Using Context

INWORLD_API_KEY=<your_api_key>

import 'dotenv/config';

import * as fs from 'fs';
import * as path from 'path';

import {
  CustomNode,
  SequentialGraphBuilder,
  GraphTypes,
  ProcessContext,
  RemoteLLMChatNode,
  RemoteTTSNode,
} from '@inworld/runtime/graph';
import { renderJinja } from '@inworld/runtime/primitives/llm';
// @ts-ignore
import * as wavEncoder from 'wav-encoder';

const voices = [
  {
    name: "Leroy",
    language: "en-US",
    speakerId: "Alex", // English voice ID for TTS
  },
  {
    name: "Gael", 
    language: "es-ES",
    speakerId: "Diego", // Spanish voice ID for TTS
  },
  {
    name: "Yuki",
    language: "ja-JP",
    speakerId: "Asuka", // Japanese voice ID for TTS
  }
];

// Jinja template for generating culturally-appropriate introductions
const introductionPrompt = `
A character named {{name}} needs to introduce themselves in {{language}}.
Generate a natural, culturally appropriate self-introduction in that language.

The introduction should:
- Be 1-2 sentences long
- Include their name
- Be friendly and conversational
- Be appropriate for the specified language and culture

Return ONLY the introduction text in the specified language, nothing else.
`;

class PromptBuilderNode extends CustomNode {
  async process(
    context: ProcessContext,
    input: { name: string; language: string; speakerId: string }
  ): Promise<GraphTypes.LLMChatRequest> {
    // Store voice data in ProcessContext so the TTS node can access it later
    const datastore = context.getDatastore();
    datastore.add('voiceData', {
      name: input.name,
      language: input.language,
      speakerId: input.speakerId,
    });
    
    // Render the Jinja template with speaker data
    const renderedPrompt = await renderJinja(introductionPrompt, JSON.stringify({
      name: input.name,
      language: input.language,
    }));
    
    console.log(`\n[${input.name}] Generating introduction in ${input.language}...`);
    
    // Return LLM chat request
    return new GraphTypes.LLMChatRequest({
      messages: [
        {
          role: 'user',
          content: renderedPrompt,
        },
      ],
    });
  }
}

class TextExtractorNode extends CustomNode {
  process(
    context: ProcessContext,
    input: GraphTypes.Content
  ): string {
    const datastore = context.getDatastore();
    const voiceData = datastore.get('voiceData');
    const introText = input.content || '';
    
    console.log(`[${voiceData?.name}] Generated text: "${introText}"`);
    
    // Return the text content for TTS processing
    return introText;
  }
}

async function processVoice(
  voice: { name: string; language: string; speakerId: string },
  apiKey: string,
  outputDirectory: string
): Promise<void> {
  console.log(`\nStarting ${voice.name} (${voice.language}, voice: ${voice.speakerId})`);
  
  const promptBuilderNode = new PromptBuilderNode({
    id: `prompt-builder-${voice.name}`,
  });
  
  const llmNode = new RemoteLLMChatNode({
    id: `llm-node-${voice.name}`,
    modelName: 'gpt-4o-mini',
    provider: 'openai',
    textGenerationConfig: {
      maxNewTokens: 200,
      temperature: 1.1,
    },
  });
  
  const textExtractorNode = new TextExtractorNode({
    id: `text-extractor-${voice.name}`,
  });
  
  const ttsNode = new RemoteTTSNode({
    id: `tts-node-${voice.name}`,
    speakerId: voice.speakerId,
    modelId: 'inworld-tts-1-max',
    sampleRate: 48000,
    temperature: 1.1,
    speakingRate: 1,
  });
  
  const graph = new SequentialGraphBuilder({
    id: `voice-graph-${voice.name}`,
    apiKey,
    enableRemoteConfig: false,
    nodes: [
        promptBuilderNode,
        llmNode,
        textExtractorNode,
        ttsNode,
    ]
  })

  const executor = graph.build();

  try {
    // Start the graph execution with the voice configuration as input
    const { outputStream } = await executor.start(voice);
    
    let allAudioData: number[] = [];
    let processedText = '';
    
    // Process the output stream
    for await (const result of outputStream) {
      await result.processResponse({
        // Handle TTS output stream
        TTSOutputStream: async (ttsStream: GraphTypes.TTSOutputStream) => {
          console.log(`[${voice.name}] Generating audio with voice ${voice.speakerId}...`);
          
          // Collect audio chunks from the TTS stream
          for await (const chunk of ttsStream) {
            if (chunk.text) {
              processedText += chunk.text;
            }
            if (chunk.audio?.data) {
              allAudioData = allAudioData.concat(Array.from(chunk.audio.data));
            }
          }
        },
      });
    }
    
    // Save audio to WAV file
    if (allAudioData.length > 0) {
      const audio = {
        sampleRate: 48000,
        channelData: [new Float32Array(allAudioData)],
      };
      
      const buffer = await wavEncoder.encode(audio);
      const outputPath = path.join(
        outputDirectory,
        `${voice.name}_${voice.language}_introduction.wav`
      );
      
      fs.writeFileSync(outputPath, Buffer.from(buffer));
      
      console.log(`[${voice.name}] ✓ Audio saved to: ${outputPath}`);
      console.log(`[${voice.name}]   Duration: ~${(allAudioData.length / 48000).toFixed(1)}s`);
    }
  } catch (error) {
    console.error(`[${voice.name}] Error during processing:`, error);
    throw error;
  }
}

/**
 * Main function that demonstrates parallel graph execution
 * All voice configurations are processed simultaneously
 */
async function main() {
  const apiKey = process.env.INWORLD_API_KEY || '';
  
  if (!apiKey) {
    throw new Error('Please set INWORLD_API_KEY environment variable');
  }
  
  const OUTPUT_DIRECTORY = path.join(__dirname, 'audio_output');
  
  // Ensure output directory exists
  if (!fs.existsSync(OUTPUT_DIRECTORY)) {
    fs.mkdirSync(OUTPUT_DIRECTORY, { recursive: true });
  }
  
  const startTime = Date.now();
  

  // Process in parallel
  const processingPromises = voices.map(voice => 
    processVoice(voice, apiKey, OUTPUT_DIRECTORY).catch(error => {
      console.error(`Failed to process ${voice.name}:`, error.message);
      return null;
    })
  );
  
  await Promise.all(processingPromises);
  
  const duration = ((Date.now() - startTime) / 1000).toFixed(1);
  
  console.log('\n' + '='.repeat(60));
  console.log(`All voices completed in ${duration}s (parallel execution)`);
  console.log(`Audio files saved in: ${OUTPUT_DIRECTORY}`);
  console.log('='.repeat(60) + '\n');
}

// Run the main function
main().catch((error) => {
  console.error('Error:', error);
  process.exit(1);
});