all_accents_combined_4x79

This model is a fine-tuned version of zainulhakim/240801-wav2vec2-ASR-Global-All-Clients on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1243
• Wer: 0.0568
• Cer: 0.0209

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: 4
• 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: 500
• num_epochs: 500
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.6407	12.6582	1000	0.5311	0.5653	0.0874
0.3386	25.3165	2000	0.3585	0.3864	0.0491
0.2369	37.9747	3000	0.3723	0.3182	0.0584
0.1899	50.6329	4000	0.3953	0.3324	0.0509
0.1554	63.2911	5000	0.4053	0.3040	0.0550
0.1416	75.9494	6000	0.2952	0.2273	0.0425
0.1114	88.6076	7000	0.2022	0.2131	0.0368
0.0953	101.2658	8000	0.2619	0.2102	0.0371
0.0903	113.9241	9000	0.3288	0.2074	0.0375
0.0739	126.5823	10000	0.2549	0.1676	0.0321
0.0656	139.2405	11000	0.2227	0.1705	0.0293
0.0586	151.8987	12000	0.2873	0.1875	0.0349
0.0549	164.5570	13000	0.2169	0.1392	0.0243
0.0487	177.2152	14000	0.2097	0.1392	0.0267
0.0525	189.8734	15000	0.2497	0.1278	0.0337
0.0443	202.5316	16000	0.2112	0.1278	0.0291
0.0379	215.1899	17000	0.1633	0.1392	0.0237
0.038	227.8481	18000	0.2188	0.1420	0.0373
0.0408	240.5063	19000	0.3014	0.1335	0.0297
0.0287	253.1646	20000	0.2378	0.1222	0.0285
0.0294	265.8228	21000	0.2474	0.1278	0.0278
0.0276	278.4810	22000	0.2216	0.1136	0.0270
0.0323	291.1392	23000	0.1315	0.0881	0.0242
0.0239	303.7975	24000	0.1592	0.1080	0.0292
0.0187	316.4557	25000	0.1695	0.1335	0.0245
0.0169	329.1139	26000	0.2834	0.1136	0.0329
0.0163	341.7722	27000	0.1301	0.0852	0.0224
0.0151	354.4304	28000	0.2210	0.1080	0.0268
0.0102	367.0886	29000	0.1428	0.0852	0.0183
0.0098	379.7468	30000	0.1792	0.0653	0.0248
0.0104	392.4051	31000	0.2045	0.0682	0.0182
0.0101	405.0633	32000	0.2616	0.0881	0.0274
0.0069	417.7215	33000	0.1305	0.0739	0.0227
0.0063	430.3797	34000	0.1328	0.0795	0.0227
0.0046	443.0380	35000	0.1473	0.0682	0.0218
0.0039	455.6962	36000	0.1711	0.0710	0.0224
0.0028	468.3544	37000	0.1243	0.0568	0.0209
0.0022	481.0127	38000	0.1762	0.0625	0.0231
0.0025	493.6709	39000	0.1892	0.0625	0.0225

Framework versions

• Transformers 4.43.3
• Pytorch 2.3.1+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1