Asr

ASR (Speech to Text) Skill

This skill guides the implementation of speech-to-text (ASR) functionality using the z-ai-web-dev-sdk package, enabling accurate transcription of spoken audio into text.

Skills Path

Skill Location: {project_path}/skills/ASR

this skill is located at above path in your project.

Reference Scripts: Example test scripts are available in the {Skill Location}/scripts/ directory for quick testing and reference. See {Skill Location}/scripts/asr.ts for a working example.

Overview

Speech-to-Text (ASR - Automatic Speech Recognition) allows you to build applications that convert spoken language in audio files into written text, enabling voice-controlled interfaces, transcription services, and audio content analysis.

IMPORTANT: z-ai-web-dev-sdk MUST be used in backend code only. Never use it in client-side code.

Prerequisites

The z-ai-web-dev-sdk package is already installed. Import it as shown in the examples below.

CLI Usage (For Simple Tasks)

For simple audio transcription tasks, you can use the z-ai CLI instead of writing code. This is ideal for quick transcriptions, testing audio files, or batch processing.

Basic Transcription from File

# Transcribe an audio file
z-ai asr --file ./audio.wav

# Save transcription to JSON file
z-ai asr -f ./recording.mp3 -o transcript.json

# Transcribe and view output
z-ai asr --file ./interview.wav --output result.json

Transcription from Base64

# Transcribe from base64 encoded audio
z-ai asr --base64 "UklGRiQAAABXQVZFZm10..." -o result.json

# Using short option
z-ai asr -b "base64_encoded_audio_data" -o transcript.json

Streaming Output

# Stream transcription results
z-ai asr -f ./audio.wav --stream

CLI Parameters

• --file, -f <path>: Required (if not using --base64) - Audio file path
• --base64, -b <base64>: Required (if not using --file) - Base64 encoded audio
• --output, -o <path>: Optional - Output file path (JSON format)
• --stream: Optional - Stream the transcription output

Supported Audio Formats

The ASR service supports various audio formats including:

• WAV (.wav)
• MP3 (.mp3)
• Other common audio formats

When to Use CLI vs SDK

Use CLI for:

• Quick audio file transcriptions
• Testing audio recognition accuracy
• Simple batch processing scripts
• One-off transcription tasks

Use SDK for:

• Real-time audio transcription in applications
• Integration with recording systems
• Custom audio processing workflows
• Production applications with streaming audio

Basic ASR Implementation

Simple Audio Transcription

import ZAI from 'z-ai-web-dev-sdk';
import fs from 'fs';

async function transcribeAudio(audioFilePath) {
  const zai = await ZAI.create();

  // Read audio file and convert to base64
  const audioFile = fs.readFileSync(audioFilePath);
  const base64Audio = audioFile.toString('base64');

  const response = await zai.audio.asr.create({
    file_base64: base64Audio
  });

  return response.text;
}

// Usage
const transcription = await transcribeAudio('./audio.wav');
console.log('Transcription:', transcription);

Transcribe Multiple Audio Files

import ZAI from 'z-ai-web-dev-sdk';
import fs from 'fs';

async function transcribeBatch(audioFilePaths) {
  const zai = await ZAI.create();
  const results = [];

  for (const filePath of audioFilePaths) {
    try {
      const audioFile = fs.readFileSync(filePath);
      const base64Audio = audioFile.toString('base64');

      const response = await zai.audio.asr.create({
        file_base64: base64Audio
      });

      results.push({
        file: filePath,
        success: true,
        transcription: response.text
      });
    } catch (error) {
      results.push({
        file: filePath,
        success: false,
        error: error.message
      });
    }
  }

  return results;
}

// Usage
const files = ['./interview1.wav', './interview2.wav', './interview3.wav'];
const transcriptions = await transcribeBatch(files);

transcriptions.forEach(result => {
  if (result.success) {
    console.log(`${result.file}: ${result.transcription}`);
  } else {
    console.error(`${result.file}: Error - ${result.error}`);
  }
});

Advanced Use Cases

Audio File Processing with Metadata

import ZAI from 'z-ai-web-dev-sdk';
import fs from 'fs';
import path from 'path';

async function transcribeWithMetadata(audioFilePath) {
  const zai = await ZAI.create();

  // Get file metadata
  const stats = fs.statSync(audioFilePath);
  const audioFile = fs.readFileSync(audioFilePath);
  const base64Audio = audioFile.toString('base64');

  const startTime = Date.now();

  const response = await zai.audio.asr.create({
    file_base64: base64Audio
  });

  const endTime = Date.now();

  return {
    filename: path.basename(audioFilePath),
    filepath: audioFilePath,
    fileSize: stats.size,
    transcription: response.text,
    wordCount: response.text.split(/\s+/).length,
    processingTime: endTime - startTime,
    timestamp: new Date().toISOString()
  };
}

// Usage
const result = await transcribeWithMetadata('./meeting_recording.wav');
console.log('Transcription Details:', JSON.stringify(result, null, 2));

Real-time Audio Processing Service

import ZAI from 'z-ai-web-dev-sdk';
import fs from 'fs';

class ASRService {
  constructor() {
    this.zai = null;
    this.transcriptionCache = new Map();
  }

  async initialize() {
    this.zai = await ZAI.create();
  }

  generateCacheKey(audioBuffer) {
    const crypto = require('crypto');
    return crypto.createHash('md5').update(audioBuffer).digest('hex');
  }

  async transcribe(audioFilePath, useCache = true) {
    const audioBuffer = fs.readFileSync(audioFilePath);
    const cacheKey = this.generateCacheKey(audioBuffer);

    // Check cache
    if (useCache && this.transcriptionCache.has(cacheKey)) {
      return {
        transcription: this.transcriptionCache.get(cacheKey),
        cached: true
      };
    }

    // Transcribe audio
    const base64Audio = audioBuffer.toString('base64');

    const response = await this.zai.audio.asr.create({
      file_base64: base64Audio
    });

    // Cache result
    if (useCache) {
      this.transcriptionCache.set(cacheKey, response.text);
    }

    return {
      transcription: response.text,
      cached: false
    };
  }

  clearCache() {
    this.transcriptionCache.clear();
  }

  getCacheSize() {
    return this.transcriptionCache.size;
  }
}

// Usage
const asrService = new ASRService();
await asrService.initialize();

const result1 = await asrService.transcribe('./audio.wav');
console.log('First call (not cached):', result1);

const result2 = await asrService.transcribe('./audio.wav');
console.log('Second call (cached):', result2);

Directory Transcription

import ZAI from 'z-ai-web-dev-sdk';
import fs from 'fs';
import path from 'path';

async function transcribeDirectory(directoryPath, outputJsonPath) {
  const zai = await ZAI.create();

  // Get all audio files
  const files = fs.readdirSync(directoryPath);
  const audioFiles = files.filter(file => 
    /\.(wav|mp3|m4a|flac|ogg)$/i.test(file)
  );

  const results = {
    directory: directoryPath,
    totalFiles: audioFiles.length,
    processedAt: new Date().toISOString(),
    transcriptions: []
  };

  for (const filename of audioFiles) {
    const filePath = path.join(directoryPath, filename);

    try {
      const audioFile = fs.readFileSync(filePath);
      const base64Audio = audioFile.toString('base64');

      const response = await zai.audio.asr.create({
        file_base64: base64Audio
      });

      results.transcriptions.push({
        filename: filename,
        success: true,
        text: response.text,
        wordCount: response.text.split(/\s+/).length
      });

      console.log(`✓ Transcribed: ${filename}`);
    } catch (error) {
      results.transcriptions.push({
        filename: filename,
        success: false,
        error: error.message
      });

      console.error(`✗ Failed: ${filename} - ${error.message}`);
    }
  }

  // Save results to JSON
  fs.writeFileSync(
    outputJsonPath,
    JSON.stringify(results, null, 2)
  );

  return results;
}

// Usage
const results = await transcribeDirectory(
  './audio-recordings',
  './transcriptions.json'
);

console.log(`\nProcessed ${results.totalFiles} files`);
console.log(`Successful: ${results.transcriptions.filter(t => t.success).length}`);
console.log(`Failed: ${results.transcriptions.filter(t => !t.success).length}`);

Best Practices

1. Audio Format Handling

import ZAI from 'z-ai-web-dev-sdk';
import fs from 'fs';

async function transcribeAnyFormat(audioFilePath) {
  // Supported formats: WAV, MP3, M4A, FLAC, OGG, etc.
  const validExtensions = ['.wav', '.mp3', '.m4a', '.flac', '.ogg'];
  const ext = audioFilePath.toLowerCase().substring(audioFilePath.lastIndexOf('.'));

  if (!validExtensions.includes(ext)) {
    throw new Error(`Unsupported audio format: ${ext}`);
  }

  const zai = await ZAI.create();
  const audioFile = fs.readFileSync(audioFilePath);
  const base64Audio = audioFile.toString('base64');

  const response = await zai.audio.asr.create({
    file_base64: base64Audio
  });

  return response.text;
}

2. Error Handling

import ZAI from 'z-ai-web-dev-sdk';
import fs from 'fs';

async function safeTranscribe(audioFilePath) {
  try {
    // Validate file exists
    if (!fs.existsSync(audioFilePath)) {
      throw new Error(`File not found: ${audioFilePath}`);
    }

    // Check file size (e.g., limit to 100MB)
    const stats = fs.statSync(audioFilePath);
    const fileSizeMB = stats.size / (1024 * 1024);
    
    if (fileSizeMB > 100) {
      throw new Error(`File too large: ${fileSizeMB.toFixed(2)}MB (max 100MB)`);
    }

    // Transcribe
    const zai = await ZAI.create();
    const audioFile = fs.readFileSync(audioFilePath);
    const base64Audio = audioFile.toString('base64');

    const response = await zai.audio.asr.create({
      file_base64: base64Audio
    });

    if (!response.text || response.text.trim().length === 0) {
      throw new Error('Empty transcription result');
    }

    return {
      success: true,
      transcription: response.text,
      filePath: audioFilePath,
      fileSize: stats.size
    };
  } catch (error) {
    console.error('Transcription error:', error);
    return {
      success: false,
      error: error.message,
      filePath: audioFilePath
    };
  }
}

3. Post-Processing Transcriptions

function cleanTranscription(text) {
  // Remove excessive whitespace
  text = text.replace(/\s+/g, ' ').trim();

  // Capitalize first letter of sentences
  text = text.replace(/(^\w|[.!?]\s+\w)/g, match => match.toUpperCase());

  // Remove filler words (optional)
  const fillers = ['um', 'uh', 'ah', 'like', 'you know'];
  const fillerPattern = new RegExp(`\\b(${fillers.join('|')})\\b`, 'gi');
  text = text.replace(fillerPattern, '').replace(/\s+/g, ' ');

  return text;
}

async function transcribeAndClean(audioFilePath) {
  const zai = await ZAI.create();
  
  const audioFile = fs.readFileSync(audioFilePath);
  const base64Audio = audioFile.toString('base64');

  const response = await zai.audio.asr.create({
    file_base64: base64Audio
  });

  return {
    raw: response.text,
    cleaned: cleanTranscription(response.text)
  };
}

Common Use Cases

1. Meeting Transcription: Convert recorded meetings into searchable text
2. Interview Processing: Transcribe interviews for analysis and documentation
3. Podcast Transcription: Create text versions of podcast episodes
4. Voice Notes: Convert voice memos to text for easier reference
5. Call Center Analytics: Analyze customer service calls
6. Accessibility: Provide text alternatives for audio content
7. Voice Commands: Enable voice-controlled applications
8. Language Learning: Transcribe pronunciation practice

Integration Examples

Express.js API Endpoint

import express from 'express';
import multer from 'multer';
import ZAI from 'z-ai-web-dev-sdk';
import fs from 'fs';

const app = express();
const upload = multer({ dest: 'uploads/' });

let zaiInstance;

async function initZAI() {
  zaiInstance = await ZAI.create();
}

app.post('/api/transcribe', upload.single('audio'), async (req, res) => {
  try {
    if (!req.file) {
      return res.status(400).json({ error: 'No audio file provided' });
    }

    const audioFile = fs.readFileSync(req.file.path);
    const base64Audio = audioFile.toString('base64');

    const response = await zaiInstance.audio.asr.create({
      file_base64: base64Audio
    });

    // Clean up uploaded file
    fs.unlinkSync(req.file.path);

    res.json({
      success: true,
      transcription: response.text,
      wordCount: response.text.split(/\s+/).length
    });
  } catch (error) {
    // Clean up on error
    if (req.file && fs.existsSync(req.file.path)) {
      fs.unlinkSync(req.file.path);
    }

    res.status(500).json({
      success: false,
      error: error.message
    });
  }
});

initZAI().then(() => {
  app.listen(3000, () => {
    console.log('ASR API running on port 3000');
  });
});

Troubleshooting

Issue: "SDK must be used in backend"

• Solution: Ensure z-ai-web-dev-sdk is only imported in server-side code

Issue: Empty or incorrect transcription

• Solution: Verify audio quality and format. Check if audio contains clear speech

Issue: Large file processing fails

• Solution: Consider splitting large audio files into smaller segments

Issue: Slow transcription speed

• Solution: Implement caching for repeated transcriptions, optimize file sizes

Issue: Memory errors with large files

• Solution: Process files in chunks or increase Node.js memory limit

Performance Tips

1. Reuse SDK Instance: Create once, use multiple times
2. Implement Caching: Cache transcriptions for duplicate files
3. Batch Processing: Process multiple files efficiently with proper queuing
4. Audio Optimization: Compress audio files before processing when possible
5. Async Operations: Use Promise.all for parallel processing when appropriate

Audio Quality Guidelines

For best transcription results:

• Sample Rate: 16kHz or higher
• Format: WAV, MP3, or M4A recommended
• Noise Level: Minimize background noise
• Speech Clarity: Clear pronunciation and normal speaking pace
• File Size: Under 100MB recommended for individual files

Remember

• Always use z-ai-web-dev-sdk in backend code only
• The SDK is already installed - import as shown in examples
• Audio files must be converted to base64 before processing
• Implement proper error handling for production applications
• Consider audio quality for best transcription accuracy
• Clean up temporary files after processing
• Cache results for frequently transcribed files