20220415 - 210530 Open-source Speech Recognition Model - Free to Use and Accurately Recognize Speech Content

Home

20220415 210530

Developed by lilitket

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-xls-r-2b on the common_voice dataset

Speech Recognition

Transformers

Open Source License:Apache-2.0 #Speech Recognition #Multilingual Support #Low Word Error Rate

Downloads 20

Release Time : 4/15/2022

Model Overview

This is a fine-tuned model for speech recognition tasks, based on the wav2vec2-xls-r-2b architecture and trained on the common_voice dataset

Model Features

Large-scale Pre-trained Model Fine-tuning

Fine-tuned from the 2-billion-parameter wav2vec2-xls-r-2b model

Relatively Low Word Error Rate

Achieves a word error rate of 0.3881 on the evaluation set

Efficient Training

Optimized training process using techniques like gradient accumulation

Model Capabilities

Speech-to-Text

Automatic Speech Recognition

Use Cases

Speech Transcription

Speech-to-Text Service

Convert speech content into text transcripts

Word error rate 0.3881

Assistive Technology

Real-time Caption Generation

Generate real-time captions for video or live streaming content

🚀 20220415-210530

This model is a fine - tuned version of facebook/wav2vec2-xls-r-2b on the common_voice dataset. It addresses speech - related tasks by leveraging the pre - trained capabilities of the base model and further adapting them to the specific characteristics of the common_voice dataset. This fine - tuning process enables the model to achieve better performance on speech recognition and related tasks.

🚀 Quick Start

This model is a fine - tuned version of facebook/wav2vec2-xls-r-2b on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 0.6544
Wer: 0.3881

📚 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: 1e - 05
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 400
num_epochs: 1200
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.1495	2.27	200	2.4098	1.0
0.4347	4.54	400	1.4211	0.9914
0.2295	6.82	600	1.0229	0.9349
0.1349	9.09	800	1.0063	0.9228
0.1001	11.36	1000	1.0333	0.9197
0.0847	13.63	1200	0.9021	0.8725
0.0697	15.91	1400	0.9117	0.8779
0.0634	18.18	1600	0.9550	0.8725
0.0607	20.45	1800	0.9063	0.8303
0.0551	22.73	2000	0.8163	0.7956
0.0536	25.0	2200	0.7385	0.7235
0.0511	27.27	2400	0.7917	0.7215
0.0449	29.54	2600	0.7508	0.6938
0.0417	31.82	2800	0.6892	0.6775
0.0415	34.09	3000	0.7029	0.6790
0.0384	36.36	3200	0.6839	0.6895
0.0392	38.63	3400	0.7067	0.6872
0.0358	40.91	3600	0.7310	0.6763
0.0337	43.18	3800	0.7139	0.6548
0.0362	45.45	4000	0.6975	0.6427
0.0311	47.73	4200	0.7054	0.6412
0.0327	50.0	4400	0.6530	0.6151
0.0286	52.27	4600	0.6565	0.6076
0.0304	54.54	4800	0.6931	0.6283
0.0285	56.82	5000	0.6966	0.6108
0.0279	59.09	5200	0.6473	0.5854
0.0276	61.36	5400	0.6497	0.5920
0.0238	63.63	5600	0.6283	0.5846
0.0237	65.91	5800	0.6871	0.5885
0.0221	68.18	6000	0.6518	0.5593
0.0221	70.45	6200	0.6676	0.5601
0.0215	72.73	6400	0.6299	0.5550
0.022	75.0	6600	0.6719	0.5636
0.0198	77.27	6800	0.6082	0.5569
0.0222	79.54	7000	0.6156	0.5589
0.0172	81.82	7200	0.6414	0.5636
0.0188	84.09	7400	0.5874	0.5347
0.0202	86.36	7600	0.6320	0.5421
0.0165	88.63	7800	0.6345	0.5304
0.0164	90.91	8000	0.6243	0.5289
0.0167	93.18	8200	0.6237	0.5285
0.015	95.45	8400	0.5937	0.5203
0.0169	97.73	8600	0.6171	0.5343
0.0147	100.0	8800	0.6857	0.5476
0.0164	102.27	9000	0.6099	0.5160
0.0152	104.54	9200	0.6319	0.5285
0.0149	106.82	9400	0.6133	0.5296
0.0155	109.09	9600	0.6237	0.5285
0.0149	111.36	9800	0.6127	0.5012
0.0142	113.63	10000	0.6119	0.4836
0.013	115.91	10200	0.5974	0.4746
0.012	118.18	10400	0.6296	0.5016
0.0137	120.45	10600	0.5990	0.5023
0.0146	122.73	10800	0.5784	0.4875
0.0117	125.0	11000	0.5436	0.4766
0.0133	127.27	11200	0.5890	0.5020
0.0133	129.54	11400	0.6028	0.4895
0.0119	131.82	11600	0.5483	0.4840
0.0133	134.09	11800	0.5638	0.4934
0.0108	136.36	12000	0.5750	0.4758
0.0098	138.63	12200	0.5978	0.4891
0.012	140.91	12400	0.5524	0.4805
0.01	143.18	12600	0.5731	0.4895
0.0125	145.45	12800	0.5583	0.4579
0.0102	147.73	13000	0.5806	0.5035
0.01	150.0	13200	0.5721	0.4711
0.0113	152.27	13400	0.5351	0.4602
0.011	154.54	13600	0.5472	0.4551
0.0078	156.82	13800	0.6011	0.4610
0.0105	159.09	14000	0.5702	0.4672
0.0081	161.36	14200	0.5643	0.4454
0.0088	163.63	14400	0.5084	0.4536
0.0094	165.91	14600	0.5320	0.4680
0.0083	168.18	14800	0.5175	0.4423
0.0095	170.45	15000	0.5213	0.4583
0.0097	172.73	15200	0.5242	0.4590
0.0092	175.0	15400	0.5680	0.4587
0.0081	177.27	15600	0.5668	0.4579
0.0075	179.54	15800	0.5602	0.4489
0.0094	181.82	16000	0.5540	0.4485
0.0083	184.09	16200	0.5367	0.4278
0.0084	186.36	16400	0.5376	0.4583
0.0093	188.63	16600	0.5599	0.4310
0.0085	190.91	16800	0.5356	0.4317
0.0066	193.18	17000	0.5517	0.4419
0.0074	195.45	17200	0.5401	0.4329
0.0094	197.73	17400	0.5067	0.4415
0.0078	200.0	17600	0.5410	0.4466
0.0085	202.27	17800	0.5157	0.4321
0.0081	204.54	18000	0.5390	0.4255
0.0068	206.82	18200	0.5566	0.4415
0.0069	209.09	18400	0.5693	0.4341
0.0089	211.36	18600	0.5588	0.4438
0.0086	213.63	18800	0.5656	0.4470
0.008	215.91	19000	0.5712	0.4438
0.0083	218.18	19200	0.5627	0.4423
0.0078	220.45	19400	0.5905	0.4298
0.0059	222.73	19600	0.5746	0.4228
0.0072	225.0	19800	0.5362	0.4275
0.006	227.27	20000	0.5909	0.4220
0.0074	229.54	20200	0.5863	0.4224
0.0079	231.82	20400	0.5366	0.4306
0.0066	234.09	20600	0.5128	0.4302
0.0068	236.36	20800	0.5436	0.4228
0.0073	238.63	21000	0.5731	0.4325
0.0081	240.91	21200	0.5189	0.4177
0.0061	243.18	21400	0.5593	0.4236
0.0061	245.45	21600	0.5553	0.4267
0.0044	247.73	21800	0.5763	0.4286
0.0064	250.0	22000	0.5360	0.4321
0.006	252.27	22200	0.5577	0.4372
0.0052	254.54	22400	0.5387	0.4122
0.0054	256.82	22600	0.5117	0.4239
0.0057	259.09	22800	0.5498	0.4232
0.0069	261.36	23000	0.5263	0.4353
0.005	263.63	23200	0.5147	0.4177
0.0058	265.91	23400	0.5273	0.4173
0.006	268.18	23600	0.5879	0.4380
0.0059	270.45	23800	0.5377	0.4349
0.0055	272.73	24000	0.6061	0.4364
0.0058	275.0	24200	0.5977	0.4353
0.0051	277.27	24400	0.5847	0.4208
0.0046	279.54	24600	0.5728	0.4333
0.006	281.82	24800	0.5392	0.4204
0.0074	284.09	25000	0.5618	0.4232
0.0058	286.36	25200	0.5449	0.4197
0.0057	288.63	25400	0.5635	0.4169
0.0054	290.91	25600	0.5313	0.4173
0.0044	293.18	25800	0.5544	0.4306
0.0039	295.45	26000	0.5392	0.4247
0.0054	297.73	26200	0.5395	0.4271
0.0044	300.0	26400	0.5489	0.4228
0.0042	302.27	26600	0.5414	0.4173
0.0051	304.54	26800	0.5198	0.4193
0.005	306.82	27000	0.5297	0.4146
0.0051	309.09	27200	0.5414	0.4212
0.0057	311.36	27400	0.5204	0.4228
0.0049	313.63	27600	0.5806	0.4239
0.0036	315.91	27800	0.5771	0.4173
0.0045	318.18	28000	0.5517	0.4239
0.0051	320.45	28200	0.5498	0.4173
0.0043	322.73	28400	0.5791	0.4181
0.0044	325.0	28600	0.6030	0.4200
0.0067	327.27	28800	0.5799	0.4208
0.0041	329.54	29000	0.5871	0.4134
0.0048	331.82	29200	0.5471	0.4158
0.0031	334.09	29400	0.5977	0.4220
0.0042	336.36	29600	0.5813	0.4181
0.0045	338.63	29800	0.6167	0.4306
0.0044	340.91	30000	0.5661	0.4173
0.0029	343.18	30200	0.5680	0.4158
0.0037	345.45	30400	0.5747	0.4204
0.005	347.73	30600	0.5883	0.4349
0.0037	350.0	30800	0.6187	0.4189
0.0044	352.27	31000	0.5834	0.4431
0.0047	354.54	31200	0.5567	0.4247
0.0039	356.82	31400	0.5900	0.4314
0.0044	359.09	31600	0.5879	0.4216
0.0042	361.36	31800	0.5639	0.4220
0.0046	363.63	32000	0.5292	0.4185
0.0043	365.91	32200	0.5640	0.4353
0.0033	368.18	32400	0.5468	0.4208
0.002	370.45	32600	0.5836	0.4220
0.0043	372.73	32800	0.5692	0.4142
0.0038	375.0	33000	0.5739	0.4177
0.0039	377.27	33200	0.5824	0.4103
0.0028	379.54	33400	0.6069	0.4111
0.0038	381.82	33600	0.5868	0.4185
0.0041	384.09	33800	0.5169	0.4126
0.0037	386.36	34000	0.5395	0.4275
0.0063	388.63	34200	0.5293	0.4294
0.0042	390.91	34400	0.5472	0.4165
0.0039	393.18	34600	0.5391	0.4091
0.0036	395.45	34800	0.5360	0.4239
0.0036	397.73	35000	0.5511	0.4177
0.0019	400.0	35200	0.5775	0.4115
0.0038	402.27	35400	0.5376	0.4087
0.0035	404.54	35600	0.5755	0.4130
0.0042	406.82	35800	0.5443	0.4087
0.0036	409.09	36000	0.6091	0.4200
0.004	411.36	36200	0.5817	0.4247
0.0039	413.63	36400	0.5779	0.4255
0.003	415.91	36600

📄 License

This model is licensed under the Apache 2.0 license.

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご