Best 24 AI speech recognition Tools of 2025

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.

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.

Azure Cognitive Services Speech is a voice recognition and synthesis service launched by Microsoft. It supports speech-to-text and text-to-speech functionality in over 100 languages and dialects. By creating custom voice models that can handle specific jargon, background noise, and accents, it enhances transcription accuracy. Additionally, this service supports real-time speech-to-text, speech translation, and text-to-speech functionalities, catering to various business scenarios such as caption generation, call record analysis, video translation, etc.

ChatTTS_Speaker is an experimental project based on the ERes2NetV2 speaker recognition model, aiming to provide stability ratings and voice tagging for voice textures. It helps users select stable and requirement-compliant voice textures. The project is open-source, supporting online listening and downloading voice samples.

LookOnceToHear is an innovative smart earphone interaction system that allows users to select the target speaker they want to hear by simply using visual recognition. This technology was nominated for Best Paper at CHI 2024. It achieves real-time speech extraction through synthetic audio mixing, head-related transfer functions (HRTFs), and binaural room impulse responses (BRIRs), providing users with a novel way to interact.

Explore AssemblyAI's current research, news, and updates on speech AI technology. AssemblyAI's Universal-1 delivers industry-leading performance in multilingual environments, ensuring accuracy, power, and robustness to help global customers and developers build a wide array of speech AI applications. Universal-1 achieves 10% or higher improvements in English, Spanish, and German speech-to-text accuracy, reduces hallucination rates related to speech data and environmental noise, and enjoys customer favoritism with its code conversion capabilities.

Azure AI Studio is a suite of artificial intelligence services offered by Microsoft Azure, encompassing speech services. These services may include functions such as speech recognition, text-to-speech, and speech translation, enabling developers to incorporate voice-related intelligence into their applications.

The AV-HuBERT framework is a cutting-edge self-supervised representation learning model designed for audio-visual speech processing. It has achieved state-of-the-art lip reading, automatic speech recognition (ASR), and audio-visual speech recognition outcomes on the LRS3 audio-visual speech benchmark. The framework learns audio-visual speech representations through masked multimodal clustering predictions, offering robust self-supervised audio-visual speech recognition.

SonixTw AI Voice Cloning is a high-quality online artificial intelligence voice cloning product. Through a single recording, you can achieve cloning, retaining delicate emotions and tone. You can create digital twin identities for yourself and your team, fully utilize the power of your voice, and enhance your life experience and work efficiency.

WhisperKit is a tool for compressing and optimizing automatic speech recognition (ASR) models. It allows for model compression and optimization, providing detailed performance evaluation data. WhisperKit also offers quality assurance certifications for different datasets and model formats, and supports local reproducibility of test results.

Tencent Cloud Speech Recognition ASR offers the best speech-to-text service experience for developers. The voice recognition service features high accuracy, convenient access, and stable performance. Tencent Cloud Speech Recognition ASR provides real-time voice recognition, single phrase detection, and recording file recognition, catering to the needs of developers with various requirements. Technological advancements, high cost-effectiveness, multi-language support, suitable for customer service, meetings, courts, and more scenarios.

OpenVoice is an open-source voice cloning technology capable of accurately replicating reference voicemails and generating voices in various languages and accents. It offers flexible control over voice characteristics such as emotion, accent, and can adjust rhythm, pauses, and intonation. It achieves zero-shot cross-lingual voice cloning, meaning it does not require the language of the generated or reference voice to be present in the training data.

Whisper is a general-purpose speech recognition model. It is trained on a large and diverse set of audio data and is a multi-task model capable of performing multilingual speech recognition, speech translation, and language identification.

Developed by the Department of Electronic Engineering, Tsinghua University, and ByteDance, SALMONN is a large language model (LLM) that supports voice, audio events, and music input. Unlike models that only support voice or audio event input, SALMONN can perceive and understand various audio inputs, thereby achieving new capabilities such as multilingual speech recognition and translation, as well as audio-speech co-inference. This can be seen as giving the LLM 'auditory' and cognitive auditory abilities, making SALMONN a step towards artificial general intelligence with auditory capabilities.

Whisper Turbo aims to be an alternative to the OpenAI Whisper API. It consists of three parts: a compatibility layer that converts audio files of different formats into Whisper-compatible formats; a developer-friendly API supporting both batch and streaming inference; and the Rust + WebGPU inference framework Rumble, designed for fast cross-platform inference.