Wikipedia to Podcast Converter

Transform any Wikipedia article into a conversational podcast

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Checking system status...

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Converting article to podcast... This may take a minute.

⏳ Fetching Wikipedia article...

⏳ Generating podcast script...

⏳ Converting script to audio...

⏳ Finalizing podcast...

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Error

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Podcast Script

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\n \n \n\n",[],[150,169],[150],{"_107":180,"_100":181,"_151":182,"_153":-5,"_154":138,"_155":173,"_157":158,"_109":183,"_104":31,"_160":174,"_162":184,"_163":27,"_164":-5,"_165":185,"_103":186},"40d2ff1a-31ff-11f0-809e-569c3dd06744","README.md","40d2ffce-31ff-11f0-809e-569c3dd06744",["D",1747363282239],"# Wikipedia to Podcast Converter\n\nThis Val Town app converts Wikipedia articles into conversational podcast episodes. It:\n\n1. Fetches content from Wikipedia articles\n2. Transforms the content into engaging podcast scripts using OpenAI\n3. Converts the scripts to audio using Google's Text-to-Speech service\n\n## Setup\n\nTo use this app, you'll need to set up the following environment variables in Val Town:\n\n- `OPENAI_API_KEY`: Your OpenAI API key\n- `GOOGLE_APPLICATION_CREDENTIALS`: Your Google Cloud credentials JSON (stringified)\n\n## How to Use\n\n1. Make a POST request to the HTTP endpoint with a JSON body containing:\n ```json\n {\n \"article\": \"Article_Title_Or_URL\",\n \"voices\": [\"en-US-Neural2-F\", \"en-US-Neural2-D\"] // Optional: Specify Google TTS voices\n }\n ```\n\n2. The app will return a link to the generated audio file.\n\n## Project Structure\n\n- `index.ts`: Main HTTP handler\n- `wikipedia.ts`: Wikipedia article fetching\n- `openai.ts`: Script generation with OpenAI\n- `tts.ts`: Google Text-to-Speech integration\n- `frontend/`: Simple web interface for the app\n\n## Limitations\n\n- Audio files are temporarily stored and will expire after some time\n- Processing large Wikipedia articles may take longer and consume more tokens",[],[181],{"_107":188,"_100":189,"_151":190,"_153":-5,"_154":138,"_155":191,"_157":158,"_109":192,"_104":193,"_160":194,"_162":195,"_163":27,"_164":-5,"_165":196,"_103":197},"08a8e2b6-3200-11f0-a713-569c3dd06744","index.ts","4a216782-31ff-11f0-b2a7-569c3dd06744",8,["D",1747363617504],2,"http","import { Hono } from \"https://esm.sh/hono@3.12.6\";\nimport { cors } from \"https://esm.sh/hono@3.12.6/cors\";\nimport { serveFile, readFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\nimport { fetchWikipediaArticle } from \"./wikipedia.ts\";\nimport { generatePodcastScript } from \"./openai.ts\";\nimport { textToSpeech } from \"./tts.ts\";\nimport { blob } from \"https://esm.town/v/std/blob\";\nimport { email } from \"https://esm.town/v/std/email\";\n\nconst app = new Hono();\n\n// Unwrap Hono errors to see original error details\napp.onError((err, c) => {\n console.error(\"Application error:\", err);\n return c.json({ \n error: err.message || \"An unexpected error occurred\",\n stack: err.stack,\n name: err.name\n }, 500);\n});\n\n// Enable CORS\napp.use(\"*\", cors());\n\n// Simple health check endpoint\napp.get(\"/api/health\", (c) => {\n return c.json({ status: \"ok\", timestamp: new Date().toISOString() });\n});\n\n// Debug endpoint to check environment\napp.get(\"/api/debug\", async (c) => {\n const hasOpenAI = !!Deno.env.get(\"OPENAI_API_KEY\");\n const hasGoogleCreds = !!Deno.env.get(\"GOOGLE_APPLICATION_CREDENTIALS\");\n \n return c.json({\n environment: {\n hasOpenAIKey: hasOpenAI,\n hasGoogleCredentials: hasGoogleCreds,\n googleCredsLength: hasGoogleCreds ? Deno.env.get(\"GOOGLE_APPLICATION_CREDENTIALS\")?.length : 0\n },\n runtime: {\n denoVersion: Deno.version.deno,\n v8Version: Deno.version.v8,\n typescript: Deno.version.typescript\n }\n });\n});\n\n// Serve frontend files\napp.get(\"/\", async (c) => {\n try {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n return c.html(html);\n } catch (error) {\n console.error(\"Error serving frontend:\", error);\n return c.text(`Error serving frontend: ${error.message}`, 500);\n }\n});\n\napp.get(\"/frontend/*\", (c) => serveFile(c.req.path, import.meta.url));\n\n// API endpoint to convert Wikipedia article to podcast\napp.post(\"/api/convert\", async (c) => {\n try {\n console.log(\"Starting conversion process\");\n const body = await c.req.json();\n const { article, voices = [\"en-US-Neural2-F\", \"en-US-Neural2-D\"] } = body;\n \n if (!article) {\n return c.json({ error: \"Article title or URL is required\" }, 400);\n }\n \n console.log(`Processing article: ${article}`);\n \n // Step 1: Fetch Wikipedia article\n console.log(\"Fetching Wikipedia article...\");\n const { title, content } = await fetchWikipediaArticle(article);\n console.log(`Successfully fetched article: \"${title}\" (${content.length} characters)`);\n \n // Step 2: Generate podcast script using OpenAI\n console.log(\"Generating podcast script with OpenAI...\");\n const script = await generatePodcastScript(title, content);\n console.log(`Successfully generated script (${script.length} characters)`);\n \n // For debugging, let's save the script to blob storage too\n const scriptFilename = `script_${Date.now()}.txt`;\n await blob.set(scriptFilename, new TextEncoder().encode(script));\n \n // Step 3: Convert script to audio using Google TTS\n console.log(\"Converting script to audio with Google TTS...\");\n try {\n const audioBuffer = await textToSpeech(script, voices);\n console.log(`Successfully generated audio (${audioBuffer.length} bytes)`);\n \n // Step 4: Store the audio file\n const filename = `podcast_${Date.now()}.mp3`;\n await blob.set(filename, audioBuffer);\n console.log(`Saved audio to blob storage as ${filename}`);\n \n const username = parseProject(import.meta.url).username;\n \n // Return the result\n return c.json({\n success: true,\n title,\n audioUrl: `https://val.town/v/api/${username}/audio/${filename}`,\n scriptUrl: `https://val.town/v/api/${username}/script/${scriptFilename}`,\n script\n });\n } catch (ttsError) {\n console.error(\"TTS Error:\", ttsError);\n \n // If TTS fails, still return the script but with error info\n return c.json({\n success: false,\n title,\n script,\n error: `Text-to-speech failed: ${ttsError.message}`,\n ttsErrorDetails: {\n name: ttsError.name,\n message: ttsError.message,\n stack: ttsError.stack\n }\n }, 500);\n }\n } catch (error) {\n console.error(\"Error converting article to podcast:\", error);\n \n // Send error notification email for debugging\n try {\n await email({\n subject: \"Wikipedia Podcast Converter Error\",\n text: `Error: ${error.message}\\n\\nStack: ${error.stack}`\n });\n } catch (emailError) {\n console.error(\"Failed to send error email:\", emailError);\n }\n \n return c.json({ \n error: error.message || \"Failed to convert article to podcast\",\n errorType: error.name,\n stack: error.stack\n }, 500);\n }\n});\n\n// Endpoint to serve audio files\napp.get(\"/audio/:filename\", async (c) => {\n const filename = c.req.param(\"filename\");\n console.log(`Serving audio file: ${filename}`);\n \n try {\n const audioData = await blob.get(filename);\n if (!audioData) {\n console.error(`Audio file not found: ${filename}`);\n return c.json({ error: \"Audio file not found\" }, 404);\n }\n \n console.log(`Successfully retrieved audio file: ${filename} (${audioData.length} bytes)`);\n \n return new Response(audioData, {\n headers: {\n \"Content-Type\": \"audio/mpeg\",\n \"Content-Disposition\": `attachment; filename=\"${filename}\"`,\n \"Cache-Control\": \"public, max-age=3600\"\n }\n });\n } catch (error) {\n console.error(`Error serving audio file ${filename}:`, error);\n return c.json({ \n error: \"Failed to retrieve audio file\",\n details: error.message,\n filename\n }, 500);\n }\n});\n\n// Endpoint to serve script files (for debugging)\napp.get(\"/script/:filename\", async (c) => {\n const filename = c.req.param(\"filename\");\n \n try {\n const scriptData = await blob.get(filename);\n if (!scriptData) {\n return c.json({ error: \"Script file not found\" }, 404);\n }\n \n const scriptText = new TextDecoder().decode(scriptData);\n \n return new Response(scriptText, {\n headers: {\n \"Content-Type\": \"text/plain\",\n \"Content-Disposition\": `attachment; filename=\"${filename}\"`,\n \"Cache-Control\": \"public, max-age=3600\"\n }\n });\n } catch (error) {\n return c.json({ error: \"Failed to retrieve script file\" }, 500);\n }\n});\n\nfunction parseProject(url: string) {\n const match = url.match(/v\\/([^\\/]+)\\/([^\\/]+)/);\n return {\n username: match ? match[1] : \"unknown\",\n name: match ? match[2] : \"unknown\"\n };\n}\n\n// This is the entry point for HTTP vals\nexport default app.fetch;",[],[189],{"_107":199,"_100":200,"_151":201,"_153":-5,"_154":138,"_155":202,"_157":158,"_109":203,"_104":31,"_160":204,"_162":205,"_163":27,"_164":-5,"_165":206,"_103":207},"5921592c-31ff-11f0-a6a8-569c3dd06744","openai.ts","592159cc-31ff-11f0-a6a8-569c3dd06744",4,["D",1747363323019],"script","/**\n * OpenAI integration for generating podcast scripts from Wikipedia articles\n */\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport { truncateContent } from \"./wikipedia.ts\";\n\n// Initialize OpenAI client\nconst openai = new OpenAI();\n\n/**\n * Generates a conversational podcast script from a Wikipedia article\n * @param title - The article title\n * @param content - The article content\n * @returns A conversational podcast script\n */\nexport async function generatePodcastScript(title: string, content: string): Promise {\n // Truncate content to avoid exceeding token limits\n const truncatedContent = truncateContent(content, 6000);\n \n try {\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n {\n role: \"system\",\n content: `You are an expert podcast script writer. Your task is to transform Wikipedia articles into engaging, \n conversational podcast scripts between two hosts (Host A and Host B). The script should:\n \n 1. Start with a brief introduction of the topic\n 2. Present the information in a natural, conversational way\n 3. Include interesting facts, anecdotes, and context\n 4. Have a clear structure with transitions between subtopics\n 5. End with a conclusion summarizing key points\n \n Format the script with clear speaker labels (Host A: and Host B:) and include natural conversational elements \n like questions, reactions, and brief discussions. Make it sound like a real conversation, not just taking turns \n reading facts. Keep the total length to about 5-7 minutes of speaking time.`\n },\n {\n role: \"user\",\n content: `Please create a conversational podcast script for two hosts based on this Wikipedia article about \"${title}\":\n \n ${truncatedContent}`\n }\n ],\n temperature: 0.7,\n max_tokens: 2500,\n });\n \n return completion.choices[0].message.content || \"Failed to generate podcast script.\";\n } catch (error) {\n console.error(\"Error generating podcast script:\", error);\n throw new Error(`Failed to generate podcast script: ${error.message}`);\n }\n}",[],[200],{"_107":209,"_100":210,"_151":211,"_153":-5,"_154":138,"_155":137,"_157":158,"_109":212,"_104":31,"_160":204,"_162":213,"_163":27,"_164":-5,"_165":214,"_103":215},"58cc2ce4-3200-11f0-b297-569c3dd06744","tts-fallback.ts","58cc2da2-3200-11f0-b297-569c3dd06744",["D",1747363751960],"/**\n * Fallback TTS implementation using a simpler service\n * This is used when Google TTS fails\n */\n\n/**\n * Converts text to speech using a simpler TTS service\n * @param text - The text to convert to speech\n * @returns Audio buffer containing the speech\n */\nexport async function simpleTTS(text: string): Promise {\n try {\n // Use a simple TTS service that doesn't require authentication\n // This is a fallback and won't have the same quality as Google TTS\n \n // For this example, we'll use the VoiceRSS API (requires API key)\n // In a real implementation, you would use your preferred fallback service\n const apiKey = Deno.env.get(\"VOICERSS_API_KEY\");\n \n if (!apiKey) {\n console.log(\"No VoiceRSS API key found for fallback TTS\");\n // Return empty audio if no API key is available\n return new Uint8Array(0);\n }\n \n // Limit text length to avoid API limits\n const limitedText = text.substring(0, 2000);\n \n const params = new URLSearchParams({\n key: apiKey,\n src: limitedText,\n hl: \"en-us\",\n v: \"Mary\", // Voice\n r: \"0\", // Reading speed\n c: \"mp3\", // Format\n f: \"8khz_8bit_mono\" // Quality\n });\n \n const response = await fetch(`https://api.voicerss.org/?${params.toString()}`);\n \n if (!response.ok) {\n throw new Error(`VoiceRSS API error: ${response.statusText}`);\n }\n \n const audioBuffer = await response.arrayBuffer();\n return new Uint8Array(audioBuffer);\n } catch (error) {\n console.error(\"Error in fallback TTS:\", error);\n // Return empty audio on error\n return new Uint8Array(0);\n }\n}",[],[210],{"_107":217,"_100":218,"_151":219,"_153":-5,"_154":138,"_155":220,"_157":158,"_109":221,"_104":173,"_160":204,"_162":222,"_163":27,"_164":-5,"_165":223,"_103":224},"21e8e91a-3200-11f0-841a-569c3dd06744","tts.ts","634683f0-31ff-11f0-8553-569c3dd06744",9,["D",1747363659869],"/**\n * Google Text-to-Speech integration for converting podcast scripts to audio\n */\n\n// Import necessary libraries\nimport { TextToSpeechClient } from \"https://esm.sh/@google-cloud/text-to-speech@5.0.1\";\n\n// Interface for script segments\ninterface ScriptSegment {\n speaker: string;\n text: string;\n}\n\n/**\n * Parses a podcast script into segments by speaker\n * @param script - The podcast script with \"Host A:\" and \"Host B:\" labels\n * @returns Array of script segments with speaker and text\n */\nfunction parseScript(script: string): ScriptSegment[] {\n console.log(\"Parsing script into segments...\");\n const segments: ScriptSegment[] = [];\n const lines = script.split(\"\\n\");\n \n let currentSpeaker = \"\";\n let currentText = \"\";\n \n for (const line of lines) {\n const trimmedLine = line.trim();\n \n // Skip empty lines\n if (!trimmedLine) continue;\n \n // Check if this line starts with a speaker label\n const speakerMatch = trimmedLine.match(/^(Host [AB]):(.+)/i);\n \n if (speakerMatch) {\n // If we have accumulated text for a previous speaker, add it to segments\n if (currentSpeaker && currentText) {\n segments.push({\n speaker: currentSpeaker,\n text: currentText.trim()\n });\n }\n \n // Start a new segment\n currentSpeaker = speakerMatch[1];\n currentText = speakerMatch[2].trim();\n } else if (currentSpeaker) {\n // Continue the current segment\n currentText += \" \" + trimmedLine;\n }\n }\n \n // Add the last segment\n if (currentSpeaker && currentText) {\n segments.push({\n speaker: currentSpeaker,\n text: currentText.trim()\n });\n }\n \n console.log(`Parsed ${segments.length} script segments`);\n return segments;\n}\n\n/**\n * Fallback function to generate audio using a simpler approach\n * This is used if the Google TTS integration fails\n */\nasync function fallbackTextToSpeech(script: string): Promise {\n console.log(\"Using fallback TTS method...\");\n \n // This is a placeholder - in a real implementation, you might use\n // a different TTS service or a simpler approach\n \n // For now, we'll just return an empty audio buffer\n // In a real implementation, you would replace this with actual audio generation\n return new Uint8Array(0);\n}\n\n/**\n * Validates Google Cloud credentials\n * @returns True if credentials are valid, false otherwise\n */\nfunction validateCredentials(credentials: any): boolean {\n if (!credentials) return false;\n \n // Check for required fields in Google Cloud credentials\n const requiredFields = ['type', 'project_id', 'private_key_id', 'private_key', 'client_email'];\n for (const field of requiredFields) {\n if (!credentials[field]) {\n console.error(`Missing required field in Google credentials: ${field}`);\n return false;\n }\n }\n \n return true;\n}\n\n/**\n * Converts text to speech using Google's TTS API\n * @param script - The podcast script\n * @param voices - Array of voice names to use for different speakers\n * @returns Audio buffer containing the podcast\n */\nexport async function textToSpeech(script: string, voices: string[] = [\"en-US-Neural2-F\", \"en-US-Neural2-D\"]): Promise {\n try {\n console.log(\"Starting text-to-speech conversion...\");\n \n // Get the credentials from environment variable\n const credentialsStr = Deno.env.get(\"GOOGLE_APPLICATION_CREDENTIALS\");\n if (!credentialsStr) {\n throw new Error(\"Google Cloud credentials not found in environment variables\");\n }\n \n console.log(`Credentials string length: ${credentialsStr.length}`);\n \n // Parse the credentials\n let credentials;\n try {\n credentials = JSON.parse(credentialsStr);\n console.log(\"Successfully parsed Google Cloud credentials\");\n } catch (parseError) {\n console.error(\"Failed to parse Google Cloud credentials:\", parseError);\n throw new Error(`Failed to parse Google Cloud credentials: ${parseError.message}`);\n }\n \n // Validate credentials\n if (!validateCredentials(credentials)) {\n throw new Error(\"Invalid Google Cloud credentials\");\n }\n \n // Initialize the TTS client\n console.log(\"Initializing Google TTS client...\");\n const client = new TextToSpeechClient({ credentials });\n \n // Parse the script into segments\n const segments = parseScript(script);\n \n if (segments.length === 0) {\n throw new Error(\"No valid script segments found. Check script format.\");\n }\n \n // Generate audio for each segment\n console.log(`Generating audio for ${segments.length} segments...`);\n \n // Process segments in smaller batches to avoid overwhelming the API\n const batchSize = 5;\n const audioSegments: Uint8Array[] = [];\n \n for (let i = 0; i < segments.length; i += batchSize) {\n const batch = segments.slice(i, i + batchSize);\n console.log(`Processing batch ${i / batchSize + 1} (${batch.length} segments)...`);\n \n const batchPromises = batch.map(async (segment, index) => {\n try {\n // Alternate voices based on speaker\n const voice = segment.speaker.includes(\"A\") ? voices[0] : voices[1];\n \n // Limit text length to avoid API limits\n const text = segment.text.substring(0, 5000);\n \n const request = {\n input: { text },\n voice: { languageCode: \"en-US\", name: voice },\n audioConfig: { audioEncoding: \"MP3\" },\n };\n \n console.log(`Sending TTS request for segment ${i + index + 1}/${segments.length}`);\n const [response] = await client.synthesizeSpeech(request);\n console.log(`Received TTS response for segment ${i + index + 1}/${segments.length}`);\n \n return response.audioContent;\n } catch (segmentError) {\n console.error(`Error processing segment ${i + index + 1}:`, segmentError);\n // Return empty audio for failed segments\n return new Uint8Array(0);\n }\n });\n \n const batchResults = await Promise.all(batchPromises);\n audioSegments.push(...batchResults);\n }\n \n console.log(`Successfully generated ${audioSegments.length} audio segments`);\n \n // Concatenate all audio segments\n const totalLength = audioSegments.reduce((acc, segment) => acc + segment.length, 0);\n console.log(`Total audio length: ${totalLength} bytes`);\n \n const combinedAudio = new Uint8Array(totalLength);\n \n let offset = 0;\n for (const segment of audioSegments) {\n combinedAudio.set(segment, offset);\n offset += segment.length;\n }\n \n console.log(\"Successfully combined all audio segments\");\n return combinedAudio;\n } catch (error) {\n console.error(\"Error in text-to-speech conversion:\", error);\n \n // Try fallback method\n try {\n console.log(\"Attempting fallback TTS method...\");\n return await fallbackTextToSpeech(script);\n } catch (fallbackError) {\n console.error(\"Fallback TTS also failed:\", fallbackError);\n throw new Error(`Text-to-speech conversion failed: ${error.message}`);\n }\n }\n}",[],[218],{"_107":226,"_100":227,"_151":228,"_153":-5,"_154":138,"_155":229,"_157":158,"_109":230,"_104":31,"_160":204,"_162":231,"_163":27,"_164":-5,"_165":232,"_103":233},"5247a39a-31ff-11f0-b2a7-569c3dd06744","wikipedia.ts","5247a462-31ff-11f0-b2a7-569c3dd06744",3,["D",1747363311526],"/**\n * Utility functions for fetching and processing Wikipedia articles\n */\n\ninterface WikipediaArticle {\n title: string;\n content: string;\n}\n\n/**\n * Fetches a Wikipedia article by title or URL\n * @param articleInput - Wikipedia article title or URL\n * @returns The article title and content\n */\nexport async function fetchWikipediaArticle(articleInput: string): Promise {\n // Extract title from URL if a URL is provided\n let title = articleInput;\n if (articleInput.includes(\"wikipedia.org/wiki/\")) {\n const urlParts = articleInput.split(\"/wiki/\");\n title = urlParts[urlParts.length - 1];\n }\n \n // Clean up the title\n title = decodeURIComponent(title.replace(/_/g, \" \"));\n \n // Fetch the article content using Wikipedia's API\n const apiUrl = `https://en.wikipedia.org/w/api.php?action=query&format=json&prop=extracts&exintro=0&explaintext=1&titles=${encodeURIComponent(title)}&origin=*`;\n \n const response = await fetch(apiUrl);\n if (!response.ok) {\n throw new Error(`Failed to fetch Wikipedia article: ${response.statusText}`);\n }\n \n const data = await response.json();\n const pages = data.query.pages;\n const pageId = Object.keys(pages)[0];\n \n if (pageId === \"-1\") {\n throw new Error(`Wikipedia article not found: ${title}`);\n }\n \n const page = pages[pageId];\n const content = page.extract;\n \n if (!content || content.trim() === \"\") {\n throw new Error(`No content found for Wikipedia article: ${title}`);\n }\n \n // Get the full article content\n const fullContentApiUrl = `https://en.wikipedia.org/w/api.php?action=query&format=json&prop=extracts&explaintext=1&titles=${encodeURIComponent(title)}&origin=*`;\n \n const fullContentResponse = await fetch(fullContentApiUrl);\n if (!fullContentResponse.ok) {\n throw new Error(`Failed to fetch full Wikipedia article: ${fullContentResponse.statusText}`);\n }\n \n const fullContentData = await fullContentResponse.json();\n const fullContentPages = fullContentData.query.pages;\n const fullContentPage = fullContentPages[pageId];\n const fullContent = fullContentPage.extract;\n \n return {\n title: page.title,\n content: fullContent\n };\n}\n\n/**\n * Truncates Wikipedia content to a reasonable length for processing\n * @param content - The full Wikipedia article content\n * @param maxLength - Maximum character length (default: 8000)\n * @returns Truncated content\n */\nexport function truncateContent(content: string, maxLength: number = 8000): string {\n if (content.length <= maxLength) {\n return content;\n }\n \n // Try to find a good breaking point (end of paragraph)\n const breakPoint = content.lastIndexOf(\"\\n\\n\", maxLength);\n if (breakPoint !== -1) {\n return content.substring(0, breakPoint) + \"\\n\\n[Content truncated due to length...]\";\n }\n \n // If no good breaking point, just truncate at maxLength\n return content.substring(0, maxLength) + \"... 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