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import streamlit as st
import torch
import numpy as np
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import pyaudio
import sounddevice as sd
from TTS.api import TTS
class VoiceAssistant:
def __init__(self):
# Cargar modelo Wav2Vec2 para reconocimiento de voz en espa帽ol
self.processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-xlsr-53-spanish")
self.model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-xlsr-53-spanish")
# Cargar modelo TTS para s铆ntesis de voz en espa帽ol (modelo corregido)
self.tts_model = TTS(model_name="microsoft/speecht5_tts", progress_bar=False)
# Par谩metros de audio
self.sample_rate = 16000
self.chunk_size = 480
self.p = pyaudio.PyAudio()
self.stream = self.p.open(format=pyaudio.paFloat32, channels=1, rate=self.sample_rate, input=True, frames_per_buffer=self.chunk_size)
# Palabras clave
self.keyword_activation = "jarvis"
self.keyword_deactivation = "detente"
# Estado de escucha
self.listening = False
def vad_collector(self, vad_threshold=0.5):
audio_chunks, keyword_detected = [], False
while self.listening:
data = self.stream.read(self.chunk_size)
audio_chunk = np.frombuffer(data, dtype=np.float32)
# Detectar palabra de activaci贸n
if self.keyword_activation.lower() in str(audio_chunk).lower():
keyword_detected = True
break
# Detectar palabra de desactivaci贸n
if self.keyword_deactivation.lower() in str(audio_chunk).lower():
self.listening = False
break
audio_chunks.append(audio_chunk)
return audio_chunks, keyword_detected
def transcribe_audio(self, audio_chunks):
audio_data = np.concatenate(audio_chunks)
# Procesar y transcribir el audio usando Wav2Vec2
input_values = self.processor(audio_data, return_tensors="pt", sampling_rate=self.sample_rate).input_values
with torch.no_grad():
logits = self.model(input_values).logits
# Decodificar la transcripci贸n
predicted_ids = torch.argmax(logits, dim=-1)
transcription = self.processor.decode(predicted_ids[0])
return transcription
def generate_response(self, text):
return "Respuesta generada para: " + text
def text_to_speech(self, text):
output_path = "response.wav"
self.tts_model.tts_to_file(text=text, file_path=output_path)
return output_path
def run(self):
st.title("Asistente de Voz JARVIS")
# Bot贸n para iniciar/desactivar la escucha
if st.button("Iniciar/Detener Escucha"):
self.listening = not self.listening
st.write("Escucha activada." if self.listening else "Escucha desactivada.")
# Realizar la transcripci贸n y s铆ntesis de voz si la escucha est谩 activada
if self.listening:
audio_chunks, keyword_detected = self.vad_collector()
if keyword_detected:
st.success("Palabra clave 'JARVIS' detectada. Procesando...")
transcribed_text = self.transcribe_audio(audio_chunks)
st.write(f"Texto transcrito: {transcribed_text}")
response = self.generate_response(transcribed_text)
st.write(f"Respuesta: {response}")
audio_path = self.text_to_speech(response)
st.audio(audio_path)
def main():
assistant = VoiceAssistant()
assistant.run()
if __name__ == "__main__":
main()
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