Open-source model of wav2vec2-large-xlsr-coraa-portuguese-cv8 - Achieve accurate Portuguese speech recognition for free

Wav2vec2 Large Xlsr Coraa Portuguese Cv8

Developed by lgris

A Portuguese speech recognition model fine-tuned on the Common Voice dataset based on Edresson/wav2vec2-large-xlsr-coraa-portuguese

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Portuguese speech recognition #Low word error rate #XLSR fine-tuning

Downloads 34

Release Time : 3/2/2022

Model Overview

This model is an automatic speech recognition (ASR) model optimized for Portuguese, based on the wav2vec2-large-xlsr architecture and fine-tuned on the Common Voice 8.0 dataset, capable of converting Portuguese speech to text.

Model Features

High Accuracy

Achieves a word error rate (WER) of 13.65% on the evaluation set, demonstrating excellent performance.

Based on Large-Scale Pretraining

Fine-tuned from a pretrained model based on the wav2vec2-large-xlsr architecture.

Optimized Specifically for Portuguese

Specially fine-tuned on the Common Voice Portuguese dataset.

Model Capabilities

Portuguese speech recognition

Speech-to-text

Use Cases

Speech Transcription

Voice Memo Transcription

Convert Portuguese voice memos into searchable text

Accuracy approximately 86.35%

Accessibility Applications

Real-time Caption Generation

Generate real-time captions for Portuguese video content

🚀 wav2vec2-large-xlsr-coraa-portuguese-cv8

This model is a fine - tuned version of Edresson/wav2vec2-large-xlsr-coraa-portuguese on the common_voice dataset. It offers high - quality speech recognition capabilities with low loss and word error rate.

🚀 Quick Start

This model is a fine-tuned version of Edresson/wav2vec2-large-xlsr-coraa-portuguese on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.1626
Wer: 0.1365

📚 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
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
training_steps: 5000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.5614	0.1	100	0.2542	0.1986
0.5181	0.19	200	0.2740	0.2146
0.5056	0.29	300	0.2472	0.2068
0.4747	0.39	400	0.2464	0.2166
0.4627	0.48	500	0.2277	0.2041
0.4403	0.58	600	0.2245	0.1977
0.4413	0.68	700	0.2156	0.1968
0.437	0.77	800	0.2102	0.1919
0.4305	0.87	900	0.2130	0.1864
0.4324	0.97	1000	0.2144	0.1902
0.4217	1.06	1100	0.2230	0.1891
0.3823	1.16	1200	0.2033	0.1774
0.3641	1.25	1300	0.2143	0.1830
0.3707	1.35	1400	0.2034	0.1793
0.3767	1.45	1500	0.2029	0.1823
0.3483	1.54	1600	0.1999	0.1740
0.3577	1.64	1700	0.1928	0.1728
0.3667	1.74	1800	0.1898	0.1726
0.3283	1.83	1900	0.1920	0.1688
0.3571	1.93	2000	0.1904	0.1649
0.3467	2.03	2100	0.1994	0.1648
0.3145	2.12	2200	0.1940	0.1682
0.3186	2.22	2300	0.1879	0.1571
0.3058	2.32	2400	0.1975	0.1678
0.3096	2.41	2500	0.1877	0.1589
0.2964	2.51	2600	0.1862	0.1568
0.3068	2.61	2700	0.1809	0.1588
0.3036	2.7	2800	0.1769	0.1573
0.3084	2.8	2900	0.1836	0.1524
0.3109	2.9	3000	0.1807	0.1519
0.2969	2.99	3100	0.1851	0.1516
0.2698	3.09	3200	0.1737	0.1490
0.2703	3.19	3300	0.1759	0.1457
0.2759	3.28	3400	0.1778	0.1471
0.2728	3.38	3500	0.1717	0.1462
0.2398	3.47	3600	0.1767	0.1451
0.256	3.57	3700	0.1742	0.1410
0.2712	3.67	3800	0.1674	0.1414
0.2648	3.76	3900	0.1717	0.1423
0.2576	3.86	4000	0.1672	0.1403
0.2504	3.96	4100	0.1683	0.1381
0.2406	4.05	4200	0.1685	0.1399
0.2403	4.15	4300	0.1656	0.1381
0.2233	4.25	4400	0.1687	0.1371
0.2546	4.34	4500	0.1642	0.1377
0.2431	4.44	4600	0.1655	0.1372
0.2337	4.54	4700	0.1625	0.1370
0.2607	4.63	4800	0.1618	0.1363
0.2292	4.73	4900	0.1622	0.1366
0.2232	4.83	5000	0.1626	0.1365

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.2
Tokenizers 0.11.0

📄 License

This project is licensed under the Apache-2.0 license.

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