Update README.md

README.md

@@ -16,45 +16,66 @@ model-index:
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-# SpeechT5 TTS technical train2 
+# 🎤 SpeechT5 TTS Technical Train2
 
-This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the custom dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3763
+This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) using a custom dataset, specifically trained for *Text-to-Speech (TTS)* tasks. 
+
+🎯 *Key Metric:*
+- *Loss* on the evaluation set: 0.3763
+
+📢 *Listen to the generated sample:*
+
+  The text is " Hello ,few technical terms i used while fine tuning are  API and REST and CUDA and TTS."
 
-SAMPLE TEXT : "hello ,few technical terms i used while fine tuning are  API and REST and CUDA and TTS."
 <audio controls src="https://cdn-uploads.huggingface.co/production/uploads/66f64964584cae45b5494560/JYJmDNPHnBRLuvqGTJQSu.wav"></audio>
 
-  
+---
+
+## 📝 Model Description
 
-## Model description
+The *SpeechT5 TTS Technical Train2* is built on the *SpeechT5* architecture and was fine-tuned for speech synthesis (TTS). The fine-tuning focused on improving the naturalness and clarity of the generated audio from text.
 
-More information needed
+🛠 *Base Model*: [Microsoft SpeechT5](https://huggingface.co/microsoft/speecht5_tts)  
+📚 *Dataset*: Custom (specific details to be provided)
 
-## Intended uses & limitations
+---
+
+## 🔧 Intended Uses & Limitations
+
+### ✅ *Primary Use Cases:*
+- *Text-to-Speech (TTS)* for technical Interview Texts .
+- *Virtual Assistants*:
+  
 
-More information needed
+### ⚠ *Limitations:*
+- Best suited for English TTS tasks.
+- Require further fine-tuning on Large dataset  .
 
-## Training and evaluation data
+---
 
-More information needed
+## 📅 Training Data
 
-## Training procedure
+The model was fine-tuned on a *custom dataset*, curated for enhancing TTS outputs. This dataset consists of various types of text that help the model generate more natural speech, making it suitable for TTS applications.
 
-### Training hyperparameters
+---
 
-The following hyperparameters were used during training:
-- learning_rate: 1e-05
-- train_batch_size: 16
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 32
-- 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: 50
-- training_steps: 500
-- mixed_precision_training: Native AMP
+## ⚙ Training Procedure
+
+### ⚙ *Hyperparameters*:
+
+The model was trained with the following hyperparameters:
+```yaml
+learning_rate: 1e-05
+train_batch_size: 16
+eval_batch_size: 8
+seed: 42
+gradient_accumulation_steps: 2
+total_train_batch_size: 32
+optimizer: adamw_torch (betas=(0.9, 0.999), epsilon=1e-08)
+lr_scheduler_type: linear
+lr_scheduler_warmup_steps: 50
+training_steps: 500
+mixed_precision_training: Native AMP
 
 ### Training results