speecht5_tts_wolof

This model is a fine-tuned version of Moustapha91/speecht5_finetuned_wo_v1 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2943

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• training_steps: 4000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.9404	0.5952	50	0.4362
0.8342	1.1905	100	0.3784
0.7869	1.7857	150	0.3627
0.7841	2.3810	200	0.3546
0.762	2.9762	250	0.3489
0.7487	3.5714	300	0.3431
0.7423	4.1667	350	0.3392
0.7211	4.7619	400	0.3362
0.7147	5.3571	450	0.3304
0.7097	5.9524	500	0.3266
0.7058	6.5476	550	0.3223
0.6929	7.1429	600	0.3198
0.6887	7.7381	650	0.3152
0.664	8.3333	700	0.3131
0.6736	8.9286	750	0.3115
0.6767	9.5238	800	0.3105
0.6722	10.1190	850	0.3095
0.6702	10.7143	900	0.3075
0.6615	11.3095	950	0.3058
0.6654	11.9048	1000	0.3063
0.6682	12.5	1050	0.3083
0.6607	13.0952	1100	0.3051
0.6514	13.6905	1150	0.3042
0.6605	14.2857	1200	0.3041
0.6509	14.8810	1250	0.3028
0.6556	15.4762	1300	0.3025
0.6477	16.0714	1350	0.3019
0.6489	16.6667	1400	0.3011
0.6567	17.2619	1450	0.3007
0.6533	17.8571	1500	0.3016
0.6489	18.4524	1550	0.3009
0.6454	19.0476	1600	0.3002
0.6354	19.6429	1650	0.2992
0.645	20.2381	1700	0.2996
0.6376	20.8333	1750	0.2993
0.6472	21.4286	1800	0.2991
0.6571	22.0238	1850	0.2995
0.6333	22.6190	1900	0.2986
0.6323	23.2143	1950	0.2973
0.6314	23.8095	2000	0.2980
0.6437	24.4048	2050	0.2980
0.6383	25.0	2100	0.2977
0.6314	25.5952	2150	0.2978
0.6309	26.1905	2200	0.2965
0.6365	26.7857	2250	0.2965
0.6406	27.3810	2300	0.2966
0.6286	27.9762	2350	0.2968
0.6279	28.5714	2400	0.2963
0.6304	29.1667	2450	0.2967
0.6457	29.7619	2500	0.2960
0.6372	30.3571	2550	0.2958
0.6338	30.9524	2600	0.2952
0.6325	31.5476	2650	0.2956
0.6313	32.1429	2700	0.2951
0.6345	32.7381	2750	0.2956
0.6289	33.3333	2800	0.2949
0.6264	33.9286	2850	0.2947
0.6302	34.5238	2900	0.2952
0.6248	35.1190	2950	0.2945
0.626	35.7143	3000	0.2945
0.6248	36.3095	3050	0.2947
0.6306	36.9048	3100	0.2943
0.6258	37.5	3150	0.2944
0.6318	38.0952	3200	0.2947
0.6279	38.6905	3250	0.2947
0.628	39.2857	3300	0.2940
0.632	39.8810	3350	0.2947
0.6259	40.4762	3400	0.2939
0.6305	41.0714	3450	0.2943
0.6381	41.6667	3500	0.2939
0.6341	42.2619	3550	0.2942
0.6163	42.8571	3600	0.2937
0.6256	43.4524	3650	0.2934
0.628	44.0476	3700	0.2934
0.6371	44.6429	3750	0.2945
0.6209	45.2381	3800	0.2930
0.6285	45.8333	3850	0.2939
0.6309	46.4286	3900	0.2938
0.6216	47.0238	3950	0.2935
0.6352	47.6190	4000	0.2943

Framework versions

• Transformers 4.47.0.dev0
• Pytorch 2.4.0
• Datasets 3.0.1
• Tokenizers 0.20.0