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Wav2vec2 Base Cynthia Tedlium 2500 V2
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Wav2vec2 Base Cynthia Tedlium 2500 V2

Developed by huyue012
This model is a fine-tuned speech recognition model based on facebook/wav2vec2-base-960h on the TED-LIUM dataset, achieving a word error rate of 20.33% on the evaluation set.
Speech Recognition
Transformers
Open Source License:Apache-2.0#Speech Recognition#English Transcription#Low WER
Downloads 25
Release Time : 3/2/2022

Model Overview

An optimized wav2vec2 model for English speech recognition tasks, suitable for speech-to-text applications.

Model Features

Low Word Error Rate
Achieves a word error rate of 20.33% on the TED-LIUM evaluation set, demonstrating excellent performance.
Based on wav2vec2 Architecture
Utilizes the proven wav2vec2-base-960h as the base model.
Fine-tuning Process
Undergoes a fine-tuning process with 50 training epochs and 3500 steps.

Model Capabilities

English Speech Recognition
Audio to Text Conversion
Continuous Speech Recognition

Use Cases

Education
Lecture Transcription
Automatically transcribe educational content like TED Talks into text.
Accuracy of approximately 80%
Meeting Minutes
Automated Meeting Minutes
Automatically record meeting content and generate text transcripts.
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