/ASR

name: ASR
description: Implement speech-to-text (ASR/automatic speech recognition) capabilities using the z-ai-web-dev-sdk. Use this skill when the user needs to transcribe audio files, convert speech to text, build voice input features, or process audio recordings. Supports base64 encoded audio files and returns accurate text transcriptions.
license: MIT

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 = requi