Update README.md

README.md

@@ -20,17 +20,53 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-More information needed
+UZBTTS - bu asason 250 MB Text2Audio datasetga (microsoft/speecht5_tts) modeliga fine-tuned qilindi, natija datasetga yarasha yaxshi.
 
-## Intended uses & limitations
+Agar siz buni modelni foydalanishini xoxlasangiz.
 
-More information needed
+example:
+```
+   #dastlab run qiling :
+    !pip install transformers datasets
 
-## Training and evaluation data
+    from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
 
-More information needed
+    processor = SpeechT5Processor.from_pretrained("ai-nightcoder/UZBTTS")
+    model = SpeechT5ForTextToSpeech.from_pretrained("ai-nightcoder/UZBTTS")
+
+    # ***************************************************************************
+    text = "O‘zbekistonda import qilingan sovitkich,
+           muzlatkich va konditsionerlarni energosamaradorlik bo‘yicha sinovdan o‘tkazish boshlandi.
+           Kun.uz'ga murojaat qilgan importchi tadbirkorlarga ko‘ra, bu yangilik ham vaqt,
+           ham naqd nuqtayi nazaridan yangi xarajatlarga olib kelgan.
+           Kelgusida bunday tekshiruv boshqa turdagi maishiy texnikalarga ham joriy etilishi kutilyapti."
+
+    inputs = processor(text=text, return_tensors="pt")
+
+    # ***************************************************************************
+
+    from datasets import load_dataset
+
+    embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+
+    import torch
+    # voice clone uchun ham ishlatilsa bo'ladi.
+    speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+    spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
+
+    from transformers import SpeechT5HifiGan
+
+    vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+
+    # ****************************************************************************
+
+    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+
+    from IPython.display import Audio
+
+    Audio(speech, rate=16000)
 
-## Training procedure
 
 ### Training hyperparameters