wav2vec2-large-xls-r-300m-pt-cv Open-Source Model - Accurately Identify Portuguese Speech Content

Wav2vec2 Large Xls R 300m Pt Cv

Developed by lgris

An automatic speech recognition model fine-tuned on the Common Voice Portuguese dataset based on facebook/wav2vec2-xls-r-300m

Speech Recognition

Transformers

OtherOpen Source License:Apache-2.0 #Portuguese speech recognition #Multi-scenario robustness #Low CER transcription

Downloads 22

Release Time : 3/2/2022

Model Overview

This model is a wav2vec2 model optimized for Portuguese automatic speech recognition (ASR) tasks, fine-tuned on the Common Voice dataset, capable of converting Portuguese speech into text.

Model Features

Portuguese Optimization

Specifically optimized and fine-tuned for Portuguese speech recognition

Based on Large-scale Pre-trained Model

Fine-tuned based on the facebook/wav2vec2-xls-r-300m model, inheriting its powerful speech feature extraction capabilities

Multi-dataset Evaluation

Evaluated on multiple datasets including Common Voice and Robust Speech Event

Model Capabilities

Portuguese speech recognition

Speech-to-text

Use Cases

Speech Transcription

Portuguese Speech Transcription

Convert Portuguese speech content into text

Achieved a WER of 24.29% on the Common Voice test set

Voice Assistants

Portuguese Voice Command Recognition

Used for voice command recognition in Portuguese voice assistant systems

🚀 wav2vec2-large-xls-r-300m-pt-cv

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It's designed for automatic speech recognition, offering a practical solution for Portuguese speech processing tasks.

🚀 Quick Start

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.3418
Wer: 0.3581

📚 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: 0.0003
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: 500
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
10.9035	0.2	100	4.2750	1.0
3.3275	0.41	200	3.0334	1.0
3.0016	0.61	300	2.9494	1.0
2.1874	0.82	400	1.4355	0.8721
1.09	1.02	500	0.9987	0.7165
0.8251	1.22	600	0.7886	0.6406
0.6927	1.43	700	0.6753	0.5801
0.6143	1.63	800	0.6300	0.5509
0.5451	1.84	900	0.5586	0.5156
0.5003	2.04	1000	0.5493	0.5027
0.3712	2.24	1100	0.5271	0.4872
0.3486	2.45	1200	0.4953	0.4817
0.3498	2.65	1300	0.4619	0.4538
0.3112	2.86	1400	0.4570	0.4387
0.3013	3.06	1500	0.4437	0.4147
0.2136	3.27	1600	0.4176	0.4124
0.2131	3.47	1700	0.4281	0.4194
0.2099	3.67	1800	0.3864	0.3949
0.1925	3.88	1900	0.3926	0.3913
0.1709	4.08	2000	0.3764	0.3804
0.1406	4.29	2100	0.3787	0.3742
0.1342	4.49	2200	0.3645	0.3693
0.1305	4.69	2300	0.3463	0.3625
0.1298	4.9	2400	0.3418	0.3581

Framework versions

Transformers 4.11.3
Pytorch 1.10.0+cu111
Datasets 1.13.3
Tokenizers 0.10.3

📄 License

The model is released under the apache - 2.0 license.

📊 Model Information

Property	Details
Model Type	Fine - tuned wav2vec2 for Portuguese automatic speech recognition
Training Data	common_voice
Tags	automatic - speech - recognition, generated_from_trainer, robust - speech - event, pt, hf - asr - leaderboard

Results

Task	Dataset	Test WER	Test CER
Automatic Speech Recognition	Common Voice 6 (pt)	24.29	7.51
Automatic Speech Recognition	Robust Speech Event - Dev Data (sv)	55.72	21.82
Automatic Speech Recognition	Robust Speech Event - Dev Data (pt)	47.88	N/A
Automatic Speech Recognition	Robust Speech Event - Test Data (pt)	50.78	N/A

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