mirror of
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287 lines
7.9 KiB
Python
287 lines
7.9 KiB
Python
from typing import ClassVar, List, Optional
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from loguru import logger
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from podcastfy.client import generate_podcast
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from pydantic import Field, field_validator, model_validator
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from open_notebook.config import DATA_FOLDER
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from open_notebook.domain.notebook import ObjectModel
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class PodcastEpisode(ObjectModel):
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table_name: ClassVar[str] = "podcast_episode"
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name: str
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template: str
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instructions: str
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text: str
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audio_file: str
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class PodcastConfig(ObjectModel):
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table_name: ClassVar[str] = "podcast_config"
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name: str
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podcast_name: str
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podcast_tagline: str
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output_language: str = Field(default="English")
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person1_role: List[str]
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person2_role: List[str]
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conversation_style: List[str]
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engagement_technique: List[str]
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dialogue_structure: List[str]
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transcript_model: Optional[str] = None
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transcript_model_provider: Optional[str] = None
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user_instructions: Optional[str] = None
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ending_message: Optional[str] = None
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creativity: float = Field(ge=0, le=1)
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provider: str = Field(default="openai")
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voice1: str
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voice2: str
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model: str
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# Backwards compatibility
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@field_validator("person1_role", "person2_role", mode="before")
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@classmethod
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def split_string_to_list(cls, value):
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if isinstance(value, str):
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return [item.strip() for item in value.split(",")]
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return value
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@model_validator(mode="after")
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def validate_voices(self) -> "PodcastConfig":
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if not self.voice1 or not self.voice2:
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raise ValueError("Both voice1 and voice2 must be provided")
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return self
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def generate_episode(
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self,
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episode_name: str,
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text: str,
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instructions: str = "",
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longform: bool = False,
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chunks: int = 8,
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min_chunk_size=600,
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):
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self.user_instructions = (
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instructions if instructions else self.user_instructions
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)
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conversation_config = {
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"max_num_chunks": chunks,
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"min_chunk_size": min_chunk_size,
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"conversation_style": self.conversation_style,
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"roles_person1": self.person1_role,
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"roles_person2": self.person2_role,
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"dialogue_structure": self.dialogue_structure,
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"podcast_name": self.podcast_name,
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"podcast_tagline": self.podcast_tagline,
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"output_language": self.output_language,
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"user_instructions": self.user_instructions,
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"engagement_techniques": self.engagement_technique,
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"creativity": self.creativity,
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"text_to_speech": {
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"output_directories": {
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"transcripts": f"{DATA_FOLDER}/podcasts/transcripts",
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"audio": f"{DATA_FOLDER}/podcasts/audio",
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},
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"temp_audio_dir": f"{DATA_FOLDER}/podcasts/audio/tmp",
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"ending_message": "Thank you for listening to this episode. Don't forget to subscribe to our podcast for more interesting conversations.",
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"default_tts_model": self.provider,
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self.provider: {
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"default_voices": {
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"question": self.voice1,
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"answer": self.voice2,
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},
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"model": self.model,
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},
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"audio_format": "mp3",
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},
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}
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api_key_label = None
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llm_model_name = None
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tts_model = None
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if self.transcript_model_provider:
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if self.transcript_model_provider == "openai":
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api_key_label = "OPENAI_API_KEY"
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llm_model_name = self.transcript_model
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elif self.transcript_model_provider == "anthropic":
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api_key_label = "ANTHROPIC_API_KEY"
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llm_model_name = self.transcript_model
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elif self.transcript_model_provider == "gemini":
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api_key_label = "GEMINI_API_KEY"
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llm_model_name = self.transcript_model
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if self.provider == "gemini":
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tts_model = "geminimulti"
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elif self.provider == "openai":
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tts_model = "openai"
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elif self.provider == "anthropic":
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tts_model = "anthropic"
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elif self.provider == "elevenlabs":
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tts_model = "elevenlabs"
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logger.debug(
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f"Generating episode {episode_name} with config {conversation_config} and using model {llm_model_name}, tts model {tts_model}"
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)
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audio_file = generate_podcast(
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conversation_config=conversation_config,
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text=text,
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tts_model=tts_model,
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llm_model_name=llm_model_name,
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api_key_label=api_key_label,
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longform=longform,
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)
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episode = PodcastEpisode(
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name=episode_name,
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template=self.name,
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instructions=instructions,
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text=str(text),
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audio_file=audio_file,
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)
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episode.save()
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@field_validator(
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"name", "podcast_name", "podcast_tagline", "output_language", "model"
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)
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@classmethod
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def validate_required_strings(cls, value: str, field) -> str:
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if value is None or value.strip() == "":
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raise ValueError(f"{field.field_name} cannot be None or empty string")
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return value.strip()
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@field_validator("creativity")
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def validate_creativity(cls, value):
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if not 0 <= value <= 1:
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raise ValueError("Creativity must be between 0 and 1")
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return value
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conversation_styles = [
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"Analytical",
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"Argumentative",
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"Informative",
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"Humorous",
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"Casual",
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"Formal",
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"Inspirational",
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"Debate-style",
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"Interview-style",
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"Storytelling",
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"Satirical",
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"Educational",
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"Philosophical",
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"Speculative",
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"Motivational",
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"Fun",
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"Technical",
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"Light-hearted",
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"Serious",
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"Investigative",
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"Debunking",
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"Didactic",
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"Thought-provoking",
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"Controversial",
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"Sarcastic",
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"Emotional",
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"Exploratory",
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"Fast-paced",
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"Slow-paced",
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"Introspective",
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]
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# Dialogue Structures
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dialogue_structures = [
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"Topic Introduction",
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"Opening Monologue",
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"Guest Introduction",
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"Icebreakers",
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"Historical Context",
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"Defining Terms",
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"Problem Statement",
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"Overview of the Issue",
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"Deep Dive into Subtopics",
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"Pro Arguments",
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"Con Arguments",
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"Cross-examination",
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"Expert Interviews",
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"Case Studies",
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"Myth Busting",
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"Q&A Session",
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"Rapid-fire Questions",
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"Summary of Key Points",
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"Recap",
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"Key Takeaways",
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"Actionable Tips",
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"Call to Action",
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"Future Outlook",
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"Closing Remarks",
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"Resource Recommendations",
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"Trending Topics",
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"Closing Inspirational Quote",
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"Final Reflections",
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]
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# Podcast Participant Roles
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participant_roles = [
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"Main Summarizer",
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"Questioner/Clarifier",
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"Optimist",
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"Skeptic",
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"Specialist",
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"Thesis Presenter",
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"Counterargument Provider",
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"Professor",
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"Student",
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"Moderator",
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"Host",
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"Co-host",
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"Expert Guest",
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"Novice",
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"Devil's Advocate",
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"Analyst",
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"Storyteller",
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"Fact-checker",
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"Comedian",
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"Interviewer",
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"Interviewee",
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"Historian",
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"Visionary",
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"Strategist",
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"Critic",
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"Enthusiast",
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"Mediator",
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"Commentator",
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"Researcher",
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"Reporter",
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"Advocate",
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"Debater",
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"Explorer",
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]
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# Engagement Techniques
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engagement_techniques = [
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"Rhetorical Questions",
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"Anecdotes",
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"Analogies",
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"Humor",
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"Metaphors",
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"Storytelling",
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"Quizzes",
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"Personal Testimonials",
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"Quotes",
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"Jokes",
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"Emotional Appeals",
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"Provocative Statements",
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"Sarcasm",
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"Pop Culture References",
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"Thought Experiments",
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"Puzzles and Riddles",
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"Role-playing",
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"Debates",
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"Catchphrases",
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"Statistics and Facts",
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"Open-ended Questions",
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"Challenges to Assumptions",
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"Evoking Curiosity",
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]
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