wav2vec2-xls-r-1b-hi-cv8 Open-Source Speech Recognition Model

Wav2vec2 Xls R 1b Hi Cv8

Developed by anuragshas

This is an automatic speech recognition model fine-tuned on the Common Voice 8.0 Hindi dataset based on the facebook/wav2vec2-xls-r-1b model, supporting Hindi speech-to-text tasks.

Speech Recognition

Transformers

OtherOpen Source License:Apache-2.0 #Hindi speech transcription #Low-resource speech recognition #XLS-R architecture

Downloads 16

Release Time : 3/2/2022

Model Overview

This model is specifically designed for Hindi speech recognition, capable of converting Hindi speech into text. It achieved a 36.7% word error rate on the Common Voice evaluation set.

Model Features

Large-scale pre-training foundation

Fine-tuned on the 1-billion-parameter wav2vec2-xls-r model, equipped with powerful speech feature extraction capabilities

Hindi optimization

Specially fine-tuned for Hindi speech data, adapting to Hindi pronunciation characteristics

Relatively low word error rate

Achieved a 36.7% word error rate on the evaluation set, performing well for Hindi speech recognition tasks

Model Capabilities

Hindi speech recognition

Speech-to-text

Supports long audio processing

Use Cases

Speech transcription

Hindi speech-to-text

Convert Hindi speech content into editable text format

Word error rate 36.7%

Assistive tools

Voice input system

Provide voice input support for Hindi users

🚀 Automatic Speech Recognition Model

This model is designed for automatic speech recognition, offering a practical solution for converting spoken language into text. It is fine - tuned from a well - known base model and evaluated on specific datasets to ensure performance.

🚀 Quick Start

This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 1b](https://huggingface.co/facebook/wav2vec2 - xls - r - 1b) on the MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set:

Loss: 0.6780
Wer: 0.3670

📚 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: 5e - 05
train_batch_size: 8
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1500
num_epochs: 50.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.514	2.07	400	1.4589	0.8531
1.4289	4.15	800	0.8940	0.6475
1.276	6.22	1200	0.7743	0.6089
1.2213	8.29	1600	0.6919	0.4973
1.1522	10.36	2000	0.6635	0.4588
1.0914	12.44	2400	0.6839	0.4586
1.0499	14.51	2800	0.7151	0.4467
1.0238	16.58	3200	0.6824	0.4436
0.9963	18.65	3600	0.6872	0.4437
0.9728	20.73	4000	0.7047	0.4244
0.9373	22.8	4400	0.6569	0.4189
0.9028	24.87	4800	0.6623	0.4094
0.8759	26.94	5200	0.6723	0.4152
0.8824	29.02	5600	0.6467	0.4017
0.8371	31.09	6000	0.6911	0.4080
0.8205	33.16	6400	0.7145	0.4063
0.7837	35.23	6800	0.7037	0.3930
0.7708	37.31	7200	0.6925	0.3840
0.7359	39.38	7600	0.7034	0.3829
0.7153	41.45	8000	0.7030	0.3794
0.7127	43.52	8400	0.6823	0.3761
0.6884	45.6	8800	0.6854	0.3711
0.6835	47.67	9200	0.6723	0.3665
0.6703	49.74	9600	0.6773	0.3668

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0

📄 License

This model is released under the Apache - 2.0 license.

Property	Details
Model Type	Automatic Speech Recognition
Training Data	MOZILLA - FOUNDATION/COMMON_VOICE_8_0 - HI
Tags	automatic - speech - recognition, mozilla - foundation/common_voice_8_0, generated_from_trainer
Datasets	common_voice

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