Update app.py

app.py

@@ -1,109 +1,56 @@
-import streamlit as st
-import base64
-import io
-from huggingface_hub import InferenceClient
-from gtts import gTTS
-from audiorecorder import audiorecorder
+import webrtcvad
 import speech_recognition as sr
-from pydub import AudioSegment
 
-pre_prompt_text = "Eres una IA conductual, tus respuestas deberán ser breves, estóicas y humanistas."
-
-if "history" not in st.session_state:
-    st.session_state.history = []
-
-if "pre_prompt_sent" not in st.session_state:
-    st.session_state.pre_prompt_sent = False
-
-def recognize_speech(audio_data, show_messages=True):
-    recognizer = sr.Recognizer()
-    audio_recording = sr.AudioFile(audio_data)
-
-    with audio_recording as source:
-        audio = recognizer.record(source)
-
-    try:
-        audio_text = recognizer.recognize_google(audio, language="es-ES")
-        if show_messages:
-            st.subheader("Texto Reconocido:")
-            st.write(audio_text)
-            st.success("Reconocimiento de voz completado.")
-    except sr.UnknownValueError:
-        st.warning("No se pudo reconocer el audio. ¿Intentaste grabar algo?")
-        audio_text = ""
-    except sr.RequestError:
-        st.error("Hablame para comenzar!")
-        audio_text = ""
-
-    return audio_text
-
-def format_prompt(message, history):
-    prompt = "<s>"
-
-    if not st.session_state.pre_prompt_sent:
-        prompt += f"[INST] {pre_prompt_text} [/INST]"
-        st.session_state.pre_prompt_sent = True
-
-    for user_prompt, bot_response in history:
-        prompt += f"[INST] {user_prompt} [/INST]"
-        prompt += f" {bot_response}</s> "
-
-    prompt += f"[INST] {message} [/INST]"
-    return prompt
-
-def generate(audio_text, history, temperature=None, max_new_tokens=512, top_p=0.95, repetition_penalty=1.0):
-    client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
-
-    temperature = float(temperature) if temperature is not None else 0.9
-    temperature = max(temperature, 1e-2)
-    top_p = float(top_p)
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42)
-
-    formatted_prompt = format_prompt(audio_text, history)
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
-    response = ""
-
-    for response_token in stream:
-        response += response_token.token.text
-    
-    response = ' '.join(response.split()).replace('</s>', '')
-    audio_file = text_to_speech(response, speed=1.3)
-    return response, audio_file
-
-def text_to_speech(text, speed=1.3):
-    tts = gTTS(text=text, lang='es')
-    audio_fp = io.BytesIO()
-    tts.write_to_fp(audio_fp)
-    audio_fp.seek(0)
-    audio = AudioSegment.from_file(audio_fp, format="mp3")
-    modified_speed_audio = audio.speedup(playback_speed=speed)
-    modified_audio_fp = io.BytesIO()
-    modified_speed_audio.export(modified_audio_fp, format="mp3")
-    modified_audio_fp.seek(0)
-    return modified_audio_fp
-
-def main():
-    audio_data = audiorecorder("Presiona para hablar", "Deteniendo la grabación...")
-
-    if not audio_data.empty():
-        st.audio(audio_data.export().read(), format="audio/wav")
-        audio_data.export("audio.wav", format="wav")
-        audio_text = recognize_speech("audio.wav")
-
-        if audio_text:
-            output, audio_file = generate(audio_text, history=st.session_state.history)  
-
-            if audio_file is not None:
-                st.markdown(
-                    f"""<audio autoplay="autoplay" controls="controls" src="data:audio/mp3;base64,{base64.b64encode(audio_file.read()).decode()}" type="audio/mp3" id="audio_player"></audio>""",
-                    unsafe_allow_html=True)
-
-if __name__ == "__main__":
-    main()
+# Configuramos la tasa de muestreo y el tamaño del frame
+sample_rate = 16000
+frame_size = 30
+
+# Creamos un objeto VAD y un reconocedor de voz
+vad = webrtcvad.Vad(aggressiveness=3)
+recognizer = sr.Recognizer()
+
+# Leemos un archivo de audio
+with open("audio.wav", "rb") as f:
+    audio_data = f.read()
+
+# Indicadores de estado
+vad_active = False
+speech_detected = False
+phrase = ""
+
+# Procesamos el audio en frames
+for i in range(0, len(audio_data), frame_size):
+    # Obtenemos el frame actual
+    frame = audio_data[i:i+frame_size]
+
+    # Detectamos si hay voz en el frame
+    is_speech = vad.is_speech(frame, sample_rate)
+
+    # Actualizamos los indicadores de estado
+    if is_speech and not vad_active:
+        vad_active = True
+        speech_detected = True
+        print("️ Detección de voz iniciada")
+    elif not is_speech and vad_active:
+        vad_active = False
+        print("⏹️ Detección de voz finalizada")
+        
+    # Si se ha detectado voz y hay un silencio, transcribimos la frase
+    if speech_detected and not is_speech:
+        # Transcribimos la frase
+        with sr.AudioData(frame, sample_rate) as source:
+            audio = recognizer.record(source)
+            try:
+                text = recognizer.recognize_google(audio)
+                phrase += f" {text}"
+                print(f"️ {text}")
+            except sr.RequestError:
+                print("⚠️ Error al transcribir la frase")
+            except sr.UnknownValueError:
+                print("⚠️ No se ha reconocido la frase")
+        
+        # Reiniciamos el indicador de frase
+        speech_detected = False
+
+# Imprimimos la frase completa
+print(f"Transcripción completa: {phrase}")