wav2vec2-base-intent-classification-ori Open-source Model - Free Deployment for Speech Intent Classification

Home

Wav2vec2 Base Intent Classification Ori

Developed by MuhammadIqbalBazmi

This model is a fine-tuned version of facebook/wav2vec2-base on the intent-dataset for intent classification tasks.

Audio Classification

Transformers

Open Source License:Apache-2.0 #Speech Intent Recognition #High Accuracy #wav2vec2 Fine-tuning

Downloads 18

Release Time : 10/11/2022

Model Overview

A wav2vec2-base fine-tuned intent classification model achieving 91.67% accuracy on the evaluation set.

Model Features

High Accuracy

Achieved 91.67% accuracy on the evaluation set.

Fine-tuned on wav2vec2-base

Utilizes the pre-trained wav2vec2-base model for fine-tuning to enhance intent classification performance.

Model Capabilities

Speech Intent Classification

Speech Feature Extraction

Use Cases

Voice Assistant

Voice Command Recognition

Recognizes user voice command intents.

High-accuracy intent classification

Smart Customer Service

Customer Intent Understanding

Understands the intent behind customer voice queries.

Improves response accuracy of customer service systems

🚀 wav2vec2-base-intent-classification-ori

This model is a fine - tuned version of facebook/wav2vec2-base for intent classification, offering high accuracy on the intent-dataset.

🚀 Quick Start

This model is a fine-tuned version of facebook/wav2vec2-base on the intent-dataset dataset. It achieves the following results on the evaluation set:

Loss: 0.4928
Accuracy: 0.9167

📚 Documentation

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 45

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1867	1.0	28	2.1745	0.2708
2.1177	2.0	56	2.1165	0.2708
2.1012	3.0	84	2.0553	0.2708
1.9851	4.0	112	1.9551	0.375
1.9092	5.0	140	1.9765	0.2917
1.6848	6.0	168	1.8461	0.2917
1.6576	7.0	196	1.5223	0.5
1.4492	8.0	224	1.4500	0.4792
1.2193	9.0	252	1.5349	0.4792
1.1149	10.0	280	1.2159	0.5833
1.0615	11.0	308	1.1469	0.6875
1.0584	12.0	336	1.2778	0.6042
0.8237	13.0	364	1.1774	0.5625
0.6699	14.0	392	0.9661	0.6875
0.7414	15.0	420	1.2787	0.5208
0.5324	16.0	448	0.8592	0.7292
0.3753	17.0	476	0.6860	0.7917
0.3274	18.0	504	0.6210	0.8333
0.3667	19.0	532	0.7310	0.75
0.2347	20.0	560	0.6801	0.7292
0.2036	21.0	588	0.9876	0.6875
0.1711	22.0	616	0.6323	0.7917
0.205	23.0	644	0.4414	0.8958
0.0892	24.0	672	0.4253	0.8958
0.0777	25.0	700	0.4703	0.8958
0.0717	26.0	728	0.4883	0.8958
0.041	27.0	756	0.6224	0.8542
0.0493	28.0	784	0.5839	0.875
0.0405	29.0	812	0.6454	0.8542
0.04	30.0	840	0.6102	0.875
0.0333	31.0	868	0.6080	0.875
0.0303	32.0	896	0.5539	0.875
0.025	33.0	924	0.5799	0.8958
0.0246	34.0	952	0.5766	0.8958
0.0209	35.0	980	0.5700	0.8958
0.0225	36.0	1008	0.5709	0.8958
0.0225	37.0	1036	0.5582	0.8958
0.0217	38.0	1064	0.5258	0.875
0.0207	39.0	1092	0.5058	0.8958
0.0234	40.0	1120	0.4981	0.8958
0.021	41.0	1148	0.4928	0.9167
0.0224	42.0	1176	0.4962	0.9167
0.0212	43.0	1204	0.5329	0.8958
0.0208	44.0	1232	0.5727	0.8958
0.0206	45.0	1260	0.5733	0.8958

Framework versions

Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1

📄 License

This model is licensed under the Apache-2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご