Open-source model wav2vec2-large-xlsr-coraa-portuguese-cv7 - Empowering accurate Portuguese speech recognition

Wav2vec2 Large Xlsr Coraa Portuguese Cv7

Developed by lgris

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

Speech Recognition

Transformers

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

Downloads 24

Release Time : 3/2/2022

Model Overview

This model is an automatic speech recognition (ASR) system optimized for Portuguese, based on the wav2vec2 architecture and fine-tuned on the Common Voice 7.0 dataset.

Model Features

High-precision Portuguese recognition

Fine-tuned on the Common Voice Portuguese dataset with a word error rate (WER) of 13.39%

Based on XLSR architecture

Leverages the advantages of pre-trained cross-lingual speech representation learning (XLSR)

Robust speech processing

Capable of handling various speech qualities and accent variations

Model Capabilities

Portuguese speech-to-text

Speech recognition

Speech transcription

Use Cases

Speech transcription

Portuguese speech transcription

Convert Portuguese speech content into text

Word error rate of 13.39%

Voice assistants

Portuguese voice command recognition

Used for voice command recognition in Portuguese voice assistant systems

🚀 wav2vec2-large-xlsr-coraa-portuguese-cv7

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, achieving excellent results on the evaluation set.

🚀 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: 0.1777
Wer: 0.1339

✨ Features

Based on the pre - trained model Edresson/wav2vec2-large-xlsr-coraa-portuguese, fine - tuned on the common_voice dataset.
Achieves relatively low loss and word error rate (Wer) on the evaluation set.

📚 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
0.4779	0.13	100	0.2620	0.2020
0.4505	0.26	200	0.2339	0.1998
0.4285	0.39	300	0.2507	0.2109
0.4148	0.52	400	0.2311	0.2101
0.4072	0.65	500	0.2278	0.1899
0.388	0.78	600	0.2193	0.1898
0.3952	0.91	700	0.2108	0.1901
0.3851	1.04	800	0.2121	0.1788
0.3496	1.17	900	0.2154	0.1776
0.3063	1.3	1000	0.2095	0.1730
0.3376	1.43	1100	0.2129	0.1801
0.3273	1.56	1200	0.2132	0.1776
0.3347	1.69	1300	0.2054	0.1698
0.323	1.82	1400	0.1986	0.1724
0.3079	1.95	1500	0.2005	0.1701
0.3029	2.08	1600	0.2159	0.1644
0.2694	2.21	1700	0.1992	0.1678
0.2733	2.34	1800	0.2032	0.1657
0.269	2.47	1900	0.2056	0.1592
0.2869	2.6	2000	0.2058	0.1616
0.2813	2.73	2100	0.1868	0.1584
0.2616	2.86	2200	0.1841	0.1550
0.2809	2.99	2300	0.1902	0.1577
0.2598	3.12	2400	0.1910	0.1514
0.24	3.25	2500	0.1971	0.1555
0.2481	3.38	2600	0.1853	0.1537
0.2437	3.51	2700	0.1897	0.1496
0.2384	3.64	2800	0.1842	0.1495
0.2405	3.77	2900	0.1884	0.1500
0.2372	3.9	3000	0.1950	0.1548
0.229	4.03	3100	0.1928	0.1477
0.2047	4.16	3200	0.1891	0.1472
0.2102	4.29	3300	0.1930	0.1473
0.199	4.42	3400	0.1914	0.1456
0.2121	4.55	3500	0.1840	0.1437
0.211	4.67	3600	0.1843	0.1403
0.2072	4.8	3700	0.1836	0.1428
0.2224	4.93	3800	0.1747	0.1412
0.1974	5.06	3900	0.1813	0.1416
0.1895	5.19	4000	0.1869	0.1406
0.1763	5.32	4100	0.1830	0.1394
0.2001	5.45	4200	0.1775	0.1394
0.1909	5.58	4300	0.1806	0.1373
0.1812	5.71	4400	0.1784	0.1359
0.1737	5.84	4500	0.1778	0.1353
0.1915	5.97	4600	0.1777	0.1349
0.1921	6.1	4700	0.1784	0.1359
0.1805	6.23	4800	0.1757	0.1348
0.1742	6.36	4900	0.1771	0.1341
0.1709	6.49	5000	0.1777	0.1339

Framework versions

Transformers 4.16.1
Pytorch 1.10.0+cu111
Datasets 1.18.2
Tokenizers 0.11.0

📄 License

This model is released under the Apache - 2.0 license.

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