Open-source Speech Recognition Model wavlm-large-CORAA-pt-cv7 - Accurately Identify Portuguese Speech Content

Wavlm Large CORAA Pt Cv7

Developed by lgris

Portuguese automatic speech recognition model based on WavLM-large architecture, fine-tuned on the Common Voice 7.0 dataset

Speech Recognition

Transformers

OtherOpen Source License:Apache-2.0 #Portuguese speech recognition #WavLM architecture #Common Voice fine-tuning

Downloads 15

Release Time : 3/2/2022

Model Overview

This model is an automatic speech recognition (ASR) system optimized for Portuguese, based on the WavLM-large architecture and fine-tuned on the Mozilla Common Voice 7.0 Portuguese dataset, capable of converting Portuguese speech into text.

Model Features

Portuguese optimization

Specifically optimized and fine-tuned for Portuguese speech recognition

High performance

Achieved a word error rate (WER) of 0.2261 on the evaluation set

Based on WavLM-large

Utilizes the advanced WavLM-large architecture as the base model

Model Capabilities

Portuguese speech recognition

Speech-to-text

Use Cases

Speech transcription

Voice memo to text

Automatically converts Portuguese voice memos into text

Accuracy approximately 77.39% (1-WER)

Assistive technology

Voice-controlled applications

Provides voice control interfaces for Portuguese-speaking users

🚀 wavlm-large-CORAA-pt-cv7

This model is a fine - tuned version of lgris/WavLM-large-CORAA-pt on the common_voice dataset. It offers significant value in the field of automatic speech recognition by providing a more accurate and efficient model for Portuguese language processing.

🚀 Quick Start

This model is a fine-tuned version of lgris/WavLM-large-CORAA-pt on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.2546
Wer: 0.2261

🔧 Technical Details

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
0.6029	0.13	100	0.3679	0.3347
0.5297	0.26	200	0.3516	0.3227
0.5134	0.39	300	0.3327	0.3167
0.4941	0.52	400	0.3281	0.3122
0.4816	0.65	500	0.3154	0.3102
0.4649	0.78	600	0.3199	0.3058
0.461	0.91	700	0.3047	0.2974
0.4613	1.04	800	0.3006	0.2900
0.4198	1.17	900	0.2951	0.2891
0.3864	1.3	1000	0.2989	0.2862
0.3963	1.43	1100	0.2932	0.2830
0.3953	1.56	1200	0.2936	0.2829
0.3962	1.69	1300	0.2952	0.2773
0.3811	1.82	1400	0.2915	0.2748
0.3736	1.95	1500	0.2839	0.2684
0.3507	2.08	1600	0.2914	0.2678
0.3277	2.21	1700	0.2895	0.2652
0.3344	2.34	1800	0.2843	0.2673
0.335	2.47	1900	0.2821	0.2635
0.3559	2.6	2000	0.2830	0.2599
0.3254	2.73	2100	0.2711	0.2577
0.3263	2.86	2200	0.2685	0.2546
0.3266	2.99	2300	0.2679	0.2521
0.3066	3.12	2400	0.2727	0.2526
0.2998	3.25	2500	0.2648	0.2537
0.2961	3.38	2600	0.2630	0.2519
0.3046	3.51	2700	0.2684	0.2506
0.3006	3.64	2800	0.2604	0.2492
0.2992	3.77	2900	0.2682	0.2508
0.2775	3.9	3000	0.2732	0.2440
0.2903	4.03	3100	0.2659	0.2427
0.2535	4.16	3200	0.2650	0.2433
0.2714	4.29	3300	0.2588	0.2394
0.2636	4.42	3400	0.2652	0.2434
0.2647	4.55	3500	0.2624	0.2371
0.2796	4.67	3600	0.2611	0.2373
0.2644	4.8	3700	0.2604	0.2341
0.2657	4.93	3800	0.2567	0.2331
0.2423	5.06	3900	0.2594	0.2322
0.2556	5.19	4000	0.2587	0.2323
0.2327	5.32	4100	0.2639	0.2299
0.2613	5.45	4200	0.2569	0.2310
0.2382	5.58	4300	0.2585	0.2298
0.2404	5.71	4400	0.2543	0.2287
0.2368	5.84	4500	0.2553	0.2286
0.2514	5.97	4600	0.2517	0.2279
0.2415	6.1	4700	0.2524	0.2270
0.2338	6.23	4800	0.2540	0.2265
0.219	6.36	4900	0.2549	0.2263
0.2428	6.49	5000	0.2546	0.2261

Framework versions

Transformers 4.16.1
Pytorch 1.10.0+cu111
Datasets 1.18.2
Tokenizers 0.11.0

📄 License

This model is licensed under the Apache - 2.0 license.

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