ABG_TTS

This model is a fine-tuned version of microsoft/speecht5_tts on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

• Loss: 0.5066

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: 2
• seed: 42
• gradient_accumulation_steps: 8
• 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: 100
• training_steps: 5000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.7566	0.3972	100	0.6804
0.6819	0.7944	200	0.6558
0.6512	1.1917	300	0.6097
0.6315	1.5889	400	0.5807
0.6092	1.9861	500	0.5860
0.619	2.3833	600	0.5818
0.6099	2.7805	700	0.5627
0.5908	3.1778	800	0.5849
0.5887	3.5750	900	0.5505
0.585	3.9722	1000	0.5505
0.5773	4.3694	1100	0.5550
0.5708	4.7666	1200	0.5419
0.5664	5.1639	1300	0.5425
0.559	5.5611	1400	0.5422
0.5605	5.9583	1500	0.5289
0.5641	6.3555	1600	0.5327
0.5507	6.7527	1700	0.5443
0.5622	7.1500	1800	0.5346
0.5613	7.5472	1900	0.5355
0.5469	7.9444	2000	0.5325
0.5523	8.3416	2100	0.5267
0.5477	8.7388	2200	0.5186
0.5466	9.1360	2300	0.5192
0.5383	9.5333	2400	0.5179
0.5332	9.9305	2500	0.5165
0.5351	10.3277	2600	0.5148
0.5377	10.7249	2700	0.5186
0.5295	11.1221	2800	0.5196
0.5263	11.5194	2900	0.5133
0.5301	11.9166	3000	0.5138
0.5209	12.3138	3100	0.5143
0.5205	12.7110	3200	0.5103
0.5132	13.1082	3300	0.5157
0.5194	13.5055	3400	0.5085
0.5177	13.9027	3500	0.5110
0.5156	14.2999	3600	0.5100
0.5082	14.6971	3700	0.5098
0.5179	15.0943	3800	0.5053
0.5165	15.4916	3900	0.5064
0.5036	15.8888	4000	0.5058
0.504	16.2860	4100	0.5098
0.5119	16.6832	4200	0.5061
0.5017	17.0804	4300	0.5107
0.5066	17.4777	4400	0.5074
0.5071	17.8749	4500	0.5088
0.5119	18.2721	4600	0.5053
0.5032	18.6693	4700	0.5064
0.5014	19.0665	4800	0.5089
0.5001	19.4638	4900	0.5078
0.4926	19.8610	5000	0.5066

Framework versions

• Transformers 4.44.2
• Pytorch 2.3.0+cu118
• Datasets 3.0.0
• Tokenizers 0.19.1