wav2vec2-base-sound2 Open-source Speech Processing Model - Achieves 53.57% Accuracy and Is Highly Practical for Speech Processing

Wav2vec2 Base Sound2

Developed by learningdude

A speech processing model fine-tuned based on facebook/wav2vec2-base, achieving an accuracy of 53.57% on the evaluation set

Audio Classification

Transformers

Open Source License:Apache-2.0 #Audio Feature Extraction #Speech Classification #Transfer Learning

Downloads 17

Release Time : 5/5/2022

Model Overview

This model is a fine-tuned version of wav2vec2-base, suitable for sound-related tasks, with specific applications requiring further confirmation

Model Features

Fine-tuned based on wav2vec2-base

Optimized on the base model to adapt to specific sound processing tasks

Moderate Accuracy

Achieves an accuracy of 53.57% on the evaluation set

Model Capabilities

Sound Feature Extraction

Sound Classification

Use Cases

Sound Analysis

Environmental Sound Recognition

Identify different types of environmental sounds

🚀 wav2vec2-base-sound2

This model is a fine - tuned version of facebook/wav2vec2-base on an unknown dataset. It can achieve certain evaluation results, which shows its potential in relevant tasks.

🚀 Quick Start

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

Loss: 1.5012
Accuracy: 0.5357

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 9e - 05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 25

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	1	2.0762	0.0714
No log	2.0	2	2.0638	0.1429
No log	3.0	3	2.0387	0.2143
No log	4.0	4	2.0124	0.2143
No log	5.0	5	1.9864	0.2143
No log	6.0	6	1.9609	0.2143
No log	7.0	7	1.9235	0.2143
No log	8.0	8	1.9379	0.2143
No log	9.0	9	1.8627	0.2857
1.9713	10.0	10	1.8277	0.3214
1.9713	11.0	11	1.7765	0.3571
1.9713	12.0	12	1.7204	0.5
1.9713	13.0	13	1.6956	0.5
1.9713	14.0	14	1.6602	0.5357
1.9713	15.0	15	1.6277	0.5714
1.9713	16.0	16	1.6053	0.5
1.9713	17.0	17	1.5825	0.5
1.9713	18.0	18	1.5656	0.4286
1.9713	19.0	19	1.5616	0.4643
1.6334	20.0	20	1.5613	0.4286
1.6334	21.0	21	1.5419	0.5
1.6334	22.0	22	1.5166	0.5357
1.6334	23.0	23	1.5088	0.5
1.6334	24.0	24	1.5052	0.5
1.6334	25.0	25	1.5012	0.5357