import streamlit as st import base64 import io from huggingface_hub import InferenceClient from gtts import gTTS from audio_recorder_streamlit import audio_recorder import speech_recognition as sr from pydub import AudioSegment pre_prompt_text = "Hablarás español, tus principios el estoicismo, eres una IA conductual, tus respuestas serán breves." 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("Háblame para comenzar!") audio_text = "" return audio_text def format_prompt(message, history): prompt = "" 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} " 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 if temperature < 1e-2: 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('', '') 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_mp3(audio_fp) 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 audio_play(audio_fp): st.audio(audio_fp.read(), format="audio/mp3", start_time=0) def display_recognition_result(audio_text, output, audio_file): if audio_text: st.session_state.history.append((audio_text, output)) if audio_file is not None: st.markdown( f"""""", unsafe_allow_html=True) def main(): if not st.session_state.pre_prompt_sent: st.session_state.pre_prompt_sent = True audio_bytes = audio_recorder(text="", recording_color="#b81414", neutral_color="#808080",) if audio_bytes: st.audio(audio_bytes, format="audio/wav") audio_data = io.BytesIO(audio_bytes) audio_data.seek(0) audio_text = recognize_speech(audio_data) if audio_text: output, audio_file = generate(audio_text, history=st.session_state.history) display_recognition_result(audio_text, output, audio_file) if __name__ == "__main__": main()