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| import gradio as gr | |
| from pydub import AudioSegment | |
| import google.generativeai as genai | |
| from google.generativeai.types import HarmCategory, HarmBlockThreshold | |
| import json | |
| import uuid | |
| import io | |
| import edge_tts | |
| import asyncio | |
| import aiofiles | |
| import pypdf | |
| import os | |
| import time | |
| from typing import List, Dict, Tuple | |
| class PodcastGenerator: | |
| def __init__(self): | |
| pass | |
| async def generate_script(self, prompt: str, language: str, api_key: str) -> Dict: | |
| """ | |
| 異步生成基於給定提示和語言的播客腳本。 | |
| 參數： | |
| prompt (str): 用於生成播客腳本的用戶輸入文本。 | |
| language (str): 播客腳本所需的語言。 | |
| api_key (str): 用於訪問 Gemini AI 服務的 API 密鑰。 | |
| 返回： | |
| Dict: 包含以 JSON 格式生成的播客腳本的字典。 | |
| 異常： | |
| gr.Error: 如果 API 密鑰或速率限制出現問題。 | |
| 此方法使用 Gemini AI 模型根據用戶的輸入生成播客腳本。 | |
| 它處理語言選擇，使用適當的配置設置 AI 模型，並處理生成的響應。 | |
| """ | |
| # 定義一個示例JSON結構，用於指導AI生成類似格式的Podcast指令碼 | |
| example = """ | |
| { | |
| "topic": "AGI", | |
| "podcast": [ | |
| { | |
| "speaker": 2, | |
| "line": "So, AGI, huh? Seems like everyone's talking about it these days." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Yeah, it's definitely having a moment, isn't it?" | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "It is and for good reason, right? I mean, you've been digging into this stuff, listening to the podcasts and everything. What really stood out to you? What got you hooked?" | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Honestly, it's the sheer scale of what AGI could do. We're talking about potentially reshaping well everything." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "No kidding, but let's be real. Sometimes it feels like every other headline is either hyping AGI up as this technological utopia or painting it as our inevitable robot overlords." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "It's easy to get lost in the noise, for sure." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "Exactly. So how about we try to cut through some of that, shall we?" | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Sounds like a plan." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "Okay, so first things first, AGI, what is it really? And I don't just mean some dictionary definition, we're talking about something way bigger than just a super smart computer, right?" | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Right, it's not just about more processing power or better algorithms, it's about a fundamental shift in how we think about intelligence itself." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "So like, instead of programming a machine for a specific task, we're talking about creating something that can learn and adapt like we do." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Exactly, think of it this way: Right now, we've got AI that can beat a grandmaster at chess but ask that same AI to, say, write a poem or compose a symphony. No chance." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "Okay, I see. So, AGI is about bridging that gap, creating something that can move between those different realms of knowledge seamlessly." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Precisely. It's about replicating that uniquely human ability to learn something new and apply that knowledge in completely different contexts and that's a tall order, let me tell you." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "I bet. I mean, think about how much we still don't even understand about our own brains." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "That's exactly it. We're essentially trying to reverse-engineer something we don't fully comprehend." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "And how are researchers even approaching that? What are some of the big ideas out there?" | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Well, there are a few different schools of thought. One is this idea of neuromorphic computing where they're literally trying to build computer chips that mimic the structure and function of the human brain." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "Wow, so like actually replicating the physical architecture of the brain. That's wild." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "It's pretty mind-blowing stuff and then you've got folks working on something called whole brain emulation." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "Okay, and what's that all about?" | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "The basic idea there is to create a complete digital copy of a human brain down to the last neuron and synapse and run it on a sufficiently powerful computer simulation." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "Hold on, a digital copy of an entire brain, that sounds like something straight out of science fiction." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "It does, doesn't it? But it gives you an idea of the kind of ambition we're talking about here and the truth is we're still a long way off from truly achieving AGI, no matter which approach you look at." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "That makes sense but it's still exciting to think about the possibilities, even if they're a ways off." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Absolutely and those possibilities are what really get people fired up about AGI, right? Yeah." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "For sure. In fact, I remember you mentioning something in that podcast about AGI's potential to revolutionize scientific research. Something about supercharging breakthroughs." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Oh, absolutely. Imagine an AI that doesn't just crunch numbers but actually understands scientific data the way a human researcher does. We're talking about potential breakthroughs in everything from medicine and healthcare to material science and climate change." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "It's like giving scientists this incredibly powerful new tool to tackle some of the biggest challenges we face." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Exactly, it could be a total game changer." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "Okay, but let's be real, every coin has two sides. What about the potential downsides of AGI? Because it can't all be sunshine and roses, right?" | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Right, there are definitely valid concerns. Probably the biggest one is the impact on the job market. As AGI gets more sophisticated, there's a real chance it could automate a lot of jobs that are currently done by humans." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "So we're not just talking about robots taking over factories but potentially things like, what, legal work, analysis, even creative fields?" | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Potentially, yes. And that raises a whole host of questions about what happens to those workers, how we retrain them, how we ensure that the benefits of AGI are shared equitably." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "Right, because it's not just about the technology itself, but how we choose to integrate it into society." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Absolutely. We need to be having these conversations now about ethics, about regulation, about how to make sure AGI is developed and deployed responsibly." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "So it's less about preventing some kind of sci-fi robot apocalypse and more about making sure we're steering this technology in the right direction from the get-go." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Exactly, AGI has the potential to be incredibly beneficial, but it's not going to magically solve all our problems. It's on us to make sure we're using it for good." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "It's like you said earlier, it's about shaping the future of intelligence." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "I like that. It really is." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "And honestly, that's a responsibility that extends beyond just the researchers and the policymakers." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "100%" | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "So to everyone listening out there I'll leave you with this. As AGI continues to develop, what role do you want to play in shaping its future?" | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "That's a question worth pondering." | |
| }, | |
| { | |
| "speaker": 2, | |
| "line": "It certainly is and on that note, we'll wrap up this deep dive. Thanks for listening, everyone." | |
| }, | |
| { | |
| "speaker": 1, | |
| "line": "Peace." | |
| } | |
| ] | |
| } | |
| """ | |
| # 根據使用者選擇的語言設定指令 | |
| if language == "Auto Detect": | |
| language_instruction = "- The podcast MUST be in the same language as the user input." | |
| else: | |
| language_instruction = f"- The podcast MUST be in {language} language" | |
| # 設定系統提示，指導AI如何生成Podcast指令碼 | |
| system_prompt = f""" | |
| You are a professional podcast generator. Your task is to generate a professional podcast script based on the user input. | |
| {language_instruction} | |
| - The podcast should have 2 speakers. | |
| - The podcast should be long. | |
| - Do not use names for the speakers. | |
| - The podcast should be interesting, lively, and engaging, and hook the listener from the start. | |
| - The input text might be disorganized or unformatted, originating from sources like PDFs or text files. Ignore any formatting inconsistencies or irrelevant details; your task is to distill the essential points, identify key definitions, and highlight intriguing facts that would be suitable for discussion in a podcast. | |
| - The script must be in JSON format. | |
| Follow this example structure: | |
| {example} | |
| """ | |
| # 設定使用者提示，包含使用者輸入的內容 | |
| user_prompt = f"Please generate a podcast script based on the following user input:\n{prompt}" | |
| # 設定訊息列表，包含使用者提示 | |
| messages = [ | |
| {"role": "user", "parts": [user_prompt]} | |
| ] | |
| # 配置Google Generative AI | |
| genai.configure(api_key=api_key) | |
| # 設定生成配置 | |
| generation_config = { | |
| "temperature": 1, | |
| "max_output_tokens": 8192, | |
| "response_mime_type": "application/json", | |
| } | |
| # 建立生成模型實例 | |
| model = genai.GenerativeModel( | |
| model_name="gemini-1.5-flash-002", | |
| generation_config=generation_config, | |
| safety_settings={ | |
| HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, | |
| HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, | |
| HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, | |
| HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE | |
| }, | |
| system_instruction=system_prompt | |
| ) | |
| # 嘗試生成內容 | |
| try: | |
| response = await model.generate_content_async(messages) | |
| except Exception as e: | |
| # 處理可能的錯誤 | |
| if "API key not valid" in str(e): | |
| raise gr.Error("Invalid API key. Please provide a valid Gemini API key.") | |
| elif "rate limit" in str(e).lower(): | |
| raise gr.Error("Rate limit exceeded for the API key. Please try again later or provide your own Gemini API key.") | |
| else: | |
| raise gr.Error(f"Failed to generate podcast script: {e}") | |
| # 列印生成的Podcast指令碼 | |
| print(f"Generated podcast script:\n{response.text}") | |
| # 返回解析後的JSON資料 | |
| return json.loads(response.text) | |
| async def tts_generate(self, text: str, speaker: int, speaker1: str, speaker2: str) -> str: | |
| """ | |
| 非同步生成文字轉語音音訊檔案。 | |
| 參數： | |
| text (str): 要轉換為語音的文字內容。 | |
| speaker (int): 說話者的編號（1 或 2）。 | |
| speaker1 (str): 第一位說話者的語音設定。 | |
| speaker2 (str): 第二位說話者的語音設定。 | |
| 返回： | |
| str: 生成的臨時音訊檔案的檔名。 | |
| 此方法使用 Edge TTS 將文字轉換為語音，並將結果保存為臨時音訊檔案。 | |
| 根據指定的說話者編號選擇相應的語音設定。 | |
| """ | |
| # 根據說話者選擇語音 | |
| voice = speaker1 if speaker == 1 else speaker2 | |
| # 建立語音合成對象 | |
| speech = edge_tts.Communicate(text, voice) | |
| # 生成臨時檔名 | |
| temp_filename = f"temp_{uuid.uuid4()}.wav" | |
| try: | |
| # 儲存語音檔案 | |
| await speech.save(temp_filename) | |
| return temp_filename | |
| except Exception as e: | |
| # 如果出錯，刪除臨時檔案並丟擲異常 | |
| if os.path.exists(temp_filename): | |
| os.remove(temp_filename) | |
| raise e | |
| async def combine_audio_files(self, audio_files: List[str]) -> str: | |
| """ | |
| 非同步合併音訊檔案。 | |
| 參數： | |
| audio_files (List[str]): 包含音訊檔案路徑的列表。 | |
| 返回： | |
| str: 合併後的音訊檔案的檔名。 | |
| """ | |
| # 建立空的音訊段 | |
| combined_audio = AudioSegment.empty() | |
| # 遍歷所有音訊檔案並合併 | |
| for audio_file in audio_files: | |
| combined_audio += AudioSegment.from_file(audio_file) | |
| os.remove(audio_file) # 清理臨時檔案 | |
| # 生成輸出文件名 | |
| output_filename = f"output_{uuid.uuid4()}.wav" | |
| # 匯出合併後的音訊 | |
| combined_audio.export(output_filename, format="wav") | |
| return output_filename | |
| async def generate_podcast(self, input_text: str, language: str, speaker1: str, speaker2: str, api_key: str) -> str: | |
| """ | |
| 非同步生成播客音訊檔案。 | |
| 參數： | |
| input_text (str): 用於生成播客腳本的輸入文本。 | |
| language (str): 播客使用的語言。 | |
| speaker1 (str): 第一位說話者的語音設定。 | |
| speaker2 (str): 第二位說話者的語音設定。 | |
| api_key (str): 用於訪問 Gemini AI 服務的 API 密鑰。 | |
| 返回： | |
| str: 生成的播客音訊檔案的檔名。 | |
| 此方法執行以下步驟： | |
| 1. 使用 generate_script 方法生成播客腳本。 | |
| 2. 使用 tts_generate 方法為每個對話行生成音訊檔案。 | |
| 3. 使用 combine_audio_files 方法將所有音訊檔案合併為一個完整的播客。 | |
| 整個過程是非同步的，以提高效率。方法還會記錄並顯示每個步驟的執行時間。 | |
| """ | |
| # 生成Podcast指令碼 | |
| gr.Info("Generating podcast script...") | |
| start_time = time.time() | |
| podcast_json = await self.generate_script(input_text, language, api_key) | |
| end_time = time.time() | |
| gr.Info(f"Successfully generated podcast script in {(end_time - start_time):.2f} seconds!") | |
| # 生成Podcast音訊檔案 | |
| gr.Info("Generating podcast audio files...") | |
| start_time = time.time() | |
| audio_files = await asyncio.gather(*[self.tts_generate(item['line'], item['speaker'], speaker1, speaker2) for item in podcast_json['podcast']]) | |
| end_time = time.time() | |
| gr.Info(f"Successfully generated podcast audio files in {(end_time - start_time):.2f} seconds!") | |
| # 合併音訊檔案 | |
| combined_audio = await self.combine_audio_files(audio_files) | |
| return combined_audio | |
| class TextExtractor: | |
| async def extract_from_pdf(file_path: str) -> str: | |
| # 從PDF檔案中提取文字 | |
| async with aiofiles.open(file_path, 'rb') as file: | |
| content = await file.read() | |
| pdf_reader = pypdf.PdfReader(io.BytesIO(content)) | |
| return "\n\n".join(page.extract_text() for page in pdf_reader.pages if page.extract_text()) | |
| async def extract_from_txt(file_path: str) -> str: | |
| # 從TXT檔案中提取文字 | |
| async with aiofiles.open(file_path, 'r') as file: | |
| return await file.read() | |
| async def extract_text(cls, file_path: str) -> str: | |
| # 根據檔案型別選擇適當的提取方法 | |
| _, file_extension = os.path.splitext(file_path) | |
| if file_extension.lower() == '.pdf': | |
| return await cls.extract_from_pdf(file_path) | |
| elif file_extension.lower() == '.txt': | |
| return await cls.extract_from_txt(file_path) | |
| else: | |
| raise gr.Error(f"Unsupported file type: {file_extension}") | |
| async def process_input(input_text: str, input_file, language: str, speaker1: str, speaker2: str, api_key: str = "") -> str: | |
| """ | |
| 處理輸入並生成播客的非同步函數。 | |
| 參數: | |
| input_text (str): 用戶輸入的文本內容。 | |
| input_file: 用戶上傳的文件（可以是 PDF 或 TXT）。 | |
| language (str): 選擇的語言。 | |
| speaker1 (str): 第一位說話者的語音選擇。 | |
| speaker2 (str): 第二位說話者的語音選擇。 | |
| api_key (str): 用於生成 AI 的 API 金鑰，預設為空字串。 | |
| 返回: | |
| str: 生成的播客音頻文件路徑。 | |
| 此函數協調整個播客生成過程，包括文本提取、腳本生成和音頻合成。 | |
| 它處理不同的輸入類型（文本或文件），並使用指定的語音和語言設置來創建最終的播客。 | |
| """ | |
| # 開始生成Podcast | |
| gr.Info("Starting podcast generation...") | |
| start_time = time.time() | |
| # 定義語音名稱對映 | |
| voice_names = { | |
| "Andrew - English (United States)": "en-US-AndrewMultilingualNeural", | |
| "Ava - English (United States)": "en-US-AvaMultilingualNeural", | |
| "Brian - English (United States)": "en-US-BrianMultilingualNeural", | |
| "Emma - English (United States)": "en-US-EmmaMultilingualNeural", | |
| "Florian - German (Germany)": "de-DE-FlorianMultilingualNeural", | |
| "Seraphina - German (Germany)": "de-DE-SeraphinaMultilingualNeural", | |
| "Remy - French (France)": "fr-FR-RemyMultilingualNeural", | |
| "Vivienne - French (France)": "fr-FR-VivienneMultilingualNeural" | |
| } | |
| # 獲取實際的語音名稱 | |
| speaker1 = voice_names[speaker1] | |
| speaker2 = voice_names[speaker2] | |
| # 如果提供了輸入檔案，則從檔案中提取文字 | |
| if input_file: | |
| input_text = await TextExtractor.extract_text(input_file.name) | |
| # 如果沒有提供API金鑰，則使用環境變數中的金鑰 | |
| if not api_key: | |
| api_key = os.getenv("GENAI_API_KEY") | |
| # 建立PodcastGenerator實例並生成Podcast | |
| podcast_generator = PodcastGenerator() | |
| podcast = await podcast_generator.generate_podcast(input_text, language, speaker1, speaker2, api_key) | |
| # 計算總耗時並顯示資訊 | |
| end_time = time.time() | |
| gr.Info(f"Successfully generated podcast in {(end_time - start_time):.2f} seconds!") | |
| return podcast | |
| # 定義Gradio介面 | |
| iface = gr.Interface( | |
| fn=process_input, | |
| inputs=[ | |
| gr.Textbox(label="Input Text"), | |
| gr.File(label="Or Upload a PDF or TXT file"), | |
| gr.Dropdown(label="Language", choices=[ | |
| "Auto Detect", | |
| "Afrikaans", "Albanian", "Amharic", "Arabic", "Armenian", "Azerbaijani", | |
| "Bahasa Indonesian", "Bangla", "Basque", "Bengali", "Bosnian", "Bulgarian", | |
| "Burmese", "Catalan", "Chinese Cantonese", "Chinese Mandarin", | |
| "Chinese Taiwanese", "Croatian", "Czech", "Danish", "Dutch", "English", | |
| "Estonian", "Filipino", "Finnish", "French", "Galician", "Georgian", | |
| "German", "Greek", "Hebrew", "Hindi", "Hungarian", "Icelandic", "Irish", | |
| "Italian", "Japanese", "Javanese", "Kannada", "Kazakh", "Khmer", "Korean", | |
| "Lao", "Latvian", "Lithuanian", "Macedonian", "Malay", "Malayalam", | |
| "Maltese", "Mongolian", "Nepali", "Norwegian Bokmål", "Pashto", "Persian", | |
| "Polish", "Portuguese", "Romanian", "Russian", "Serbian", "Sinhala", | |
| "Slovak", "Slovene", "Somali", "Spanish", "Sundanese", "Swahili", | |
| "Swedish", "Tamil", "Telugu", "Thai", "Turkish", "Ukrainian", "Urdu", | |
| "Uzbek", "Vietnamese", "Welsh", "Zulu" | |
| ], | |
| value="Auto Detect"), | |
| gr.Dropdown(label="Speaker 1 Voice", choices=[ | |
| "Andrew - English (United States)", | |
| "Ava - English (United States)", | |
| "Brian - English (United States)", | |
| "Emma - English (United States)", | |
| "Florian - German (Germany)", | |
| "Seraphina - German (Germany)", | |
| "Remy - French (France)", | |
| "Vivienne - French (France)" | |
| ], | |
| value="Andrew - English (United States)"), | |
| gr.Dropdown(label="Speaker 2 Voice", choices=[ | |
| "Andrew - English (United States)", | |
| "Ava - English (United States)", | |
| "Brian - English (United States)", | |
| "Emma - English (United States)", | |
| "Florian - German (Germany)", | |
| "Seraphina - German (Germany)", | |
| "Remy - French (France)", | |
| "Vivienne - French (France)" | |
| ], | |
| value="Ava - English (United States)"), | |
| gr.Textbox(label="Your Gemini API Key (Optional) - In case you are getting rate limited"), | |
| ], | |
| outputs=[ | |
| gr.Audio(label="Generated Podcast Audio") | |
| ], | |
| title="PodcastGen 🎙️", | |
| description="Generate a 2-speaker podcast from text input or documents!", | |
| allow_flagging="never" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |