Create app.py

app.py

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+# Import necessary libraries and modules
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
+from datasets import load_dataset
+import torch
+from IPython.display import Audio
+
+# Load the processor and model for text-to-speech
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+
+# Prepare the input text
+text = "Don't count the days, make the days count."
+inputs = processor(text=text, return_tensors="pt")
+
+# Load the speaker embeddings dataset and select a specific speaker
+embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
+speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
+
+# Generate the spectrogram for the speech
+spectrogram = model.generate_speech(inputs["input_ids"], speaker_embeddings)
+
+# Load the vocoder model to convert the spectrogram to speech waveform
+vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
+speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
+
+# Play the generated speech
+Audio(speech, rate=16000)