Wav2Vec2 Large XLS-R 300M Hi-WX1 Open-Source Speech Recognition Model

Wav2vec2 Large Xls R 300m Hi Wx1

Developed by DrishtiSharma

This is an automatic speech recognition (ASR) model fine-tuned on the Hindi Common Voice 7.0 dataset, based on Facebook's wav2vec2-xls-r-300m model.

Speech Recognition

Transformers

OtherOpen Source License:Apache-2.0 #Hindi speech recognition #Low word error rate #Common Voice dataset

Downloads 18

Release Time : 3/2/2022

Model Overview

This model is specifically designed for Hindi speech recognition tasks and has been optimized on the Hindi Common Voice 7.0 dataset.

Model Features

Hindi speech recognition

Speech recognition model specifically optimized for Hindi.

Based on large-scale pre-trained model

Fine-tuned from Facebook's wav2vec2-xls-r-300m model.

Relatively lightweight

With 300 million parameters, the model remains relatively lightweight while maintaining performance.

Model Capabilities

Hindi speech-to-text

Speech recognition

Speech transcription

Use Cases

Speech transcription

Hindi speech transcription

Convert Hindi speech content into text

Achieves a WER of 37.2% and CER of 11.76% on the Common Voice 7.0 test set.

Voice assistant

Hindi voice assistant

Provides speech recognition capabilities for Hindi voice assistants

🚀 wav2vec2-large-xls-r-300m-hi-wx1

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA - FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It's designed for automatic speech recognition, aiming to accurately transcribe speech in the Hindi language.

✨ Features

Fine - Tuned Model: Based on the pre - trained facebook/wav2vec2-xls-r-300m, it has been fine - tuned on the mozilla - foundation/common_voice_7_0 dataset for Hindi.
Evaluation Metrics: Achieves specific results on evaluation metrics such as WER (Word Error Rate) and CER (Character Error Rate).

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Evaluation Results

This model achieves the following results on the evaluation set:

Loss: 0.6552
Wer: 0.3200

Evaluation Commands

To evaluate on mozilla - foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs

To evaluate on speech - recognition - community - v2/dev_data NA

Training Hyperparameters

The following hyperparameters were used during training:

Property	Details
Learning Rate	0.00024
Train Batch Size	16
Eval Batch Size	8
Seed	42
Gradient Accumulation Steps	2
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	1800
Num Epochs	50
Mixed Precision Training	Native AMP

Training Results

Training Loss	Epoch	Step	Validation Loss	Wer
12.2663	1.36	200	5.9245	1.0
4.1856	2.72	400	3.4968	1.0
3.3908	4.08	600	2.9970	1.0
1.5444	5.44	800	0.9071	0.6139
0.7237	6.8	1000	0.6508	0.4862
0.5323	8.16	1200	0.6217	0.4647
0.4426	9.52	1400	0.5785	0.4288
0.3933	10.88	1600	0.5935	0.4217
0.3532	12.24	1800	0.6358	0.4465
0.3319	13.6	2000	0.5789	0.4118
0.2877	14.96	2200	0.6163	0.4056
0.2663	16.33	2400	0.6176	0.3893
0.2511	17.68	2600	0.6065	0.3999
0.2275	19.05	2800	0.6183	0.3842
0.2098	20.41	3000	0.6486	0.3864
0.1943	21.77	3200	0.6365	0.3885
0.1877	23.13	3400	0.6013	0.3677
0.1679	24.49	3600	0.6451	0.3795
0.1667	25.85	3800	0.6410	0.3635
0.1514	27.21	4000	0.6000	0.3577
0.1453	28.57	4200	0.6020	0.3518
0.134	29.93	4400	0.6531	0.3517
0.1354	31.29	4600	0.6874	0.3578
0.1224	32.65	4800	0.6519	0.3492
0.1199	34.01	5000	0.6553	0.3490
0.1077	35.37	5200	0.6621	0.3429
0.0997	36.73	5400	0.6641	0.3413
0.0964	38.09	5600	0.6722	0.3385
0.0931	39.45	5800	0.6365	0.3363
0.0944	40.81	6000	0.6454	0.3326
0.0862	42.18	6200	0.6497	0.3256
0.0848	43.54	6400	0.6599	0.3226
0.0793	44.89	6600	0.6625	0.3232
0.076	46.26	6800	0.6463	0.3186
0.0749	47.62	7000	0.6559	0.3225
0.0663	48.98	7200	0.6552	0.3200

Framework Versions

Property	Details
Transformers	4.16.2
Pytorch	1.10.0+cu111
Datasets	1.18.3
Tokenizers	0.11.0

📄 License

This model is licensed under the Apache - 2.0 license.

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