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import torch
import numpy as np
from transformers import pipeline, SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import gradio as gr
# Configuración del dispositivo (GPU si está disponible)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Pipeline de traducción automática de voz
pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small", device=0 if torch.cuda.is_available() else -1)
def translate(audio):
outputs = pipe(audio, generate_kwargs={"task": "translate", "max_new_tokens": 256})
return outputs["text"]
# Modelos para síntesis de voz
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
model.to(device)
vocoder.to(device)
# Embedding del hablante
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[6000]["xvector"]).unsqueeze(0)
def synthesise(text):
inputs = processor(text=text, return_tensors="pt")
speech = model.generate_speech(
inputs["input_ids"].to(device),
speaker_embeddings.to(device),
vocoder=vocoder
)
return speech.cpu()
# Conversión final
target_dtype = np.int16
max_range = np.iinfo(target_dtype).max
def speech_to_speech_translation(audio):
translated_text = translate(audio)
synthesised_speech = synthesise(translated_text)
synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
return 16000, synthesised_speech
# Interfaz Gradio
demo = gr.Interface(
fn=speech_to_speech_translation,
inputs=gr.Audio(sources="microphone", type="filepath"),
outputs=gr.Audio(label="Generated Speech", type="numpy"),
)
demo.launch(debug=True)
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