End of training

README.md

@@ -1,42 +1,24 @@
 ---
 library_name: transformers
-language:
-- ar
-license: apache-2.0
-base_model: openai/whisper-small
+license: mit
+base_model: microsoft/speecht5_tts
 tags:
 - generated_from_trainer
 datasets:
-- mozilla-foundation/common_voice_13_0
-metrics:
-- wer
+- common_voice_13_0
 model-index:
-- name: 'arabic Whisper Small '
-  results:
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice 13.0
-      type: mozilla-foundation/common_voice_13_0
-      config: ar
-      split: test
-      args: 'config: ar, split: test'
-    metrics:
-    - name: Wer
-      type: wer
-      value: 44.37620667463455
+- name: ABG_TTS
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# arabic Whisper Small 
+# ABG_TTS
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13.0 dataset.
+This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the common_voice_13_0 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3384
-- Wer: 44.3762
+- Loss: 0.5066
 
 ## Model description
 
@@ -55,24 +37,72 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 8
+- 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: 500
-- training_steps: 4000
+- lr_scheduler_warmup_steps: 100
+- training_steps: 5000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Wer     |
-|:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.3475        | 0.4148 | 1000 | 0.4130          | 52.2957 |
-| 0.2522        | 0.8295 | 2000 | 0.3674          | 49.2011 |
-| 0.1608        | 1.2443 | 3000 | 0.3475          | 44.8819 |
-| 0.161         | 1.6591 | 4000 | 0.3384          | 44.3762 |
+| 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

generation_config.json

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   "transformers_version": "4.44.2"
 }

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