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Ringurooma Koe API

Orginal name: リングローマの音声API

This repository is an intermediate microservice, communicating with the Azure Speech SDK for processing and assessing Japanese speaking skills, independently developed by the Ringurooma project. Therefore, any application that supports the REST API can use it!

Feature

  • Speech to Text: Recognize and convert Japanese speech to text
  • Pronunciation Assessment: Japanese pronunciation accuracy assessment on a 100-point scale
  • Intonation and Tone Analysis: Assessing the naturalness of intonation when speaking Japanese
  • Speaking speed assessment: Analyze and evaluate whether the speaking speed is appropriate or not
  • Vocabulary Accent Analysis: Evaluate the correct placement of accents in words
  • Strengths and weaknesses analysis: Identify strengths and weaknesses in speaking skills
  • Suggestions for improvement: Give suggestions to improve your Japanese speaking skills

API Endpoints

All API endpoints require authentication with an API key via the X-API-Key header or query parameter api_key.

GET /

  • Description: Check the operating status of the server
  • Response: Version and status information
  • Example response:
{
  "message": "Ringurooma Speech API Server",
  "version": "1.0.0",
  "status": "running",
  "features": [
    "Speech to Text (POST /api/speech-to-text)",
    "Real-time Speech to Text (WebSocket)",
    "Pronunciation Assessment (POST /api/pronunciation-assessment)",
    "Text to Speech (POST /api/text-to-speech)",
    "Intent Recognition (POST /api/intent-recognition)"
  ]
}

POST /api/speech-to-text

  • Description: Convert audio to text
  • Content-Type: multipart/form-data or application/json (if using base64)
  • Parameters:
    • File Usage:
      • audio: Audio file (WAV) - required
      • user_id: User ID - optional
    • Using base64:
      • audio_base64: Base64 string of audio - required
      • user_id: User ID - optional
      • Headers:
        • X-Audio-Format: base64 - required
  • Limit: Maximum file size 10MB
  • Response: Text recognized from audio
  • Example response:
{
  "user_id": "test-user-001",
  "transcription": "こんにちは、私の名前は田中です。",
  "timestamp": "2025-05-18T07:31:28.123Z"
}

POST /api/pronunciation-assessment

  • Description: Japanese pronunciation assessment based on sound and reference text
  • Content-Type: multipart/form-data or application/json (if using base64)
  • Parameters:
    • File Usage:
      • audio: Audio file (WAV) - required
      • reference_text: Japanese Reference Text - Required
      • user_id: User ID - optional
    • Using base64:
      • audio_base64: Base64 string of audio - required
      • reference_text: Japanese Reference Text - Required
      • user_id: User ID - optional
      • Headers:
        • X-Audio-Format: base64 - required
        • Content-Type: application/json - required
  • Limit: Maximum file size 10MB
  • Response: Detailed review results
  • Example response:
{
  "user_id": "test-user-001",
  "reference_text": "こんにちは、私の名前は田中です。",
  "transcription": {
    "fromRecognition": "こんにちは、私の名前は田中です。",
    "fromAssessment": "こんにちは、私の名前は田中です。"
  },
  "jlpt_level": "N3",
  "pronunciation_scores": {
    "accuracy": 85.7,
    "fluency": 79.3,
    "completeness": 95.0,
    "pronunciation": 82.5,
    "prosody": 75.2
  },
  "speech_rate": {
    "words_per_minute": 120,
    "assessment": {
      "rating": "good",
      "feedback": "Tốc độ nói phù hợp, gần với tốc độ nói tự nhiên của người bản xứ."
    }
  },
  "word_stress": {
    "overall_score": 76.5,
    "details": [...]
  },
  "analysis": {
    "strengths": ["Phát âm chính xác các từ vựng", "Nói đầy đủ nội dung so với văn bản tham chiếu"],
    "weaknesses": ["Ngữ điệu chưa tự nhiên, còn đơn điệu"],
    "improvement_suggestions": ["Nghe và bắt chước ngữ điệu của người bản xứ, tập trung vào cao độ và trọng âm"]
  },
  "word_details": [...],
  "phoneme_details": [...],
  "benchmark_comparison": {
    "accuracy_vs_benchmark": "5.70",
    "fluency_vs_benchmark": "-0.70",
    "overall_vs_benchmark": "2.50"
  },
  "timestamp": "2025-05-18T07:31:28.123Z"
}

POST /api/text-to-speech

  • Description: Text-to-speech conversion
  • Content-Type: application/json
  • Parameters:
    • text: Text that needs to be converted to speech - required
    • voice_name: Voice name (default: 'ja-JP-NanamiNeural') – optional
  • Response: MP3 audio file
  • Response Headers:
    • Content-Type: audio/mp3
    • Content-Disposition: attachment; filename="tts-[timestamp].mp3"

POST /api/intent-recognition

  • Description: Identify intent from text or audio
  • Content-Type: multipart/form-data or application/json
  • Parameters:
    • The input is text (application/json):
      • text: Text that needs intent identification - required
      • user_id: User ID - optional
    • Input is audio (multipart/form-data):
      • audio: Audio file (WAV) - required
      • user_id: User ID - optional
    • The input is base64 audio (application/json):
      • audio_base64: Base64 string of audio - required
      • user_id: User ID - optional
      • Headers:
        • X-Audio-Format: base64 - required
  • Limit: Maximum file size of 10MB (for audio input)
  • Response: Intent Recognition Results
  • Example response:
{
  "user_id": "test-user-001",
  "query": "こんにちは、初めまして。お願いします。",
  "intent": {
    "top": "Greeting",
    "confidence": 0.5
  },
  "intents": {
    "Greeting": 2,
    "Request": 1,
    "Farewell": 0,
    "Question": 0,
    "Affirmation": 0,
    "Negation": 0,
    "Opinion": 0
  },
  "entities": [],
  "timestamp": "2025-05-18T07:45:12.456Z"
}

WebSocket Endpoint - Real-Time Voice Recognition

  • Description: WebSocket connection for real-time speech recognition
  • Protocol: WebSocket (ws:// or wss://)
  • Process:
    1. Connecting to a WebSocket endpoint
    2. Send the start command: {"command": "start"}
    3. Submit audio data as binary data
    4. Get real-time recognition results
    5. Send stop command: {"command": "stop"}
  • Message format from the server:
{
  "type": "recognition",
  "result": {
    "type": "recognizing", // or "recognized"
    "text": "こんにちは",
    "isFinal": false // true if final result
  }
}

JLPT Level Mapping

The service automatically determines the JLPT level based on pronunciation scores:

JLPT Level Pronunciation Points
N1 90-100
N2 80-89
N3 70-79
N4 60-69
N5 <60

Install and run

Requirements

  • Node.js 14+
  • Azure Speech Service account
  • Internet connection to use the Azure Speech SDK

Manual Installation

  1. Clone repository
  2. Install dependencies: npm install
  3. Configure the environment: Create an .env file with the following variables:
SPEECH_KEY=your_azure_speech_key
SPEECH_REGION=your_azure_region
API_KEY=your_api_authentication_key
PORT=3000 (optional)
  1. Run the service: node server.js or npm start

Deploy with Docker

  1. Make sure Docker and Docker Compose are installed
  2. Create an .env file with the necessary environment variables
  3. Run: docker-compose up -d

SSL/HTTPS Configuration

Use setup-ssl.sh script to set up SSL with Let's Encrypt:

./setup-ssl.sh your-domain.com

Examples of using APIs with cURL

Convert speech to text (using file)

curl -X POST \
  -H "X-API-Key: your_api_key" \
  -F "audio=@/path/to/audio.wav" \
  -F "user_id=test-user" \
  https://your-domain.com/api/speech-to-text

Evaluation of pronunciation (using file)

curl -X POST \
  -H "X-API-Key: your_api_key" \
  -F "audio=@/path/to/audio.wav" \
  -F "reference_text=こんにちは、私の名前は田中です。" \
  -F "user_id=test-user" \
  https://your-domain.com/api/pronunciation-assessment

Pronunciation evaluation (using base64)

curl -X POST \
  -H "X-API-Key: your_api_key" \
  -H "Content-Type: application/json" \
  -H "X-Audio-Format: base64" \
  -d '{
    "audio_base64": "UklGRiSFAABXQVZFLAAAAAA=...",
    "reference_text": "こんにちは、私の名前は田中です。",
    "user_id": "test-user"
  }' \
  https://your-domain.com/api/pronunciation-assessment

Convert text to speech

curl -X POST \
  -H "X-API-Key: your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"text":"こんにちは、私の名前は田中です。", "voice_name":"ja-JP-NanamiNeural"}' \
  --output speech.mp3 \
  https://your-domain.com/api/text-to-speech

Identifying intents from text

curl -X POST \
  -H "X-API-Key: your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"text":"こんにちは、初めまして。お願いします。", "user_id":"test-user"}' \
  https://your-domain.com/api/intent-recognition

Identify intents from base64 audio

curl -X POST \
  -H "X-API-Key: your_api_key" \
  -H "Content-Type: application/json" \
  -H "X-Audio-Format: base64" \
  -d '{
    "audio_base64": "UklGRiSFAABXQVZFLAAAAAA=...",
    "user_id": "test-user"
  }' \
  https://your-domain.com/api/intent-recognition

API Testing

Use test-api.js script to test API endpoints:

# Test all APIs
node test-api.js --all

# Test each API separately
node test-api.js --stt           # Speech to Text
node test-api.js --pronunciation # Pronunciation Assessment
node test-api.js --pronunciation-base64 # Pronunciation Assessment với base64
node test-api.js --tts           # Text to Speech
node test-api.js --intent        # Intent Recognition
node test-api.js --realtime      # Real-time Speech Recognition (WebSocket)

References

About

A minimal REST API for Japanese Speech Services 🎙️

phdoanh.github.io/blog/misc/ringurooma-koe-api

Topics

nodejs javascript docker-compose azure japanese api-rest speech-services sdk-js

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