The Best 1871 Speech Recognition Tools in 2025

Voice activity detection model based on pyannote.audio 2.1, used to identify speech activity segments in audio

This is a fine-tuned XLSR-53 large model for Portuguese speech recognition tasks, trained on the Common Voice 6.1 dataset, supporting Portuguese speech-to-text conversion.

Whisper is an advanced automatic speech recognition (ASR) and speech translation model proposed by OpenAI, trained on over 5 million hours of labeled data, with strong cross-dataset and cross-domain generalization capabilities.

Whisper is a state-of-the-art automatic speech recognition (ASR) and speech translation model developed by OpenAI, trained on over 5 million hours of labeled data, demonstrating strong generalization capabilities in zero-shot settings.

A Russian speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, supporting 16kHz sampled audio input

A Chinese speech recognition model fine-tuned based on facebook/wav2vec2-large-xlsr-53, supporting 16kHz sampling rate audio input.

A Dutch speech recognition model fine-tuned based on facebook/wav2vec2-large-xlsr-53, trained on the Common Voice and CSS10 datasets, supporting 16kHz audio input.

Japanese speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, supporting 16kHz sampling rate audio input

A text-to-audio forced alignment tool based on Hugging Face pre-trained models, supporting multiple languages with high memory efficiency

Arabic speech recognition model fine-tuned from facebook/wav2vec2-large-xlsr-53, trained on Common Voice and Arabic speech corpus

The Wav2Vec2 base model developed by Facebook, pre-trained and fine-tuned on 960 hours of LibriSpeech audio for English automatic speech recognition tasks.

Korean Automatic Speech Recognition (ASR) model based on Wav2Vec2 XLSR architecture, excelling on the Zeroth Korean dataset

A Hindi automatic speech recognition model fine-tuned on low-resource Indian language datasets based on facebook/wav2vec2-large-xlsr-53

A Danish automatic speech recognition model fine-tuned on Danish parliamentary speech dataset based on facebook/wav2vec2-xls-r-300m

This is a Hebrew automatic speech recognition model fine-tuned based on the facebook/wav2vec2-xls-r-300m model, optimized for performance through two-stage training on small-scale and large-scale datasets.

A speech recognition model fine-tuned on Filipino speech datasets based on facebook/wav2vec2-xls-r-300m

This is the CTranslate2 converted version of OpenAI's Whisper base model, designed for efficient speech recognition tasks.

Whisper large-v2 is a large-scale automatic speech recognition (ASR) model developed by OpenAI, supporting multilingual speech-to-text tasks.

CTranslate2 converted version of OpenAI Whisper tiny model for efficient speech recognition

HuBERT-Large is a self-supervised speech representation learning model fine-tuned on 960 hours of LibriSpeech data for automatic speech recognition tasks.

Whisper large-v3 is a large-scale multilingual automatic speech recognition (ASR) model developed by OpenAI, supporting speech-to-text tasks in multiple languages.

Automatic speech recognition model fine-tuned for Turkish based on facebook/wav2vec2-xls-r-300m

WavLM is a large-scale self-supervised pretrained speech model developed by Microsoft, pretrained on 16kHz sampled speech audio, suitable for various speech processing tasks.

This is a Portuguese automatic speech recognition model based on the XLS-R 1B architecture, fine-tuned on multiple Portuguese speech datasets.

Whisper is a pre-trained automatic speech recognition (ASR) and speech translation model, trained on 680k hours of labeled data with strong generalization capabilities.

A speech encoder based on the Conformer architecture, pretrained on 4.5 million hours of unlabeled audio data, supporting over 143 languages

Distil-Whisper is a knowledge-distilled version of Whisper large-v3, focusing on English automatic speech recognition, offering faster inference speeds while maintaining accuracy close to the original model.

XLSR-53 large model speech recognition system optimized for Polish, fine-tuned based on facebook/wav2vec2-large-xlsr-53, supports Polish automatic speech recognition

HuBERT is a self-supervised speech representation learning model that learns speech features through BERT-like prediction loss, suitable for tasks such as speech recognition.

WavLM is a large-scale self-supervised speech pre-training model developed by Microsoft, supporting full-stack speech processing tasks and excelling in the SUPERB benchmark.

CTranslate2 converted version of OpenAI Whisper small model for efficient speech recognition

This is a Whisper base.en model converted based on CTranslate2, used for English speech recognition tasks.

A speech recognition model based on wav2vec2-large, specifically designed to generate transcribed text with punctuation, suitable for TTS model construction.

Whisper Tiny is an automatic speech recognition (ASR) model developed by OpenAI, the smallest version in the Whisper series with 39M parameters.

This model is a multilingual phoneme recognition model fine-tuned on the CommonVoice dataset based on the wav2vec2-large-xlsr-53 pre-trained model, supporting the recognition of phoneme labels in multiple languages.

WhisperKit is a local speech recognition framework optimized for Apple Silicon, supporting efficient automatic speech recognition tasks.

XLSR-53 large model speech recognition system optimized for Persian, fine-tuned based on facebook/wav2vec2-large-xlsr-53 architecture

This is a version of the Whisper large-v3 turbo model converted to the CTranslate2 format for efficient automatic speech recognition tasks.

An English speech recognition model fine-tuned from the facebook/wav2vec2-large-xlsr-53 model, trained on the Common Voice 6.1 dataset

This is an automatic speech recognition model fine-tuned on Czech datasets based on facebook/wav2vec2-xls-r-300m, supporting 16kHz sampled audio input.

An automatic speech recognition model with 600 million parameters, supporting English transcription, punctuation, capitalization, and timestamp prediction

Ukrainian automatic speech recognition model based on facebook/wav2vec2-xls-r-300m, trained on the Common Voice 10.0 dataset