Update app.py

app.py

@@ -273,36 +273,15 @@ print(summarized_text)
    # number_of_sentences-=1
     #print(summarized_text_list_list)
     #print(number_of_sentences)
-#text to speech
-#!pip install git+https://github.com/huggingface/transformers.git
-#!pip install datasets sentencepiece
-import torch
-#import soundfile as sf
-#from IPython.display import Audio
-from datasets import load_dataset
-from transformers import pipeline
+# Step 2: Text to Speech (TTS)
 from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
+
 processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
 model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
-#text = "The future belongs to those who believe in the beauty of their dreams."
-#text = (summarized_text_list_list)
-text = (summarized_text)
-
-#inputs = processor(text=summarized_text_list_list, return_tensors="pt")
-#inputs = processor("Michelletest", return_tensors="pt")
-inputs = processor(text, return_tensors="pt")
-from datasets import load_dataset
-embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
 
-import torch
-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")
-with torch.no_grad():
-    speech = vocoder(spectrogram)
-speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
-Audio(speech, rate=16000)
+# Convert summarized text to speech
+inputs = processor(summarized_text, return_tensors="pt")
+spectrogram = model.generate_speech(inputs["input_ids"])