wav2vec2-large-xls-r-300m-spanish-small Open Source Model

Wav2vec2 Large Xls R 300m Spanish Small

Developed by glob-asr

This is a Spanish speech recognition model based on the wav2vec2 architecture, fine-tuned on the Common Voice dataset with a word error rate (WER) of 0.2105.

Speech Recognition

Transformers

#Spanish speech recognition #Low word error rate #wav2vec2 fine-tuning

Downloads 58

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom, specifically designed for Spanish speech recognition tasks.

Model Features

Efficient Speech Recognition

Excellent performance on Spanish speech recognition tasks with a word error rate of only 0.2105

Based on Large-Scale Pre-trained Model

Built on the wav2vec2-large-xls-r-300m architecture with powerful speech feature extraction capabilities

Precise Fine-Tuning

Fine-tuned for 30 epochs on the Common Voice dataset, optimizing Spanish recognition performance

Model Capabilities

Spanish speech recognition

Speech-to-text

Continuous speech recognition

Use Cases

Speech Transcription

Spanish Meeting Minutes

Automatically convert Spanish meeting recordings into text transcripts

Word error rate around 21%

Voice Assistant

Provide speech recognition capabilities for Spanish voice assistants

Education

Language Learning Applications

Help learners practice Spanish pronunciation and listening

🚀 wav2vec2-large-xls-r-300m-spanish-small

This is a fine - tuned model based on the Transformer architecture. It addresses the task of speech - related processing on the common_voice dataset, offering high - quality speech recognition capabilities.

🚀 Quick Start

This model is a fine - tuned version of jhonparra18/wav2vec2-large-xls-r-300m-spanish-custom on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.3596
Wer: 0.2105

📚 Documentation

Training and Evaluation Data

More information needed

Model Description

More information needed

Intended Uses & Limitations

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: 30
mixed_precision_training: Native AMP

Training Results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1971	0.79	400	0.2169	0.2077
0.2293	1.58	800	0.2507	0.2418
0.2065	2.37	1200	0.2703	0.2459
0.1842	3.16	1600	0.2716	0.2495
0.1634	3.95	2000	0.2695	0.2510
0.1443	4.74	2400	0.2754	0.2435
0.1345	5.53	2800	0.3119	0.2654
0.1267	6.32	3200	0.3154	0.2573
0.1237	7.11	3600	0.3251	0.2666
0.1118	7.91	4000	0.3139	0.2503
0.1051	8.7	4400	0.3286	0.2573
0.0964	9.49	4800	0.3348	0.2587
0.0946	10.28	5200	0.3357	0.2587
0.0897	11.07	5600	0.3408	0.2590
0.0812	11.86	6000	0.3380	0.2560
0.079	12.65	6400	0.3304	0.2415
0.0753	13.44	6800	0.3557	0.2540
0.0717	14.23	7200	0.3507	0.2519
0.0691	15.02	7600	0.3554	0.2587
0.0626	15.81	8000	0.3619	0.2520
0.0661	16.6	8400	0.3609	0.2564
0.0582	17.39	8800	0.3818	0.2520
0.0556	18.18	9200	0.3685	0.2410
0.0515	18.97	9600	0.3658	0.2367
0.0478	19.76	10000	0.3701	0.2413
0.0486	20.55	10400	0.3681	0.2371
0.0468	21.34	10800	0.3607	0.2370
0.0452	22.13	11200	0.3499	0.2286
0.0399	22.92	11600	0.3647	0.2282
0.0393	23.72	12000	0.3638	0.2255
0.0381	24.51	12400	0.3359	0.2202
0.0332	25.3	12800	0.3488	0.2177
0.033	26.09	13200	0.3628	0.2175
0.0311	26.88	13600	0.3695	0.2195
0.0294	27.67	14000	0.3624	0.2164
0.0281	28.46	14400	0.3688	0.2113
0.0274	29.25	14800	0.3596	0.2105

Framework Versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.17.1.dev0
Tokenizers 0.11.0

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