speecht5_finetuned_bu_tts_cy_en

This model is a fine-tuned version of microsoft/speecht5_tts on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.4377

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: 32
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• 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: 50000
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.5446	2.0409	1000	0.5117
0.526	4.0818	2000	0.4910
0.5234	6.1226	3000	0.4768
0.5138	8.1635	4000	0.4711
0.497	10.2044	5000	0.4651
0.495	12.2453	6000	0.4622
0.4921	14.2862	7000	0.4596
0.4881	16.3270	8000	0.4563
0.4894	18.3679	9000	0.4561
0.4874	20.4088	10000	0.4528
0.483	22.4497	11000	0.4510
0.4802	24.4905	12000	0.4491
0.4815	26.5314	13000	0.4484
0.4763	28.5723	14000	0.4465
0.4776	30.6132	15000	0.4459
0.4744	32.6541	16000	0.4459
0.4735	34.6949	17000	0.4458
0.4721	36.7358	18000	0.4434
0.4706	38.7767	19000	0.4452
0.4677	40.8176	20000	0.4426
0.4684	42.8585	21000	0.4424
0.4739	44.8993	22000	0.4427
0.4683	46.9402	23000	0.4413
0.4678	48.9811	24000	0.4423
0.4532	51.0204	25000	0.4405
0.4653	53.0613	26000	0.4429
0.4653	55.1022	27000	0.4404
0.4625	57.1431	28000	0.4399
0.4613	59.1840	29000	0.4401
0.4636	61.2248	30000	0.4402
0.4634	63.2657	31000	0.4392
0.4669	65.3066	32000	0.4384
0.4628	67.3475	33000	0.4393
0.4627	69.3883	34000	0.4389
0.4618	71.4292	35000	0.4380
0.4616	73.4701	36000	0.4394
0.4658	75.5110	37000	0.4390
0.4625	77.5519	38000	0.4374
0.4613	79.5927	39000	0.4374
0.4587	81.6336	40000	0.4384
0.4625	83.6745	41000	0.4400
0.4614	85.7154	42000	0.4386
0.4582	87.7563	43000	0.4371
0.464	89.7971	44000	0.4381
0.4666	91.8380	45000	0.4385
0.4634	93.8789	46000	0.4379
0.4657	95.9198	47000	0.4370
0.4618	97.9607	48000	0.4370
0.4469	100.0	49000	0.4373
0.4475	102.0409	50000	0.4377

Framework versions

• Transformers 4.50.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1