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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 |