audio_model.py

from transformers import AutoProcessor, BlipForConditionalGeneration, AutoTokenizer,SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
import librosa
import numpy as np
import torch

#CONSTANTS
speaker_embeddings = {
    "BDL": "spkemb/cmu_us_bdl_arctic-wav-arctic_a0009.npy",
    "CLB": "spkemb/cmu_us_clb_arctic-wav-arctic_a0144.npy",
    "RMS": "spkemb/cmu_us_rms_arctic-wav-arctic_b0353.npy",
    "SLT": "spkemb/cmu_us_slt_arctic-wav-arctic_a0508.npy",
}

# 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")

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

### TEXT TO AUDIO SPEECH  MODEL 2    
# Define la función que convierte texto en voz
def text_to_speech(text,speaker):
    # Genera el audio utilizando el modelo 
    if len(text.strip()) == 0:
       return (16000, np.zeros(0).astype(np.int16))
    inputs = processor(text=text, return_tensors="pt")

    # limit input length
    input_ids = inputs["input_ids"]
    input_ids = input_ids[..., :model.config.max_text_positions]

    if speaker == "Surprise Me!":
        # load one of the provided speaker embeddings at random
        idx = np.random.randint(len(speaker_embeddings))
        key = list(speaker_embeddings.keys())[idx]
        speaker_embedding = np.load(speaker_embeddings[key])

        # randomly shuffle the elements
        np.random.shuffle(speaker_embedding)

        # randomly flip half the values
        x = (np.random.rand(512) >= 0.5) * 1.0
        x[x == 0] = -1.0
        speaker_embedding *= x

        #speaker_embedding = np.random.rand(512).astype(np.float32) * 0.3 - 0.15
    speaker_embedding = torch.tensor(speaker_embedding).unsqueeze(0)

    speech = model.generate_speech(input_ids, speaker_embedding, vocoder=vocoder)

    speech = (speech.numpy() * 32767).astype(np.int16)
    return (16000, speech)
### END TEXT TO AUDIO SPEECH  MODEL 2