feat: add marketplace metrics, privacy features, and service registry endpoints
- Add Prometheus metrics for marketplace API throughput and error rates with new dashboard panels - Implement confidential transaction models with encryption support and access control - Add key management system with registration, rotation, and audit logging - Create services and registry routers for service discovery and management - Integrate ZK proof generation for privacy-preserving receipts - Add metrics instru
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oib
215
apps/miner-node/plugins/whisper.py
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215
apps/miner-node/plugins/whisper.py
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"""
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Whisper speech recognition plugin
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"""
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import asyncio
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import os
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import tempfile
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from typing import Dict, Any, List
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import time
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from .base import GPUPlugin, PluginResult
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from .exceptions import PluginExecutionError
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class WhisperPlugin(GPUPlugin):
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"""Plugin for Whisper speech recognition"""
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def __init__(self):
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super().__init__()
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self.service_id = "whisper"
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self.name = "Whisper Speech Recognition"
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self.version = "1.0.0"
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self.description = "Transcribe and translate audio files using OpenAI Whisper"
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self.capabilities = ["transcribe", "translate"]
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self._model_cache = {}
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def setup(self) -> None:
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"""Initialize Whisper dependencies"""
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super().setup()
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# Check for whisper installation
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try:
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import whisper
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self.whisper = whisper
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except ImportError:
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raise PluginExecutionError("Whisper not installed. Install with: pip install openai-whisper")
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# Check for ffmpeg
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import subprocess
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try:
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subprocess.run(["ffmpeg", "-version"], capture_output=True, check=True)
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except (subprocess.CalledProcessError, FileNotFoundError):
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raise PluginExecutionError("FFmpeg not found. Install FFmpeg for audio processing")
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def validate_request(self, request: Dict[str, Any]) -> List[str]:
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"""Validate Whisper request parameters"""
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errors = []
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# Check required parameters
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if "audio_url" not in request and "audio_file" not in request:
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errors.append("Either 'audio_url' or 'audio_file' must be provided")
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# Validate model
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model = request.get("model", "base")
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valid_models = ["tiny", "base", "small", "medium", "large", "large-v2", "large-v3"]
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if model not in valid_models:
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errors.append(f"Invalid model. Must be one of: {', '.join(valid_models)}")
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# Validate task
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task = request.get("task", "transcribe")
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if task not in ["transcribe", "translate"]:
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errors.append("Task must be 'transcribe' or 'translate'")
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# Validate language
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if "language" in request:
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language = request["language"]
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if not isinstance(language, str) or len(language) != 2:
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errors.append("Language must be a 2-letter language code (e.g., 'en', 'es')")
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return errors
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def get_hardware_requirements(self) -> Dict[str, Any]:
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"""Get hardware requirements for Whisper"""
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return {
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"gpu": "recommended",
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"vram_gb": 2,
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"ram_gb": 4,
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"storage_gb": 1
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}
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async def execute(self, request: Dict[str, Any]) -> PluginResult:
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"""Execute Whisper transcription"""
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start_time = time.time()
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try:
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# Validate request
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errors = self.validate_request(request)
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if errors:
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return PluginResult(
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success=False,
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error=f"Validation failed: {'; '.join(errors)}"
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)
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# Get parameters
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model_name = request.get("model", "base")
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task = request.get("task", "transcribe")
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language = request.get("language")
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temperature = request.get("temperature", 0.0)
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# Load or get cached model
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model = await self._load_model(model_name)
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# Get audio file
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audio_path = await self._get_audio_file(request)
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# Transcribe
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loop = asyncio.get_event_loop()
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if task == "translate":
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result = await loop.run_in_executor(
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None,
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lambda: model.transcribe(
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audio_path,
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task="translate",
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temperature=temperature
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)
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)
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else:
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result = await loop.run_in_executor(
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None,
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lambda: model.transcribe(
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audio_path,
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language=language,
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temperature=temperature
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)
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)
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# Clean up
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if audio_path != request.get("audio_file"):
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os.unlink(audio_path)
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execution_time = time.time() - start_time
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return PluginResult(
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success=True,
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data={
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"text": result["text"],
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"language": result.get("language"),
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"segments": result.get("segments", [])
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},
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metrics={
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"model": model_name,
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"task": task,
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"audio_duration": result.get("duration"),
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"processing_time": execution_time,
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"real_time_factor": result.get("duration", 0) / execution_time if execution_time > 0 else 0
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},
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execution_time=execution_time
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)
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except Exception as e:
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return PluginResult(
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success=False,
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error=str(e),
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execution_time=time.time() - start_time
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)
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async def _load_model(self, model_name: str):
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"""Load Whisper model with caching"""
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if model_name not in self._model_cache:
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loop = asyncio.get_event_loop()
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model = await loop.run_in_executor(
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None,
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lambda: self.whisper.load_model(model_name)
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)
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self._model_cache[model_name] = model
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return self._model_cache[model_name]
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async def _get_audio_file(self, request: Dict[str, Any]) -> str:
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"""Get audio file from URL or direct file path"""
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if "audio_file" in request:
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return request["audio_file"]
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# Download from URL
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audio_url = request["audio_url"]
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# Use requests to download
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import requests
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response = requests.get(audio_url, stream=True)
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response.raise_for_status()
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# Save to temporary file
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suffix = self._get_audio_suffix(audio_url)
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as f:
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for chunk in response.iter_content(chunk_size=8192):
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f.write(chunk)
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return f.name
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def _get_audio_suffix(self, url: str) -> str:
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"""Get file extension from URL"""
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if url.endswith('.mp3'):
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return '.mp3'
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elif url.endswith('.wav'):
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return '.wav'
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elif url.endswith('.m4a'):
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return '.m4a'
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elif url.endswith('.flac'):
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return '.flac'
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else:
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return '.mp3' # Default
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async def health_check(self) -> bool:
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"""Check Whisper health"""
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try:
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# Check if we can load the tiny model
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await self._load_model("tiny")
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return True
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except Exception:
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return False
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def cleanup(self) -> None:
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"""Cleanup resources"""
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self._model_cache.clear()
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