from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
import torch
import soundfile as sf
from datasets import load_dataset
import IPython.display as ipd

# Load processor, model and vocoder
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")

# Get speaker embeddings from a dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)

# Text to synthesize
texts = [
    "Welcome to Hugging Face! This is a demonstration of text to speech synthesis.",
    "Artificial intelligence is transforming how we interact with technology.",
    "Machine learning models can now generate realistic human speech."
]

# Synthesize speech for each text
for i, text in enumerate(texts):
    # Process text
    inputs = processor(text=text, return_tensors="pt")
    
    # Generate speech
    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
    
    # Save audio
    output_file = f"synthesized_speech_{i+1}.wav"
    sf.write(output_file, speech.numpy(), samplerate=16000)
    
    print(f"Generated speech for: '{text}'")
    # In a notebook, you could play the audio with:
    # ipd.display(ipd.Audio(output_file))
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