FunASR is an offline voice file transcription software package that integrates speech endpoint detection, speech recognition, and punctuation models. It can convert long audio and video files into punctuated text while supporting concurrent transcription of multiple requests. The system supports ITN and user-defined keywords, and the server integrates ffmpeg, accommodating various audio and video format inputs. It offers clients in multiple programming languages, making it ideal for enterprises and developers needing efficient and accurate voice transcription services.

Youtube-Whisper is a Gradio-based application that extracts audio from YouTube videos and transcribes it into text using OpenAI's Whisper model. This tool is highly beneficial for users needing to convert video content into text for analysis, archiving, or translation. It leverages cutting-edge artificial intelligence technology to enhance the accessibility and usability of video content.

Whisper large-v3-turbo is an advanced automatic speech recognition (ASR) and speech translation model proposed by OpenAI. It is trained on over 5 million hours of labeled data and can generalize to various datasets and domains in zero-shot settings. This model is a fine-tuned version of Whisper large-v3, reducing the number of decoding layers from 32 to 4 to enhance speed, though it may result in a slight decrease in quality.

OmniSenseVoice is an optimized speech recognition model based on SenseVoice, designed for rapid inference and accurate timestamps, providing a smarter and faster way to transcribe audio.

CrisperWhisper is an advanced variant of OpenAI's Whisper model, specifically designed for fast, accurate, verbatim speech recognition, providing precise word-level timestamps. Unlike the original Whisper model, CrisperWhisper aims to transcribe every spoken word, including filler words, pauses, stutters, and false starts. This model ranks first in word-level datasets such as TED and AMI, and has been accepted at INTERSPEECH 2024.

Seed-ASR is a speech recognition model developed by ByteDance that leverages large language models (LLMs). By inputting continuous speech representations and contextual information into the LLM, it significantly enhances performance in comprehensive evaluation sets across multiple fields, accents/dialects, and languages, guided by extensive training and context-awareness capabilities. Compared to recently released large ASR models, Seed-ASR achieves a 10%-40% reduction in word error rate on public test sets in both Chinese and English, further demonstrating its strong performance.

whisper-diarization is an open-source project that integrates Whisper's automatic speech recognition (ASR) capabilities, Voice Activity Detection (VAD), and speaker embedding technology. It improves the accuracy of speaker embeddings by extracting the audible portions of audio, generating transcriptions using Whisper, and correcting timestamps and alignment through WhisperX to minimize segmentation errors caused by temporal offsets. Subsequently, MarbleNet is employed for VAD and segmentation to eliminate silence, while TitaNet is used to extract speaker embeddings for identifying speakers in each segment. Finally, the results are correlated with the timestamps generated by WhisperX, determining the speaker of each word based on timestamps and realigning with a punctuation model to compensate for minor timing offsets.

WeST is an open-source speech recognition transcription model that achieves speech-to-text conversion in a concise format of 300 lines of code, based on a large language model (LLM). It includes a large language model, a speech encoder, and a projector, with only the projector being trainable. The development of WeST is inspired by SLAM-ASR and LLaMA 3.1, aiming to deliver efficient speech recognition capabilities through simplified code.

SenseVoiceSmall is a speech foundation model that supports multiple speech understanding capabilities, including automatic speech recognition (ASR), spoken language recognition (LID), speech emotion recognition (SER), and audio event detection (AED). After training for more than 400,000 hours on data, the model supports more than 50 languages and has a recognition performance that surpasses the Whisper model. The SenseVoiceSmall model, which is a small model, uses a non-autoregressive end-to-end framework with extremely low inference latency and handles a 10-second audio in only 70 milliseconds, which is 15 times faster than Whisper-Large. In addition, SenseVoice also provides convenient fine-tuning scripts and strategies, supports multi-concurrency request service deployment pipelines, and the client languages include Python, C++, HTML, Java, and C#.

FunASR is an offline voice file transcription software package that integrates speech endpoint detection, speech recognition, and punctuation models. It can convert long audio and video files into punctuated text while supporting concurrent transcription of multiple requests. The system supports ITN and user-defined keywords, and the server integrates ffmpeg, accommodating various audio and video format inputs. It offers clients in multiple programming languages, making it ideal for enterprises and developers needing efficient and accurate voice transcription services.

The Gardener Teleprompter is a desktop teleprompter application specifically designed for live streaming, speeches, teaching, and similar scenarios. Using advanced voice recognition technology, it automatically senses the user's speaking speed and intelligently adjusts the text scrolling speed to ensure alignment between prompting and expression. The product integrates cutting-edge AI technology, providing features such as copy optimization, multi-channel copy extraction, watermark-free video downloads, prohibited word detection, and voiceover for text, significantly improving text creation efficiency. The Gardener Teleprompter supports multiple window playback, catering to diverse display needs, with all windows available to be pinned on top to avoid obstruction, achieving truly invisible prompting. Background information indicates that the Gardener Teleprompter has stood the test of thousands of live streams, proving to be stable and durable, with a continuously innovating team dedicated to delivering excellent service.

Kaption AI is a Chrome browser extension that leverages artificial intelligence to transcribe audio messages on WhatsApp into text, offering message summaries and reply suggestions. This extension prioritizes user privacy and security by employing advanced AI technologies for accurate transcription and summarization. It is particularly suited for users who often use WhatsApp and find it challenging to listen to long audio messages, helping them save time and focus only on important information.

Rev AI offers high-precision voice transcription services supporting over 58 languages, capable of converting speech from video and audio applications into text. It sets accuracy standards by training on the most diverse collection of voices in the world. Rev AI also provides real-time streaming transcription, human transcription, language recognition, sentiment analysis, theme extraction, summarization, and translation services. Rev AI's technological advantages include low word error rates, minimal bias towards gender and ethnic accents, support for more languages, and provision of highly readable transcription texts. In addition, it complies with top global security standards, including SOC II, HIPAA, GDPR, and PCI compliance.

Youtube-Whisper is a Gradio-based application that extracts audio from YouTube videos and transcribes it into text using OpenAI's Whisper model. This tool is highly beneficial for users needing to convert video content into text for analysis, archiving, or translation. It leverages cutting-edge artificial intelligence technology to enhance the accessibility and usability of video content.

Whisper large-v3-turbo is an advanced automatic speech recognition (ASR) and speech translation model proposed by OpenAI. It is trained on over 5 million hours of labeled data and can generalize to various datasets and domains in zero-shot settings. This model is a fine-tuned version of Whisper large-v3, reducing the number of decoding layers from 32 to 4 to enhance speed, though it may result in a slight decrease in quality.

The Realtime API, launched by OpenAI, is a low-latency voice interaction API that enables developers to create fast voice-to-voice experiences within their applications. This API supports natural voice-to-voice conversation and can handle interruptions, similar to the advanced voice mode of ChatGPT. It operates through a WebSocket connection and supports function calls, allowing voice assistants to respond to user requests, trigger actions, or introduce new contexts. With this API, developers no longer need to combine multiple models to construct voice experiences; instead, they can achieve natural conversational interactions through a single API call.

OmniSenseVoice is an optimized speech recognition model based on SenseVoice, designed for rapid inference and accurate timestamps, providing a smarter and faster way to transcribe audio.

The Deepgram Voice Agent API is a unified voice-to-voice API that enables natural-sounding conversations between humans and machines. This API is backed by industry-leading speech recognition and synthesis models that allow for natural and real-time listening, thinking, and speaking. Deepgram is committed to advancing a voice-first AI future through its agent API, integrating cutting-edge generative AI technology to create business solutions with smooth, human-like speech agents.