's Whisper. Import audio and video files into Buzz and export them as TXT, SRT, or VTT files. Buzz supports Whisper, Whisper.cpp, Faster Whisper, Whisper-compatible models from the Hugging Face repository, and the OpenAI Whisper API. Get linux versions from: - https://flathub.org/apps/io.github.chidiwilliams.Buzz - https://snapcraft.io/buzz Home page of Buzz https://github.com/chidiwilliams/buzz Note for Windows: App is not signed, you will get a warning when you install it. ... -
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Whisper-Studio
Another whisper wrapper, built fully in C++, with some neat features.
a native lightweight C++ application for OpenAI's Whisper, with a few new things like transcribing audio in real-time, identifying speakers, auto-paste transcriptions, and a few other things. Its not the prettiest app, I suck at design, but it gets the job done. -
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VoiceInk
The best open-source alternative to Superwhisper & Wispr Flow
VoiceInk is a native macOS dictation application that converts speech into text almost instantly. It can process audio entirely on the device with local Whisper or Parakeet models, giving users a private offline workflow. Optional AI enhancement can clean up, rewrite, format, or adapt dictated text for different tasks. Modes apply different models, prompts, context settings, and shortcuts according to the active app or website. A personal dictionary improves recognition of names, technical terms, and recurring phrases, while configurable global shortcuts support toggle and push-to-talk recording. ... -
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ChatGPT Telegram Bot
A Telegram bot that integrates with OpenAI's official ChatGPT APIs
A Telegram bot that integrates with OpenAI's official ChatGPT, DALL·E and Whisper APIs to provide answers. Ready to use with minimal configuration required. -
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Handy STT
A free, open source, and extensible speech-to-text application
...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. To further refine accuracy and responsiveness, Handy integrates Silero’s Voice Activity Detection (VAD) for silence filtering, ensuring only speech segments are processed. -
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Scriberr
Self-hosted AI audio transcription
...Unlike cloud-based transcription services, Scriberr runs entirely on the user’s machine, ensuring that sensitive recordings are never sent to third-party servers and remain fully under user control. It leverages modern speech recognition models such as Whisper and other advanced architectures to deliver precise transcripts with word-level timing and speaker identification. The application includes a polished user interface that simplifies the management of recordings, transcripts, and annotations, making it suitable for both casual users and professionals handling large volumes of audio. ... -
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Hyprnote
Local-first AI Notepad for Private Meetings
Hyprnote is an open-source, privacy-first AI notepad app designed for taking notes during meetings—transcribing audio (microphone and system) and generating context-rich summaries using on-device AI models like Whisper and HyprLLM, all without any data leaving your machine.(turn0search7, turn0search1). Listens to your meetings while you write. Crafts smart summaries based on your quick notes. Runs completely offline using open-source models like Whisper or HyprLLM. Use approved third-party APIs like Gemini, Claude, or Azure-hosted GPT. -
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VideoCaptioner
AI-powered tool for generating, optimizing, and translating subtitles
...It integrates speech recognition, language processing, and translation technologies to automatically generate and refine subtitles from video or audio sources. VideoCaptioner uses speech-to-text engines such as Whisper variants to transcribe spoken content and convert it into subtitle text with accurate timestamps. After transcription, large language models are used to intelligently restructure subtitles into natural sentences, correct wording, and improve readability for viewers. It can also translate subtitles into other languages while preserving the original timing, making it suitable for multilingual video publishing and accessibility. ... -
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HeartMuLa
A Family of Open Sourced Music Foundation Models
...The project also includes HeartCodec, a music codec optimized for high reconstruction fidelity, enabling efficient tokenization and reconstruction workflows that are critical for training and generation pipelines. For text extraction from audio, it provides HeartTranscriptor, a Whisper-based model tuned specifically for lyrics transcription, which helps bridge generated or recorded audio back into structured text. It also introduces HeartCLAP, which aligns audio and text into a shared embedding space. -
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claude-video
Give Claude the ability to watch any video
...It accepts public video URLs or local video files, then extracts the information needed to answer user questions about what happened on screen and in the audio. The workflow checks captions first, downloads only what is necessary, extracts timestamped frames, and produces a transcript through native captions or Whisper fallback. It supports different detail levels so users can trade speed, token cost, and visual coverage depending on the task. The skill is useful for summarizing videos, reviewing screen recordings, analyzing content structure, diagnosing visual bugs, and turning course material into notes. It can be installed through Claude Code, agent skill hosts, claude.ai, or manual setup.
