# Whisper Fine-tuning

A speech fluency classification model based on Whisper Large v3, capable of detecting speech fluency and disfluency types

An Indian English accent speech recognition model fine-tuned based on OpenAI Whisper-large-v3-turbo, achieving a 7.99% word error rate on Indian English accent datasets

Whisper-large-v3-turbo fine-tuned model for Indian English accent speech recognition, with a word error rate of 4.39%

A Japanese speech recognition model fine-tuned on the Common Voice 17.0 dataset based on openai/whisper-small

This model is a voice cloning model fine-tuned based on openai/whisper-large-v3, primarily used for speech recognition tasks, achieving a word error rate of 15.3572 on the evaluation set.

This project utilizes the Whisper model for speech emotion recognition, capable of classifying audio into different emotional categories such as happiness, sadness, and surprise.

A pronunciation accuracy evaluation model fine-tuned based on OpenAI Whisper-base, used to assess speech pronunciation accuracy

A speech recognition model fine-tuned on the Common Voice 16.1 Japanese dataset based on openai/whisper-large-v3, trained for 4000 steps

A Whisper-based speech recognition model optimized for Haitian Creole, featuring high-accuracy speech-to-text conversion

A speech recognition model fine-tuned based on OpenAI Whisper-large-v3, specializing in Air Traffic Control (ATCO) scenarios with a word error rate of 17.04%

An audio keyword recognition model fine-tuned based on openai/whisper-small, trained on the kw-spotting-fsc-sl-agv dataset with an evaluation accuracy of 99.98%

This model is fine-tuned on the Common Voice, JVS, and JSUT datasets for Japanese speech recognition tasks using openai/whisper-base.

A general language identification model fine-tuned based on openai/whisper-small, achieving 88.6% accuracy on the evaluation dataset

A speech language identification model fine-tuned on OpenAI Whisper-medium, achieving 88.05% accuracy on the FLEURS dataset

An automatic speech recognition model fine-tuned on the Common Voice 11.0 German dataset based on openai/whisper-large-v2, supporting German speech-to-text with a word error rate of 5.76

A speech recognition model fine-tuned on Arabic datasets based on openai/whisper-medium