Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+from gtts import gTTS
+import numpy as np
+import sounddevice as sd
+
+class VoiceRecognition:
+    def __init__(self):
+        self.processor = Wav2Vec2Processor.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
+        self.model = Wav2Vec2ForCTC.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")  
+        self.sample_rate = 16000
+
+    def listen(self):
+        st.write("Escuchando...")
+        audio_data = sd.rec(int(self.sample_rate * 5), samplerate=self.sample_rate, channels=1, dtype='float32')
+        sd.wait()
+        st.write("Grabación terminada.")
+        return audio_data.flatten()
+
+    def vad(self, audio):
+        threshold = 0.02
+        return audio[np.abs(audio) > threshold]
+
+    def transcribe(self, audio):
+        input_values = self.processor(audio, return_tensors="pt", sampling_rate=self.sample_rate).input_values
+        with torch.no_grad():
+            logits = self.model(input_values).logits
+        predicted_ids = torch.argmax(logits, dim=-1)
+        return self.processor.decode(predicted_ids[0])
+
+    def text_to_speech(self, text):
+        tts = gTTS(text=text, lang='es')
+        output_path = "response.mp3"
+        tts.save(output_path)
+        return output_path
+
+def main():
+    st.title("Asistente de Voz - Reconocimiento de Voz")
+    recognizer = VoiceRecognition()
+    
+    if st.button("Iniciar Grabación"):
+        audio = recognizer.listen()
+        audio_vad = recognizer.vad(audio)
+        
+        if audio_vad.size > 0:
+            transcription = recognizer.transcribe(audio_vad)
+            st.write(f"Texto transcrito: {transcription}")
+            audio_path = recognizer.text_to_speech(transcription)
+            st.audio(audio_path)
+        else:
+            st.write("No se detectó actividad de voz.")
+
+if __name__ == "__main__":
+    main()