The open-source speech intent classification model wav2vec2-conformer-rel-pos-large-960h-ft has an accuracy of 58.33% on the evaluation set.

Wav2vec2 Conformer Rel Pos Large 960h Ft Intent Classification Ori

Developed by MuhammadIqbalBazmi

This model is a fine-tuned speech intent classification model based on facebook/wav2vec2-conformer-rel-pos-large-960h-ft, achieving an accuracy of 58.33% on the evaluation set.

Audio Classification

Transformers

Open Source License:Apache-2.0 #Speech intent classification #wav2vec2 architecture #High-precision speech processing

Downloads 15

Release Time : 10/12/2022

Model Overview

A speech intent classification model based on the wav2vec2-conformer architecture, used to identify intent categories in speech.

Model Features

Relative position encoding

Uses relative position encoding in the Conformer architecture, suitable for processing speech sequence data.

Fine-tuning optimization

Fine-tuned on the base model, focusing on intent classification tasks.

Moderate accuracy

Achieves 58.33% accuracy on the evaluation set.

Model Capabilities

Speech intent recognition

Speech classification

Use Cases

Voice assistant

Voice command classification

Identifies the intent category of user voice commands.

Accuracy 58.33%

🚀 wav2vec2-conformer-rel-pos-large-960h-ft-intent-classification-ori

This model is a fine - tuned version of facebook/wav2vec2-conformer-rel-pos-large-960h-ft on the None dataset. It can achieve certain performance in intent classification tasks, with specific evaluation results presented below.

🚀 Quick Start

This model is a fine - tuned version of facebook/wav2vec2-conformer-rel-pos-large-960h-ft on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2518
Accuracy: 0.5833

📚 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: 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
2.2018	1.0	28	2.1963	0.125
2.1871	2.0	56	2.1715	0.3333
2.1499	3.0	84	2.1349	0.3333
2.1236	4.0	112	2.0749	0.3333
2.0814	5.0	140	2.0232	0.3333
2.0905	6.0	168	1.9028	0.375
1.9167	7.0	196	1.8469	0.3958
1.7048	8.0	224	1.6481	0.4583
1.4723	9.0	252	1.5350	0.4583
1.5265	10.0	280	1.4526	0.5
1.2621	11.0	308	1.4451	0.4583
1.5083	12.0	336	1.3296	0.4792
1.1857	13.0	364	1.2983	0.4792
1.3449	14.0	392	1.3026	0.4792
1.2061	15.0	420	1.3181	0.4792
1.2544	16.0	448	1.2603	0.4792
1.0731	17.0	476	1.2607	0.4792
0.8836	18.0	504	1.2644	0.4792
1.0917	19.0	532	1.2345	0.4792
1.0786	20.0	560	1.2791	0.4792
1.1616	21.0	588	1.2238	0.4792
1.0614	22.0	616	1.2305	0.4583
0.9617	23.0	644	1.2315	0.4792
0.9652	24.0	672	1.2931	0.4792
0.9042	25.0	700	1.1246	0.5
1.0865	26.0	728	1.1490	0.4792
0.9653	27.0	756	1.1713	0.5
0.858	28.0	784	1.1726	0.5208
0.8364	29.0	812	1.2142	0.5
0.6798	30.0	840	1.2163	0.5208
0.9284	31.0	868	1.1398	0.4792
0.7383	32.0	896	1.2418	0.5208
0.651	33.0	924	1.1734	0.5
0.7416	34.0	952	1.2285	0.5
0.6287	35.0	980	1.1467	0.5833
0.6806	36.0	1008	1.1589	0.5625
0.6148	37.0	1036	1.1373	0.5833
0.7174	38.0	1064	1.2118	0.5625
0.6056	39.0	1092	1.2205	0.5833
0.7041	40.0	1120	1.2408	0.5833
0.631	41.0	1148	1.2350	0.5833
0.6028	42.0	1176	1.2787	0.5833
0.5942	43.0	1204	1.2463	0.5833
0.5441	44.0	1232	1.2496	0.5833
0.5042	45.0	1260	1.2518	0.5833

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.

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