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.

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#.

Emilia is an open-source multilingual field voice dataset specifically designed for large-scale voice generation research. It includes over 10,100 hours of high-quality voice data in six languages with corresponding text transcriptions, covering a variety of speaking styles and content types such as stand-up comedy, interviews, debates, sports commentary, and audiobooks.

SenseVoice is a speech foundation model with multiple speech understanding capabilities, including Automatic Speech Recognition (ASR), Language Identification (LID), Speech Emotion Recognition (SER), and Audio Event Detection (AED). It focuses on high-precision multilingual speech recognition, speech emotion recognition, and audio event detection, supporting over 50 languages and exceeding the recognition performance of the Whisper model. The model uses an autoregressive end-to-end framework, resulting in extremely low inference latency, making it an ideal choice for real-time speech processing.

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.

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.

The Xincheng Lingo Voice Model is an advanced artificial intelligence voice model, focusing on providing efficient and accurate voice recognition and processing services. It understands and processes natural language, making human-computer interaction smoother and more natural. Built on the powerful AI technology of Xihu Xincheng, this model aims to deliver high-quality voice interaction experiences across various scenarios.

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.