Create README.md

README.md

@@ -1,27 +1,35 @@
 ---
-dataset_info:
-  features:
-  - name: audio_id
-    dtype: string
-  - name: language
-    dtype: string
-  - name: audio
-    dtype:
-      audio:
-        sampling_rate: 16000
-  - name: text
-    dtype: string
-  - name: gender
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 20994806204.918
-    num_examples: 17798
-  download_size: 20944633904
-  dataset_size: 20994806204.918
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: apache-2.0
+language:
+- hi
+size_categories:
+- 10K<n<100K
 ---
+# 🗣️ Hindi Text-to-Speech (TTS) Model
+
+This repository hosts a Hindi TTS model trained on a custom Hindi dataset. The model converts Hindi text into natural-sounding speech and is suitable for various applications such as voice assistants, audiobooks, and accessibility tools.
+
+## 📌 Model Details
+
+- **Model Type**: [Insert TTS architecture, e.g., Tacotron2 + HiFi-GAN / VITS]
+- **Language**: Hindi (`hi`)
+- **Dataset**: Custom Hindi speech dataset (uploaded by the author)
+- **Sampling Rate**: 22050 Hz (or specify your sample rate)
+- **License**: [Apache License 2.0 x, e.g., MIT, CC-BY-4.0, etc.]
+
+## 🏁 Usage
+
+### Inference using `transformers` + `datasets` (if applicable)
+
+```python
+from transformers import pipeline
+
+# Load the model
+tts = pipeline("text-to-speech", model="Saurabh4509/TSS_Hindi_Data")
+
+# Generate speech
+output = tts("नमस्ते, आप कैसे हैं?")
+
+# Save to file
+with open("output.wav", "wb") as f:
+    f.write(output["audio"])