XLS-R-300M-UR-CV7 Open-Source Urdu Automatic Speech Recognition Model - Achieve Precise Speech Recognition for Free

Xls R 300m Ur Cv7

Developed by HarrisDePerceptron

This model is an Urdu automatic speech recognition (ASR) model fine-tuned on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset based on facebook/wav2vec2-xls-r-300m

Speech Recognition

Transformers

OtherOpen Source License:Apache-2.0 #Urdu ASR #Low-resource speech recognition #XLS-R fine-tuning

Downloads 19

Release Time : 3/2/2022

Model Overview

Automatic speech recognition model for Urdu speech-to-text tasks, trained on the Common Voice Urdu dataset

Model Features

High Accuracy Urdu Recognition

Achieves a word error rate (WER) of 0.72 on the Common Voice Urdu evaluation set

Based on Large-scale Pretrained Model

Fine-tuned on facebook's wav2vec2-xls-r-300m model with powerful speech feature extraction capabilities

Open Source License

Uses Apache 2.0 license, allowing commercial and research use

Model Capabilities

Urdu speech-to-text

Continuous speech recognition

Speech content transcription

Use Cases

Speech Transcription

Urdu Speech Content Transcription

Convert Urdu speech content into editable text format

Word error rate 0.72

Voice Assistants

Urdu Voice Command Recognition

Used for voice command recognition in Urdu voice assistant systems

🚀 Fine-tuned Wav2Vec2 Model for Urdu Automatic Speech Recognition

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UR dataset. It aims to provide high - quality automatic speech recognition for the Urdu language.

It achieves the following results on the evaluation set:

Loss: 1.2924
Wer: 0.7201

📚 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: 7.5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 200.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
11.2783	4.17	100	4.6409	1.0
3.5578	8.33	200	3.1649	1.0
3.1279	12.5	300	3.0335	1.0
2.9944	16.67	400	2.9526	0.9983
2.9275	20.83	500	2.9291	1.0009
2.8077	25.0	600	2.5633	0.9895
2.4438	29.17	700	1.9045	0.9564
1.9659	33.33	800	1.4114	0.7960
1.7092	37.5	900	1.2584	0.7637
1.517	41.67	1000	1.2040	0.7507
1.3966	45.83	1100	1.1273	0.7463
1.3197	50.0	1200	1.1054	0.6957
1.2476	54.17	1300	1.1035	0.7001
1.1796	58.33	1400	1.0890	0.7097
1.1237	62.5	1500	1.0883	0.7167
1.0777	66.67	1600	1.1067	0.7219
1.0051	70.83	1700	1.1115	0.7236
0.9521	75.0	1800	1.0867	0.7132
0.9147	79.17	1900	1.0852	0.7210
0.8798	83.33	2000	1.1411	0.7097
0.8317	87.5	2100	1.1634	0.7018
0.7946	91.67	2200	1.1621	0.7201
0.7594	95.83	2300	1.1482	0.7036
0.729	100.0	2400	1.1493	0.7062
0.7055	104.17	2500	1.1726	0.6931
0.6622	108.33	2600	1.1938	0.7001
0.6583	112.5	2700	1.1832	0.7149
0.6299	116.67	2800	1.1996	0.7175
0.5903	120.83	2900	1.1986	0.7132
0.5816	125.0	3000	1.1909	0.7010
0.5583	129.17	3100	1.2079	0.6870
0.5392	133.33	3200	1.2109	0.7228
0.5412	137.5	3300	1.2353	0.7245
0.5136	141.67	3400	1.2390	0.7254
0.5007	145.83	3500	1.2273	0.7123
0.4883	150.0	3600	1.2773	0.7289
0.4835	154.17	3700	1.2678	0.7289
0.4568	158.33	3800	1.2592	0.7350
0.4525	162.5	3900	1.2705	0.7254
0.4379	166.67	4000	1.2717	0.7306
0.4198	170.83	4100	1.2618	0.7219
0.4216	175.0	4200	1.2909	0.7158
0.4305	179.17	4300	1.2808	0.7167
0.399	183.33	4400	1.2750	0.7193
0.3937	187.5	4500	1.2719	0.7149
0.3905	191.67	4600	1.2816	0.7158
0.3892	195.83	4700	1.2951	0.7210
0.3932	200.0	4800	1.2924	0.7201

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.3
Tokenizers 0.11.0

📄 License

This model is licensed under the Apache-2.0 license.

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