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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 clasificaci贸n 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 funci贸n 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 |