MC_RN

Siyong/MC_RN

MC_RN is a automatic speech recognition model from Siyong. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Jul 23, 2022
Updated: Jul 23, 2022
SHA: 86baeae8

Model card

From the Hugging Face model README

Millad_Customer_RN

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.5635
Wer: 0.8113
Cer: 0.4817

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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 4000
num_epochs: 600
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.9257	13.33	2000	2.0606	0.9767	0.5500
1.4828	26.67	4000	2.1161	0.9019	0.4932
1.2582	40.0	6000	2.0589	0.8504	0.4942
0.9804	53.33	8000	2.4633	0.8745	0.4763
0.7862	66.67	10000	2.4794	0.8861	0.4944
0.6492	80.0	12000	2.8693	0.8554	0.4928
0.5375	93.33	14000	2.6125	0.8296	0.4802
0.4462	106.67	16000	2.7591	0.8770	0.4974
0.3873	120.0	18000	3.0325	0.8379	0.4800
0.3445	133.33	20000	2.9965	0.8761	0.4986
0.3087	146.67	22000	3.3437	0.8221	0.4923
0.2755	160.0	24000	3.3022	0.8803	0.5211
0.2467	173.33	26000	3.2348	0.8479	0.4933
0.2281	186.67	28000	3.8010	0.8695	0.5081
0.2119	200.0	30000	3.0446	0.8545	0.4902
0.194	213.33	32000	3.0873	0.8454	0.4840
0.1677	226.67	34000	3.6184	0.8645	0.5019
0.1642	240.0	36000	3.2480	0.8412	0.4903
0.1656	253.33	38000	3.4379	0.8362	0.4816
0.1371	266.67	40000	3.5117	0.8479	0.5040
0.1301	280.0	42000	3.4360	0.8404	0.4870
0.128	293.33	44000	3.6589	0.8537	0.4977
0.1152	306.67	46000	4.2359	0.8545	0.5051
0.1119	320.0	48000	3.5818	0.7980	0.4882
0.1026	333.33	50000	3.7618	0.8013	0.4865
0.0945	346.67	52000	4.2197	0.8404	0.5028
0.0962	360.0	54000	3.9231	0.8653	0.5030
0.088	373.33	56000	3.8400	0.8354	0.4914
0.0743	386.67	58000	3.4924	0.8088	0.4824
0.0811	400.0	60000	3.8370	0.8396	0.4861
0.0696	413.33	62000	4.2808	0.8412	0.5065
0.0692	426.67	64000	4.0161	0.8088	0.4744
0.0622	440.0	66000	3.9080	0.8163	0.4910
0.0591	453.33	68000	3.9838	0.8113	0.4823
0.0527	466.67	70000	3.8067	0.8329	0.4914
0.056	480.0	72000	4.1415	0.8096	0.4782
0.0535	493.33	74000	4.3350	0.8229	0.4828
0.0531	506.67	76000	3.9808	0.8071	0.4807
0.0451	520.0	78000	4.0301	0.7988	0.4816
0.044	533.33	80000	4.4680	0.8371	0.4921
0.0389	546.67	82000	4.1380	0.8121	0.4819
0.0392	560.0	84000	4.3910	0.7930	0.4763
0.0389	573.33	86000	4.5086	0.8055	0.4802
0.0355	586.67	88000	4.6259	0.8113	0.4821
0.0307	600.0	90000	4.5635	0.8113	0.4817

Framework versions

Transformers 4.17.0
Pytorch 1.12.0+cu113
Datasets 1.18.3
Tokenizers 0.12.1

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Jul 23, 2022
Updated: Jul 23, 2022