audio_model.py

from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
import librosa
import numpy as np
import torch
from datasets import load_dataset

# Carga el modelo de clasificación de tetxo a audio speech
checkpoint = "microsoft/speecht5_tts"
processor = SpeechT5Processor.from_pretrained(checkpoint)
model = SpeechT5ForTextToSpeech.from_pretrained(checkpoint)
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7440]["xvector"]).unsqueeze(0)

device = "cuda" if torch.cuda.is_available() else "cpu"

replacements = [
    ("á", "a"),
    ("í", "i"),
    ("ñ", "n"),
    ("ó", "o"),
    ("ú", "u"),
    ("ü", "u"),
]

def cleanup_text(text):
    for src, dst in replacements:
        text = text.replace(src, dst)
    return text
    
### TEXT TO AUDIO SPEECH  MODEL 2    
# Define la función que convierte texto en voz
def synthesize_speech(text):
    text = cleanup_text(text)
    inputs = processor(text=text, return_tensors="pt")

    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
    
    return speech
### END TEXT TO AUDIO SPEECH  MODEL 2