Open-source w2v2-Kaqchikel Speech Recognition Model - Accurately Identify Kaqchikel Speech Audio Content

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W2v2 Kaqchikel

Developed by sil-ai

A speech recognition model fine-tuned on Kaqchikel audio data based on facebook/wav2vec2-xls-r-300m

Speech Recognition

Transformers

OtherOpen Source License:MIT #Kaqchikel speech recognition #Low-resource language model #XLS-R fine-tuning

Downloads 21

Release Time : 4/26/2022

Model Overview

This model is an optimized speech recognition system for the Kaqchikel language, fine-tuned using data from Deditos video clips and Bloom Library audiobooks, suitable for automatic speech-to-text tasks in Kaqchikel.

Model Features

Low-resource language support

Specially optimized for low-resource languages like Kaqchikel

Multi-source training data

Combines training data from two different sources: video clips and audiobooks

Good performance

Achieves a character error rate of 0.0854 and word error rate of 0.3069 on the evaluation set

Model Capabilities

Kaqchikel speech recognition

Audio-to-text conversion

Low-resource language processing

Use Cases

Education

Language learning assistance

Provides pronunciation evaluation and transcription functions for Kaqchikel language learners

Cultural preservation

Indigenous language documentation

Transcribes Kaqchikel oral traditions into written form for preservation

🚀 wav2vec2-large-xls-r-300m-kaqchikel-with-bloom

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m for speech recognition in Kaqchikel, leveraging audio data from multiple sources.

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🚀 Quick Start

This model is a fine - tuned version of facebook/wav2vec2-xls-r-300m on a collection of audio from Deditos videos in Kaqchikel provided by Viña Studios and Kaqchikel audio from audiobooks on Bloom Library. It achieves the following results on the evaluation set:

Loss: 0.6700
Cer: 0.0854
Wer: 0.3069

✨ Features

Fine - tuned on specific Kaqchikel audio data for better performance in relevant speech recognition tasks.
Achieved relatively low loss, CER, and WER on the evaluation set.

📚 Documentation

Model description

Homepage: SIL AI
Point of Contact: SIL AI email
Source Data: Bloom Library and Viña Studios

This model is a baseline model finetuned from XLS - R 300m. Users should refer to the original model for tutorials on using a trained model for inference.

Intended uses & limitations

Users of this model should abide by the UN Declarations on the Rights of Indigenous Peoples.

This model is released under the MIT license and no guarantees are made regarding the performance of the model in specific situations.

Training and evaluation data

Training, Validation, and Test datasets were generated from the same corpus, ensuring that no duplicate files were used.

Training procedure

Standard finetuning of XLS - R was used based on the examples in the [Hugging Face Transformers Github](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech - recognition)

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: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
11.1557	1.84	100	4.2251	1.0	1.0
3.7231	3.7	200	3.5794	1.0	1.0
3.3076	5.55	300	3.4362	1.0	1.0
3.2495	7.4	400	3.2553	1.0	1.0
3.2076	9.26	500	3.2932	1.0	1.0
3.1304	11.11	600	3.1100	1.0	1.0
2.899	12.95	700	2.4021	0.8477	1.0
2.2875	14.81	800	1.5473	0.4790	0.9984
1.7605	16.66	900	1.1034	0.3061	0.9192
1.3802	18.51	1000	0.9422	0.2386	0.8530
1.0989	20.37	1100	0.7429	0.1667	0.6042
0.857	22.22	1200	0.7490	0.1499	0.5751
0.6899	24.07	1300	0.6376	0.1286	0.4798
0.5927	25.92	1400	0.6887	0.1232	0.4443
0.4699	27.77	1500	0.6341	0.1184	0.4378
0.4029	29.62	1600	0.6341	0.1103	0.4216
0.3492	31.48	1700	0.6709	0.1121	0.4120
0.3019	33.33	1800	0.7665	0.1097	0.4136
0.2681	35.18	1900	0.6671	0.1085	0.4120
0.2491	37.04	2000	0.7049	0.1010	0.3748
0.2108	38.88	2100	0.6699	0.1064	0.3974
0.2146	40.73	2200	0.7037	0.1046	0.3780
0.1854	42.59	2300	0.6970	0.1055	0.4006
0.1693	44.44	2400	0.6593	0.0980	0.3764
0.1628	46.29	2500	0.7162	0.0998	0.3764
0.156	48.15	2600	0.6445	0.0998	0.3829
0.1439	49.99	2700	0.6437	0.1004	0.3845
0.1292	51.84	2800	0.6471	0.0944	0.3457
0.1287	53.7	2900	0.6411	0.0923	0.3538
0.1186	55.55	3000	0.6754	0.0992	0.3813
0.1175	57.4	3100	0.6741	0.0953	0.3538
0.1082	59.26	3200	0.6949	0.0977	0.3619
0.105	61.11	3300	0.6919	0.0983	0.3683
0.1048	62.95	3400	0.6802	0.0950	0.3425
0.092	64.81	3500	0.6830	0.0962	0.3263
0.0904	66.66	3600	0.6993	0.0971	0.3554
0.0914	68.51	3700	0.6932	0.0995	0.3554
0.0823	70.37	3800	0.6742	0.0950	0.3409
0.0799	72.22	3900	0.6852	0.0917	0.3279
0.0767	74.07	4000	0.6684	0.0929	0.3489
0.0736	75.92	4100	0.6611	0.0923	0.3393
0.0708	77.77	4200	0.7123	0.0944	0.3393
0.0661	79.62	4300	0.6577	0.0899	0.3247
0.0651	81.48	4400	0.6671	0.0869	0.3150
0.0607	83.33	4500	0.6980	0.0893	0.3231
0.0552	85.18	4600	0.6947	0.0884	0.3183
0.0574	87.04	4700	0.6652	0.0899	0.3183
0.0503	88.88	4800	0.6798	0.0863	0.3053
0.0479	90.73	4900	0.6690	0.0884	0.3166
0.0483	92.59	5000	0.6789	0.0872	0.3069
0.0437	94.44	5100	0.6758	0.0875	0.3069
0.0458	96.29	5200	0.6662	0.0884	0.3102
0.0434	98.15	5300	0.6699	0.0881	0.3069
0.0449	99.99	5400	0.6700	0.0854	0.3069

Framework versions

Transformers 4.11.3
Pytorch 1.10.0+cu113
Datasets 2.2.1
Tokenizers 0.10.3

📄 License

This model is released under the MIT license.

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