# Speech-to-Text

An ONNX-optimized Whisper large speech recognition model designed for web deployment

A Malayalam automatic speech recognition model fine-tuned based on facebook/w2v-bert-2.0, trained on multiple Malayalam datasets and using a trigram language model trained with the KENLM library.

Whisper is an automatic speech recognition (ASR) system trained by OpenAI, supporting multilingual speech transcription.

This model is a fine-tuned speech recognition model based on OpenAI's Whisper large on the Common Voice 14.0 Swahili (SW) dataset, achieving a word error rate (WER) of 25.13%.

Distilled version of Whisper Large v3 for efficient automatic speech recognition (ASR)

This is a version converted from the GGML format of openai/whisper-tiny to Ratchet's custom format

A speech-to-text model fine-tuned based on openai/whisper-small, supporting audio translation and text generation tasks.

This model is a fine-tuned version of openai/whisper-small for speech recognition, supporting multiple languages and suitable for automatic speech recognition tasks.

This is a model for Marathi speech processing, potentially involving speech recognition or speech synthesis tasks.

Whisper Medium is a medium-scale speech recognition model developed by OpenAI, supporting automatic speech recognition (ASR) tasks in multiple languages.

Whisper Small is a small automatic speech recognition (ASR) model developed by OpenAI, capable of converting speech into text.

Whisper is an automatic speech recognition (ASR) system trained by OpenAI, supporting speech-to-text tasks in multiple languages.

Whisper Tiny is a lightweight speech recognition model open-sourced by OpenAI, suitable for web deployment.

A SpeechT5 automatic speech recognition model fine-tuned on the LibriSpeech dataset, supporting speech-to-text conversion.

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 recognition model fine-tuned using the uob_singlish dataset based on the facebook/wav2vec2-xls-r-300m pre-trained model

This model is a Turkish speech recognition model fine-tuned on the Common Voice Turkish dataset based on facebook/wav2vec2-xls-r-300m, achieving a word error rate of 30.95% on the evaluation set.

Wav2Vec2 is a speech recognition model developed by Facebook. It learns speech representations from raw audio through self-supervised learning and is fine-tuned on the LibriSpeech dataset to achieve high-accuracy speech transcription.

A speech recognition model fine-tuned on the LibriSpeech ASR clean dataset, based on wav2vec2-base and bart-base

Wav2vec2 model fine-tuned for Brazilian Portuguese using the CETUC dataset, trained with approximately 145 hours of Brazilian Portuguese speech data

An automatic speech recognition model based on Hubert and BART architecture, achieving speech-to-text conversion through clustered feature transformation

A speech recognition model fine-tuned on the Common Voice Arabic dataset based on facebook/wav2vec2-xls-r-300m

This is a baseline model for Khmer automatic speech recognition (ASR), designed to provide foundational support for Khmer speech recognition tasks.

A lightweight randomly initialized Wav2Vec2 model for speech recognition, primarily for testing and development purposes

A fine-tuned Facebook wav2vec2 model for the speech-to-text module of The Sound of AI Open Source Research Group

This model is an automatic speech recognition (ASR) model fine-tuned on the librispeech_asr-clean dataset, based on wav2vec2-large-lv60 and bart-large

This is a speech recognition model based on Facebook's wav2vec2-large-100k-voxpopuli, fine-tuned using Common Voice 7.0 and M-AILABS Russian data.

A fine-tuned speech recognition model based on Doogie/Waynehills-STT-doogie-server

This is an Urdu automatic speech recognition model based on the wav2vec2 architecture, fine-tuned on the Common Voice 8.0 Urdu dataset