sew-d-small-100k-ft-timit Open-Source Automatic Speech Recognition Model - Free Deployment and Precise Recognition of Speech Content

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Sew D Small 100k Ft Timit

Developed by patrickvonplaten

An automatic speech recognition model fine-tuned on the TIMIT_ASR dataset based on asapp/sew-d-small-100k

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech recognition fine-tuning #TIMIT dataset #Small parameter model

Downloads 18

Release Time : 3/2/2022

Model Overview

This model is a small automatic speech recognition (ASR) model specifically optimized for the TIMIT_ASR dataset. It achieved a word error rate (WER) of 0.7987 on the evaluation set.

Model Features

Efficient speech recognition

Optimized speech recognition capability for the TIMIT dataset

Small-scale model

Relatively small model size, suitable for resource-limited environments

Fine-tuning optimization

Precisely fine-tuned on the base model for specific datasets

Model Capabilities

English speech recognition

Audio-to-text conversion

Speech content analysis

Use Cases

Speech technology research

Speech recognition benchmarking

Used to evaluate and compare the performance of different ASR models

Achieved a WER of 0.7987 on the TIMIT dataset

Educational applications

Pronunciation assessment

Can be used for evaluating pronunciation accuracy in language learning

🚀 sew-d-small-100k-ft-timit

This model is a fine - tuned version of asapp/sew-d-small-100k on the TIMIT_ASR - NA dataset, achieving a Loss of 1.7482 and a Wer of 0.7987 on the evaluation set.

🚀 Quick Start

This model is a fine-tuned version of asapp/sew-d-small-100k on the TIMIT_ASR - NA dataset. It achieves the following results on the evaluation set:

Loss: 1.7482
Wer: 0.7987

📚 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: 32
eval_batch_size: 1
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: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
4.2068	0.69	100	4.0802	1.0
2.9805	1.38	200	2.9792	1.0
2.9781	2.07	300	2.9408	1.0
2.9655	2.76	400	2.9143	1.0
2.8953	3.45	500	2.8775	1.0
2.7719	4.14	600	2.7815	0.9999
2.6531	4.83	700	2.6375	1.0065
2.6425	5.52	800	2.5602	1.0210
2.3963	6.21	900	2.4665	1.0591
2.1447	6.9	1000	2.2792	0.9848
2.2719	7.59	1100	2.2237	0.9465
2.3629	8.28	1200	2.1058	0.8907
2.0913	8.97	1300	2.0113	0.9070
1.8334	9.66	1400	1.9466	0.8177
1.6608	10.34	1500	1.9217	0.8698
2.2194	11.03	1600	1.9091	0.8727
1.9002	11.72	1700	1.8746	0.8332
1.6268	12.41	1800	1.8782	0.7951
1.6455	13.1	1900	1.8230	0.8225
2.0308	13.79	2000	1.8067	0.8560
1.855	14.48	2100	1.8129	0.8177
1.5901	15.17	2200	1.7891	0.8367
1.4848	15.86	2300	1.7821	0.8201
1.8754	16.55	2400	1.7700	0.8137
1.7975	17.24	2500	1.7795	0.8171
1.5194	17.93	2600	1.7605	0.7977
1.4374	18.62	2700	1.7529	0.7978
1.7498	19.31	2800	1.7522	0.8023
1.7452	20.0	2900	1.7482	0.7987

Framework versions

Transformers 4.12.0.dev0
Pytorch 1.8.1
Datasets 1.14.1.dev0
Tokenizers 0.10.3

📄 License

This model is licensed under the Apache - 2.0 license.

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