from dataclasses import dataclass from typing import List, Tuple, Dict import os import re import httpx import json from openai import OpenAI import edge_tts import tempfile from pydub import AudioSegment import base64 from pathlib import Path import shutil # Importamos shutil para manejo de directorios @dataclass class ConversationConfig: max_words: int = 3000 prefix_url: str = "https://r.jina.ai/" model_name: str = "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo" class URLToAudioConverter: def __init__(self, config: ConversationConfig, llm_api_key: str): self.config = config self.llm_client = OpenAI(api_key=llm_api_key, base_url="https://api.together.xyz/v1") self.llm_out = None def fetch_text(self, url: str) -> str: if not url: raise ValueError("URL cannot be empty") full_url = f"{self.config.prefix_url}{url}" try: response = httpx.get(full_url, timeout=60.0) response.raise_for_status() return response.text except httpx.HTTPError as e: raise RuntimeError(f"Failed to fetch URL: {e}") def extract_conversation(self, text: str) -> Dict: if not text: raise ValueError("Input text cannot be empty") try: # Prompt mejorado para obtener JSON consistente prompt = ( f"{text}\nConvert the provided text into a short informative podcast conversation " f"between two experts. Return ONLY a JSON object with the following structure:\n" '{"conversation": [{"speaker": "Speaker1", "text": "..."}, {"speaker": "Speaker2", "text": "..."}]}' ) chat_completion = self.llm_client.chat.completions.create( messages=[{"role": "user", "content": prompt}], model=self.config.model_name, response_format={"type": "json_object"} # Fuerza formato JSON ) # Extracción robusta de JSON response_content = chat_completion.choices[0].message.content json_str = response_content.strip() # Limpieza de texto alrededor del JSON if not json_str.startswith('{'): start = json_str.find('{') if start != -1: json_str = json_str[start:] if not json_str.endswith('}'): end = json_str.rfind('}') if end != -1: json_str = json_str[:end+1] return json.loads(json_str) except Exception as e: # Debug: Imprime la respuesta del modelo para diagnóstico print(f"Error en extract_conversation: {str(e)}") print(f"Respuesta del modelo: {response_content}") raise RuntimeError(f"Failed to extract conversation: {str(e)}") async def text_to_speech(self, conversation_json: Dict, voice_1: str, voice_2: str) -> Tuple[List[str], str]: output_dir = Path(self._create_output_directory()) filenames = [] try: for i, turn in enumerate(conversation_json["conversation"]): filename = output_dir / f"output_{i}.mp3" voice = voice_1 if i % 2 == 0 else voice_2 tmp_path, error = await self._generate_audio(turn["text"], voice) if error: raise RuntimeError(f"Text-to-speech failed: {error}") os.rename(tmp_path, filename) filenames.append(str(filename)) return filenames, str(output_dir) except Exception as e: raise RuntimeError(f"Failed to convert text to speech: {e}") async def _generate_audio(self, text: str, voice: str, rate: int = 0, pitch: int = 0) -> Tuple[str, str]: if not text.strip(): return None, "Text cannot be empty" if not voice: return None, "Voice cannot be empty" voice_short_name = voice.split(" - ")[0] rate_str = f"{rate:+d}%" pitch_str = f"{pitch:+d}Hz" communicate = edge_tts.Communicate(text, voice_short_name, rate=rate_str, pitch=pitch_str) with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file: tmp_path = tmp_file.name await communicate.save(tmp_path) return tmp_path, None def _create_output_directory(self) -> str: random_bytes = os.urandom(8) folder_name = base64.urlsafe_b64encode(random_bytes).decode("utf-8") os.makedirs(folder_name, exist_ok=True) return folder_name def combine_audio_files(self, filenames: List[str], output_file: str) -> None: if not filenames: raise ValueError("No input files provided") try: combined = AudioSegment.empty() for filename in filenames: audio_segment = AudioSegment.from_file(filename, format="mp3") combined += audio_segment combined.export(output_file, format="mp3") # Limpieza mejorada y robusta dir_path = os.path.dirname(filenames[0]) # Eliminar todos los archivos en el directorio for file in os.listdir(dir_path): file_path = os.path.join(dir_path, file) if os.path.isfile(file_path): try: os.remove(file_path) except Exception as e: print(f"Warning: Could not remove file {file_path}: {str(e)}") # Intentar eliminar el directorio (no crítico si falla) try: os.rmdir(dir_path) except OSError as e: print(f"Info: Could not remove directory {dir_path}: {str(e)}") # No es crítico, el espacio puede continuar except Exception as e: raise RuntimeError(f"Failed to combine audio files: {e}") async def url_to_audio(self, url: str, voice_1: str, voice_2: str) -> Tuple[str, str]: text = self.fetch_text(url) words = text.split() if len(words) > self.config.max_words: text = " ".join(words[:self.config.max_words]) conversation_json = self.extract_conversation(text) conversation_text = "\n".join( f"{turn['speaker']}: {turn['text']}" for turn in conversation_json["conversation"] ) self.llm_out = conversation_json audio_files, folder_name = await self.text_to_speech( conversation_json, voice_1, voice_2 ) final_output = os.path.join(folder_name, "combined_output.mp3") self.combine_audio_files(audio_files, final_output) return final_output, conversation_text async def text_to_audio(self, text: str, voice_1: str, voice_2: str) -> Tuple[str, str]: """Método para procesar texto directo""" conversation_json = self.extract_conversation(text) conversation_text = "\n".join( f"{turn['speaker']}: {turn['text']}" for turn in conversation_json["conversation"] ) audio_files, folder_name = await self.text_to_speech( conversation_json, voice_1, voice_2 ) final_output = os.path.join(folder_name, "combined_output.mp3") self.combine_audio_files(audio_files, final_output) return final_output, conversation_text