# English Speech Processing

This model is a speech recognition model fine-tuned on the LibriSpeech dataset based on facebook/wav2vec2-base, achieving a word error rate of 0.3174 on the evaluation set.

Distil-Whisper is a knowledge-distilled version of OpenAI Whisper-Large-v3, offering superior performance and efficiency.

Ichigo-llama3s is a large language model series that supports both audio and text input, focusing on enhancing speech understanding capabilities and user interaction experience.

WhisperNER is a novel model capable of simultaneous speech transcription and entity recognition, supporting open-type named entity recognition (NER).

A fine-tuned English phoneme recognition model based on facebook/wav2vec2-large-lv60 on the TIMIT dataset, achieving a phoneme error rate of 10.53%

Gazelle v0.2 is a joint speech-language model released by Tincans, supporting English.

A speech emotion recognition model fine-tuned on the RAVDESS dataset based on the wav2vec2-large-xlsr-53-english model

English speech emotion recognition model based on wav2vec2 architecture, fine-tuned on the RAVDESS dataset

A speech emotion recognition model fine-tuned based on facebook/wav2vec2-base, used to predict the speaker's emotions in audio samples.

Fine-tuned intent classification model based on facebook/wav2vec2-large-960h, achieving 77.08% accuracy on the evaluation set

Wav2Vec2 large speech recognition model fine-tuned on the TEDLIUM corpus, supporting English speech-to-text conversion

A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model, featuring a low Word Error Rate (WER).

A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, specializing in English speech-to-text tasks

A speech recognition model fine-tuned based on facebook/wav2vec2-base-960h

A speech recognition model fine-tuned based on facebook/wav2vec2-base-960h, achieving a word error rate of 1.0 on the evaluation set

This is a speech recognition model fine-tuned on the superb dataset based on distilhubert, primarily used for Automatic Speech Recognition (ASR) tasks.

This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, achieving a word error rate (WER) of 0.3384 on the evaluation set.

An automatic speech recognition (ASR) model trained based on Speech-to-Text Transformer (S2T), specifically designed for English speech recognition

A fine-tuned speech recognition model based on facebook/wav2vec2-base, supporting automatic speech-to-text tasks

An English speech recognition model fine-tuned on the librispeech_asr dataset based on facebook/wav2vec2-xls-r-300m

This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, focusing on English speech-to-text tasks.

A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, achieving a word error rate (WER) of 0.3355 on the evaluation set.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 0.52 on the evaluation set.

This model is a speech recognition model fine-tuned based on facebook/wav2vec2-base-960h, achieving a word error rate (WER) of 1.0 on the evaluation set.

A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, suitable for English speech-to-text tasks

This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, primarily used for English speech-to-text tasks.

A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model

A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, specializing in English speech-to-text tasks

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, achieving a word error rate of 0.4348 on the TIMIT dataset.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base, trained on the TIMIT dataset with a word error rate (WER) of 0.4398.

This model is a speech recognition model fine-tuned from facebook/wav2vec2-base, achieving a word error rate (WER) of 0.5664 on the evaluation set.

A speech recognition model fine-tuned from facebook/wav2vec2-base on the TIMIT dataset, with a WER of 0.4458 on the evaluation set

A speech recognition model fine-tuned from facebook/wav2vec2-base, trained on the TIMIT dataset

A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base

A speech recognition model fine-tuned on the TIMIT dataset based on the wav2vec2-base model

A fine-tuned speech recognition model based on facebook/wav2vec2-base, trained and evaluated on the TIMIT dataset.

A speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, for demonstration purposes

A speech recognition model fine-tuned based on facebook/wav2vec2-base-960h, achieving a 21.6% word error rate on the TIMIT dataset

A speech recognition model fine-tuned based on facebook/wav2vec2-base, trained on the TIMIT dataset with a Word Error Rate (WER) of 0.3468.

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m, specifically designed for English child speech recognition tasks.