Wav2vec2-base-timit-demo-google-colab Open Source Model - Free Deployment to Boost Speech Recognition

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Wav2vec2 Base Timit Demo Google Colab

Developed by ones

This model is a fine-tuned version of facebook/wav2vec2-base, primarily used for speech recognition tasks.

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #TIMIT Dataset #Low-Resource Optimization

Downloads 108

Release Time : 6/29/2022

Model Overview

This model is a fine-tuned version of facebook/wav2vec2-base on the TIMIT dataset, designed for speech recognition tasks.

Model Features

Fine-Tuned on wav2vec2-base

This model is fine-tuned on the facebook/wav2vec2-base architecture, making it suitable for specific speech recognition tasks.

Low Training Loss

During training, the model exhibited low loss values, indicating strong fitting capabilities.

Model Capabilities

Speech Recognition

Audio-to-Text Conversion

Use Cases

Speech Recognition

Speech-to-Text

Convert speech signals into text content

Word Error Rate (WER): 0.9988

🚀 wav2vec2-base-timit-demo-google-colab

This model is a fine - tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5112
Wer: 0.9988

🚀 Quick Start

This section provides a high - level overview of the model and its performance. For more detailed information, please refer to the following sections.

📚 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: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.5557	1.0	500	1.6786	1.0
0.8407	2.01	1000	0.5356	0.9988
0.4297	3.01	1500	0.4431	0.9988
0.2989	4.02	2000	0.4191	0.9988
0.2338	5.02	2500	0.4251	0.9988
0.1993	6.02	3000	0.4618	0.9988
0.1585	7.03	3500	0.4577	0.9988
0.1386	8.03	4000	0.4099	0.9982
0.1234	9.04	4500	0.4945	0.9988
0.1162	10.04	5000	0.4597	0.9988
0.1008	11.04	5500	0.4563	0.9988
0.0894	12.05	6000	0.5157	0.9988
0.083	13.05	6500	0.5027	0.9988
0.0735	14.06	7000	0.4905	0.9994
0.0686	15.06	7500	0.4552	0.9988
0.0632	16.06	8000	0.5522	0.9988
0.061	17.07	8500	0.4874	0.9988
0.0626	18.07	9000	0.5243	0.9988
0.0475	19.08	9500	0.4798	0.9988
0.0447	20.08	10000	0.5250	0.9988
0.0432	21.08	10500	0.5195	0.9988
0.0358	22.09	11000	0.5008	0.9988
0.0319	23.09	11500	0.5376	0.9988
0.0334	24.1	12000	0.5149	0.9988
0.0269	25.1	12500	0.4911	0.9988
0.0275	26.1	13000	0.4907	0.9988
0.027	27.11	13500	0.4992	0.9988
0.0239	28.11	14000	0.5021	0.9988
0.0233	29.12	14500	0.5112	0.9988

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cu113
Datasets 1.18.3
Tokenizers 0.12.1

📄 License

This model is licensed under the Apache - 2.0 license.

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