Whisper-large-v3-atco2-asr Open-source Speech Recognition Model - Free Deployment for Air Traffic Control Scenarios

Whisper Large V3 Atco2 Asr

Developed by jlvdoorn

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%

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Air Traffic Control Speech Recognition #Low Word Error Rate #Multi-GPU Optimization

Downloads 1,792

Release Time : 11/10/2023

Model Overview

An Automatic Speech Recognition (ASR) model optimized for aviation communication scenarios, excelling in air traffic control command recognition tasks

Model Features

Aviation Scenario Optimization

Specially optimized for air traffic control communication scenarios, effectively recognizing aviation terminology and commands

Low Word Error Rate

Achieves a word error rate (WER) of 17.04% on the evaluation set, outperforming general speech recognition models in professional scenarios

Multi-GPU Training

Utilizes multi-GPU distributed training, supporting large-scale data processing

Model Capabilities

Aviation Speech Recognition

Real-time Speech-to-Text

Professional Terminology Recognition

Use Cases

Air Traffic Control

Control Command Transcription

Real-time conversion of voice communications between controllers and pilots into text

Word error rate 17.04%

Aviation Communication Record Analysis

Automated analysis of historical communication records for training and quality control

🚀 whisper-large-v3-atco2-asr

This model is a fine - tuned version of openai/whisper-large-v3 on the None dataset. It offers enhanced performance in relevant tasks, achieving the following results on the evaluation set:

Loss: 0.7695
Wer: 17.0374

📚 Documentation

Model Description

More information needed

Intended Uses & Limitations

More information needed

Training and Evaluation Data

More information needed

🔧 Technical Details

Training Procedure

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e - 05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi - GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 2800
mixed_precision_training: Native AMP

Training Results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1388	3.57	100	0.5488	20.1957
0.0313	7.14	200	0.5830	17.5712
0.0173	10.71	300	0.5898	20.4181
0.004	14.29	400	0.6201	16.3256
0.001	17.86	500	0.6543	18.4164
0.002	21.43	600	0.6499	17.8381
0.0003	25.0	700	0.6724	17.1263
0.0002	28.57	800	0.6890	16.9929
0.0002	32.14	900	0.7012	16.8594
0.0001	35.71	1000	0.7104	16.9484
0.0001	39.29	1100	0.7178	16.9039
0.0001	42.86	1200	0.7241	17.4377
0.0001	46.43	1300	0.7305	17.3488
0.0001	50.0	1400	0.7358	17.3043
0.0001	53.57	1500	0.7407	17.3043
0.0001	57.14	1600	0.7451	17.1263
0.0001	60.71	1700	0.7495	17.2598
0.0001	64.29	1800	0.7529	17.2153
0.0001	67.86	1900	0.7563	17.2598
0.0001	71.43	2000	0.7593	17.4377
0.0001	75.0	2100	0.7612	17.3932
0.0001	78.57	2200	0.7632	17.2598
0.0	82.14	2300	0.7651	17.1263
0.0	85.71	2400	0.7666	17.0819
0.0	89.29	2500	0.7681	17.0374
0.0	92.86	2600	0.7686	17.0374
0.0	96.43	2700	0.7695	17.1263
0.0	100.0	2800	0.7695	17.0374

Framework Versions

Transformers 4.35.0
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.14.1

📄 License

This model is released under the Apache - 2.0 license.

📦 Model Information

Property	Details
Model Type	whisper-large-v3-atco2-asr
Base Model	openai/whisper-large-v3
Tags	generated_from_trainer
Metrics	wer

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