Create IBM Granite Speech Models MCP Server

## Overview
Create a comprehensive MCP Server for IBM Granite Speech Models supporting speech recognition, translation, synthesis, and audio processing with multi-language capabilities and enterprise-grade features.

## Server Specifications

### **Server Details**
- **Name**: `granite-speech-server`
- **Language**: Python 3.11+
- **Location**: `mcp-servers/python/granite_speech_server/`
- **Purpose**: Provide access to IBM Granite speech models via MCP

### **Supported Models**
From [IBM Granite Speech on Hugging Face](https://huggingface.co/ibm-granite):
- **Speech Recognition**: granite-speech-asr-en, granite-whisper-multilingual
- **Translation**: granite-speech-translate-en-es, granite-speech-translate-en-fr
- **Synthesis**: granite-speech-tts-v1, granite-voice-cloning
- **Processing**: granite-speech-enhancement, granite-audio-classification

### **Language Support**
English to 7+ languages: Spanish, French, German, Italian, Portuguese, Japanese, Chinese

### **Provider Support**
- **Ollama**: Local inference with speech models
- **watsonx.ai**: IBM's enterprise speech services
- **Hugging Face**: Direct access to speech transformers
- **Custom Endpoints**: Flexible audio API integration

### **Tools Provided**

#### 1. `transcribe_audio`
Speech-to-text transcription with language detection
```python
@dataclass
class TranscriptionRequest:
    audio_data: str  # base64, file path, or URL
    model: str = "granite-speech-asr-en"
    provider: str = "ollama"
    language: str = "auto"  # auto-detect or specify
    enable_timestamps: bool = True
    speaker_diarization: bool = False
    noise_reduction: bool = True
    output_format: str = "text"  # text, srt, vtt, json
```

#### 2. `translate_speech`
Real-time speech translation between languages
```python
@dataclass
class SpeechTranslationRequest:
    audio_data: str
    model: str = "granite-speech-translate-en-es"
    provider: str = "watsonx"
    source_language: str = "en"
    target_language: str = "es"
    preserve_speaker_style: bool = True
    include_confidence: bool = True
    streaming: bool = False
```

#### 3. `synthesize_speech`
Text-to-speech synthesis with voice customization
```python
@dataclass
class SpeechSynthesisRequest:
    text: str
    model: str = "granite-speech-tts-v1"
    provider: str = "huggingface"
    voice: str = "default"  # voice profile
    language: str = "en"
    speed: float = 1.0
    pitch: float = 1.0
    emotion: str = "neutral"  # neutral, happy, sad, excited
    output_format: str = "wav"  # wav, mp3, ogg
```

#### 4. `enhance_audio`
Audio enhancement and noise reduction
```python
@dataclass
class AudioEnhancementRequest:
    audio_data: str
    model: str = "granite-speech-enhancement"
    provider: str = "ollama"
    enhancement_type: str = "denoise"  # denoise, enhance, normalize, amplify
    preserve_speech: bool = True
    aggressive_filtering: bool = False
    output_quality: str = "high"  # low, medium, high
```

#### 5. `detect_language`
Audio language identification and classification
```python
@dataclass
class LanguageDetectionRequest:
    audio_data: str
    model: str = "granite-audio-classification"
    provider: str = "watsonx"
    confidence_threshold: float = 0.8
    return_all_probabilities: bool = False
    segment_analysis: bool = False  # Analyze segments for language switching
```

#### 6. `analyze_speaker`
Speaker identification and voice characteristics
```python
@dataclass
class SpeakerAnalysisRequest:
    audio_data: str
    model: str = "granite-audio-classification"
    provider: str = "huggingface"
    analysis_type: str = "speaker_id"  # speaker_id, emotion, gender, age
    reference_speakers: Optional[List[str]] = None
    include_embeddings: bool = False
```

#### 7. `real_time_transcription`
Streaming audio transcription and processing
```python
@dataclass
class StreamingTranscriptionRequest:
    audio_stream: str  # streaming audio source
    model: str = "granite-speech-asr-en"
    provider: str = "ollama"
    chunk_duration: float = 2.0  # seconds
    overlap_duration: float = 0.5
    language: str = "auto"
    live_translation: Optional[str] = None  # target language
```

#### 8. `batch_audio_processing`
Efficient batch processing of multiple audio files
```python
@dataclass
class BatchAudioRequest:
    audio_files: List[str]
    model: str = "granite-speech-asr-en"
    provider: str = "watsonx"
    processing_type: str = "transcribe"  # transcribe, translate, enhance, analyze
    parallel_processing: bool = True
    max_concurrent: int = 4
    output_format: str = "json"
```

### **Implementation Requirements**

#### Directory Structure
```
mcp-servers/python/granite_speech_server/
├── src/
│   └── granite_speech_server/
│       ├── __init__.py
│       ├── server.py
│       ├── providers/
│       │   ├── __init__.py
│       │   ├── ollama_speech.py
│       │   ├── watsonx_speech.py
│       │   ├── huggingface_speech.py
│       │   └── streaming_client.py
│       ├── models/
│       │   ├── __init__.py
│       │   ├── granite_speech_models.py
│       │   └── language_models.py
│       ├── processing/
│       │   ├── __init__.py
│       │   ├── audio_processor.py
│       │   ├── transcription.py
│       │   ├── translation.py
│       │   ├── synthesis.py
│       │   └── enhancement.py
│       ├── tools/
│       │   ├── __init__.py
│       │   ├── speech_recognition.py
│       │   ├── speech_translation.py
│       │   ├── text_to_speech.py
│       │   └── audio_analysis.py
│       └── utils/
│           ├── __init__.py
│           ├── audio_utils.py
│           ├── format_converters.py
│           └── streaming_utils.py
├── tests/
├── requirements.txt
├── README.md
└── examples/
    ├── transcription_example.py
    ├── translation_example.py
    └── real_time_processing.py
```

#### Dependencies
```python
# requirements.txt
mcp>=1.0.0
transformers>=4.35.0
torch>=2.1.0
torchaudio>=2.1.0
librosa>=0.10.0
soundfile>=0.12.1
pyaudio>=0.2.11
speechrecognition>=3.10.0
pydub>=0.25.1
webrtcvad>=2.0.10
requests>=2.31.0
pydantic>=2.5.0
ollama>=0.1.7
ibm-watson>=7.0.0
numpy>=1.24.0
scipy>=1.11.0
```

### **Configuration**
```yaml
# config.yaml
providers:
  ollama:
    base_url: "http://localhost:11434"
    speech_models_enabled: true
    timeout: 300
    
  watsonx:
    url: "https://api.us-south.speech-to-text.watson.cloud.ibm.com"
    apikey: "${WATSONX_SPEECH_API_KEY}"
    region: "us-south"
    
  huggingface:
    api_key: "${HF_API_KEY}"
    cache_dir: "./hf_speech_cache"
    device: "auto"

models:
  default_asr: "granite-speech-asr-en"
  default_translation: "granite-speech-translate-en-es"
  default_tts: "granite-speech-tts-v1"
  default_enhancement: "granite-speech-enhancement"

audio:
  sample_rate: 16000
  channels: 1
  max_file_size: "100MB"
  supported_formats: ["wav", "mp3", "ogg", "flac", "m4a"]
  temp_dir: "./temp_audio"
  chunk_size: 1024

processing:
  enable_gpu: true
  max_concurrent_requests: 6
  streaming_buffer_size: 4096
  silence_threshold: 0.01
  
languages:
  supported: ["en", "es", "fr", "de", "it", "pt", "ja", "zh"]
  auto_detection: true
  confidence_threshold: 0.7
```

### **Usage Examples**

#### Audio Transcription
```python
# Transcribe audio file to text
result = await mcp_client.call_tool("transcribe_audio", {
    "audio_data": "./meeting_recording.wav",
    "model": "granite-speech-asr-en",
    "provider": "ollama",
    "language": "auto",
    "enable_timestamps": True,
    "speaker_diarization": True,
    "output_format": "srt"
})
```

#### Speech Translation
```python
# Translate speech from English to Spanish
result = await mcp_client.call_tool("translate_speech", {
    "audio_data": "data:audio/wav;base64,UklGRnoGAABXQVZFZm10IBAAAAABAAEA...",
    "model": "granite-speech-translate-en-es",
    "provider": "watsonx",
    "source_language": "en",
    "target_language": "es",
    "include_confidence": True
})
```

#### Text-to-Speech Synthesis
```python
# Convert text to natural speech
result = await mcp_client.call_tool("synthesize_speech", {
    "text": "Welcome to our multilingual customer service. How may I help you today?",
    "model": "granite-speech-tts-v1",
    "provider": "huggingface",
    "voice": "professional_female",
    "language": "en",
    "emotion": "friendly",
    "output_format": "mp3"
})
```

#### Real-time Processing
```python
# Real-time speech transcription
result = await mcp_client.call_tool("real_time_transcription", {
    "audio_stream": "rtmp://live.example.com/stream",
    "model": "granite-speech-asr-en",
    "provider": "ollama",
    "chunk_duration": 2.0,
    "language": "auto",
    "live_translation": "es"
})
```

#### Audio Enhancement
```python
# Enhance audio quality
result = await mcp_client.call_tool("enhance_audio", {
    "audio_data": "./noisy_recording.wav",
    "model": "granite-speech-enhancement",
    "provider": "watsonx",
    "enhancement_type": "denoise",
    "preserve_speech": True,
    "output_quality": "high"
})
```

#### Batch Processing
```python
# Process multiple audio files
result = await mcp_client.call_tool("batch_audio_processing", {
    "audio_files": [
        "./audio1.wav",
        "./audio2.mp3", 
        "./audio3.flac"
    ],
    "model": "granite-speech-asr-en",
    "provider": "ollama",
    "processing_type": "transcribe",
    "parallel_processing": True,
    "output_format": "json"
})
```

### **Advanced Features**
- **Real-time Streaming**: Live audio processing and transcription
- **Speaker Diarization**: Multi-speaker identification and separation
- **Emotion Recognition**: Detect emotional states in speech
- **Voice Cloning**: Custom voice synthesis from samples
- **Audio Fingerprinting**: Content identification and matching
- **Noise Cancellation**: Advanced audio enhancement

### **Enterprise Features**
- **High Accuracy Models**: Enterprise-optimized speech recognition
- **Custom Vocabulary**: Domain-specific terminology support
- **Compliance**: SOX, GDPR compliance for audio data
- **Scalability**: Handle high-volume audio processing
- **Integration**: APIs for call center and meeting platforms
- **Security**: Encrypted audio processing and storage

### **Performance Optimizations**
- **GPU Acceleration**: CUDA support for faster processing
- **Streaming Architecture**: Minimize latency for real-time applications
- **Model Quantization**: Optimized models for deployment
- **Caching**: Intelligent caching of processed audio segments
- **Batch Processing**: Efficient multi-file processing

## Acceptance Criteria
- [ ] Python MCP server with 8+ Granite speech model tools
- [ ] Support for all major Granite speech models
- [ ] Multi-provider integration (Ollama, watsonx.ai, Hugging Face)
- [ ] Speech recognition with multi-language support
- [ ] Speech translation capabilities (English to 7+ languages)
- [ ] Text-to-speech synthesis with voice customization
- [ ] Real-time streaming audio processing
- [ ] Audio enhancement and noise reduction
- [ ] Batch processing for efficiency
- [ ] Multi-format audio support
- [ ] Comprehensive test suite with sample audio (>90% coverage)
- [ ] Complete documentation with speech processing examples

## Priority
High - Demonstrates IBM's advanced speech AI capabilities via MCP

## Use Cases
- Multilingual customer service and support
- Real-time meeting transcription and translation
- Voice-enabled applications and assistants
- Audio content accessibility and processing
- Call center analytics and quality monitoring
- Language learning and pronunciation tools
- Audio content localization and dubbing
- Voice biometrics and speaker verification

Create IBM Granite Speech Models MCP Server #907

Description

Overview

Server Specifications

Server Details

Supported Models

Language Support

Provider Support

Tools Provided

1. transcribe_audio

2. translate_speech

3. synthesize_speech

4. enhance_audio

5. detect_language

6. analyze_speaker

7. real_time_transcription

8. batch_audio_processing

Implementation Requirements

Directory Structure

Dependencies

Configuration

Usage Examples

Audio Transcription

Speech Translation

Text-to-Speech Synthesis

Real-time Processing

Audio Enhancement

Batch Processing

Advanced Features

Enterprise Features

Performance Optimizations

Acceptance Criteria

Priority

Use Cases

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions

1. `transcribe_audio`

2. `translate_speech`

3. `synthesize_speech`

4. `enhance_audio`

5. `detect_language`

6. `analyze_speaker`

7. `real_time_transcription`

8. `batch_audio_processing`