End of training

README.md

@@ -1,10 +1,10 @@
 ---
-base_model: microsoft/speecht5_tts
-datasets:
-- voxpopuli
 license: mit
+base_model: microsoft/speecht5_tts
 tags:
 - generated_from_trainer
+datasets:
+- voxpopuli
 model-index:
 - name: speecht5_tts_voxpopuli_it_v2
  results: []
@@ -17,7 +17,7 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6262
+- Loss: 0.4484
 
 ## Model description
 
@@ -36,37 +36,47 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1e-06
-- train_batch_size: 16
-- eval_batch_size: 8
+- learning_rate: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 4
 - seed: 42
-- gradient_accumulation_steps: 2
+- gradient_accumulation_steps: 4
 - 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: 1000
+- lr_scheduler_warmup_steps: 200
+- training_steps: 2000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:------:|:----:|:---------------:|
-| 0.7564 | 0.6116 | 100 | 0.7051 |
-| 0.7636 | 1.2232 | 200 | 0.6764 |
-| 0.7263 | 1.8349 | 300 | 0.6662 |
-| 0.7262 | 2.4465 | 400 | 0.6555 |
-| 0.7038 | 3.0581 | 500 | 0.6483 |
-| 0.6931 | 3.6697 | 600 | 0.6422 |
-| 0.6881 | 4.2813 | 700 | 0.6344 |
-| 0.6916 | 4.8930 | 800 | 0.6300 |
-| 0.6814 | 5.5046 | 900 | 0.6286 |
-| 0.6692 | 6.1162 | 1000 | 0.6262 |
+| 0.5707 | 0.2358 | 100 | 0.5183 |
+| 0.5452 | 0.4717 | 200 | 0.5096 |
+| 0.5313 | 0.7075 | 300 | 0.4890 |
+| 0.5229 | 0.9434 | 400 | 0.4807 |
+| 0.5119 | 1.1792 | 500 | 0.4802 |
+| 0.5121 | 1.4151 | 600 | 0.4681 |
+| 0.5037 | 1.6509 | 700 | 0.4719 |
+| 0.4996 | 1.8868 | 800 | 0.4691 |
+| 0.4931 | 2.1226 | 900 | 0.4621 |
+| 0.4903 | 2.3585 | 1000 | 0.4620 |
+| 0.4949 | 2.5943 | 1100 | 0.4573 |
+| 0.4853 | 2.8302 | 1200 | 0.4579 |
+| 0.4826 | 3.0660 | 1300 | 0.4547 |
+| 0.4827 | 3.3019 | 1400 | 0.4535 |
+| 0.4835 | 3.5377 | 1500 | 0.4523 |
+| 0.4802 | 3.7736 | 1600 | 0.4514 |
+| 0.4777 | 4.0094 | 1700 | 0.4503 |
+| 0.4792 | 4.2453 | 1800 | 0.4499 |
+| 0.4779 | 4.4811 | 1900 | 0.4491 |
+| 0.4755 | 4.7170 | 2000 | 0.4484 |
 
 
 ### Framework versions
 
 - Transformers 4.43.1
-- Pytorch 2.4.1+cu121
-- Datasets 3.0.0
+- Pytorch 2.2.0
+- Datasets 3.0.1
 - Tokenizers 0.19.1