Create app.py

app.py

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+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+import torch
+import soundfile as sf
+from datasets import load_dataset
+from transformers import pipeline
+import gradio as gr
+import tempfile
+
+
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+speech = pipeline("text-to-speech", model="microsoft/speecht5_tts")     
+
+# code from the Model card
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+speaker_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(speaker_dataset[0]["xvector"]).unsqueeze(0)
+
+def summarize_text_and_speak(prompt):
+    summary = summarizer(prompt, max_length=150, min_length=30, do_sample=False)
+    summary_text = summary[0]['summary_text']
+    
+    #inputs = processor(text="Hello, my dog is cute.", return_tensors="pt")
+    inputs = processor(text=summary_text, return_tensors="pt")
+    #speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+    speech_audio  = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+    
+    #sf.write("speech.wav", speech.numpy(), samplerate=16000)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+        sf.write(tmp_file.name, speech_audio.numpy(), samplerate=16000)
+        audio_path = tmp_file.name
+        
+    return summary_text, audio_path
+    
+interface = gr.Interface(
+    fn=summarize_text_and_speak,
+    inputs=gr.Textbox(lines=10, label="Input text"),
+    outputs=[gr.Textbox(label="Summary"), gr.Audio(label="Audio")]
+)
+
+interface.launch(share=True)