776 lines
28 KiB
Markdown
776 lines
28 KiB
Markdown
# Trax v2 Technical Architecture: High-Performance Single-Node Design
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## 🎯 Architecture Overview
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Trax v2 represents a significant evolution from v1, focusing on **high performance** and **speaker diarization** rather than distributed scalability. The architecture is designed as a **highly optimized, single-node, multi-process application** that leverages the full power of modern hardware while maintaining simplicity and determinism.
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### Key Architectural Principles
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1. **Single-Node Optimization**: Maximize utilization of one powerful machine rather than distributing across multiple nodes
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2. **Multi-Pass Pipeline**: Intelligent multi-stage processing for 99.5%+ accuracy
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3. **Parallel Processing**: Concurrent execution of independent tasks within the same job
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4. **Model Caching**: Persistent model management to avoid reloading overhead
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5. **Memory Efficiency**: 8-bit quantization and smart resource management
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6. **Deterministic Processing**: Predictable, reproducible results across runs
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## 🏗️ Core Architecture Components
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### 1. Enhanced Task System
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The task system evolves from simple single-action tasks to complex pipeline workflows:
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```python
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@dataclass
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class PipelineTask:
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"""Enhanced task definition for v2 pipeline workflows"""
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id: UUID
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media_file_id: UUID
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pipeline_stages: List[str] # ["transcribe", "enhance", "diarize", "merge"]
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pipeline_config: Dict[str, Any] # Model selection, domain, quality settings
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status: TaskStatus
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current_stage: Optional[str]
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progress_percentage: float
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error_message: Optional[str]
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created_at: datetime
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updated_at: datetime
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```
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#### Pipeline Stages
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- **transcribe**: Multi-pass transcription with confidence scoring
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- **enhance**: AI-powered text refinement using DeepSeek
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- **diarize**: Speaker identification using Pyannote.audio
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- **merge**: Combine transcript and diarization results
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### 2. ModelManager Singleton
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Central model management to prevent memory duplication and enable fast model switching:
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```python
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class ModelManager:
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"""Singleton for managing AI model lifecycle and caching"""
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def __init__(self):
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self._models: Dict[str, Any] = {}
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self._lora_adapters: Dict[str, Any] = {}
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self._model_configs: Dict[str, Dict] = {}
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async def get_model(self, model_type: str, config: Dict) -> Any:
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"""Get or load model with caching"""
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cache_key = self._generate_cache_key(model_type, config)
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if cache_key not in self._models:
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model = await self._load_model(model_type, config)
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self._models[cache_key] = model
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return self._models[cache_key]
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async def load_lora_adapter(self, domain: str) -> Any:
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"""Load LoRA adapter for domain-specific processing"""
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if domain not in self._lora_adapters:
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adapter = await self._load_lora_adapter(domain)
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self._lora_adapters[domain] = adapter
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return self._lora_adapters[domain]
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```
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### 3. Multi-Pass Transcription Pipeline
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Intelligent multi-stage processing for maximum accuracy:
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```python
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class MultiPassTranscriptionPipeline:
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"""Multi-pass transcription pipeline for 99.5%+ accuracy"""
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def __init__(self, config: PipelineConfig):
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self.config = config
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self.model_manager = ModelManager()
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async def process(self, audio_file: Path) -> TranscriptionResult:
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"""Execute multi-pass transcription pipeline"""
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# Stage 1: Fast initial transcription
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initial_result = await self._fast_pass(audio_file)
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# Stage 2: Confidence scoring and refinement
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confidence_scores = self._calculate_confidence(initial_result)
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refinement_segments = self._identify_low_confidence_segments(confidence_scores)
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if refinement_segments:
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refined_result = await self._refinement_pass(audio_file, refinement_segments)
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merged_result = self._merge_transcripts(initial_result, refined_result)
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else:
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merged_result = initial_result
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# Stage 3: AI enhancement (optional)
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if self.config.enable_enhancement:
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enhanced_result = await self._enhancement_pass(merged_result)
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return enhanced_result
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return merged_result
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async def _fast_pass(self, audio_file: Path) -> TranscriptionResult:
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"""First pass using fast model (distil-small.en)"""
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model = await self.model_manager.get_model("whisper", {
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"model": "distil-small.en",
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"quantized": True
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})
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return await self._transcribe_with_model(audio_file, model)
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async def _refinement_pass(self, audio_file: Path, segments: List[Segment]) -> TranscriptionResult:
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"""Refinement pass using accurate model (distil-large-v3)"""
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model = await self.model_manager.get_model("whisper", {
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"model": "distil-large-v3",
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"quantized": True,
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"segments": segments
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})
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return await self._transcribe_with_model(audio_file, model)
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async def _enhancement_pass(self, transcript: TranscriptionResult) -> TranscriptionResult:
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"""AI enhancement using DeepSeek"""
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enhancer = await self.model_manager.get_model("deepseek", {})
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return await enhancer.enhance_transcript(transcript)
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```
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### 4. Speaker Diarization Service
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Pyannote.audio integration for speaker identification:
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```python
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class SpeakerDiarizationService:
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"""Speaker diarization using Pyannote.audio"""
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def __init__(self, config: DiarizationConfig):
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self.config = config
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self.model_manager = ModelManager()
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self._embedding_model = None
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self._clustering_model = None
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async def diarize(self, audio_file: Path) -> DiarizationResult:
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"""Perform speaker diarization on audio file"""
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# Load models (cached)
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if not self._embedding_model:
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self._embedding_model = await self.model_manager.get_model("pyannote_embedding", {})
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if not self._clustering_model:
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self._clustering_model = await self.model_manager.get_model("pyannote_clustering", {})
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# Extract speaker embeddings
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embeddings = await self._extract_embeddings(audio_file)
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# Perform clustering
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speaker_segments = await self._cluster_speakers(embeddings)
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# Post-process and validate
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validated_segments = self._validate_speaker_segments(speaker_segments)
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return DiarizationResult(
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speaker_segments=validated_segments,
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speaker_count=len(set(seg.speaker_id for seg in validated_segments)),
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confidence_score=self._calculate_diarization_confidence(validated_segments)
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)
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async def _extract_embeddings(self, audio_file: Path) -> List[Embedding]:
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"""Extract speaker embeddings from audio"""
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# Implementation using Pyannote.audio embedding model
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pass
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async def _cluster_speakers(self, embeddings: List[Embedding]) -> List[SpeakerSegment]:
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"""Cluster embeddings to identify speakers"""
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# Implementation using Pyannote.audio clustering
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pass
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```
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### 5. Domain Adaptation with LoRA
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Lightweight domain-specific model adaptation:
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```python
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class LoRAAdapterManager:
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"""Manage LoRA adapters for domain-specific processing"""
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def __init__(self):
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self.model_manager = ModelManager()
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self._base_model = None
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self._current_adapter = None
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async def load_domain_adapter(self, domain: str) -> None:
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"""Load LoRA adapter for specific domain"""
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# Load base model if not loaded
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if not self._base_model:
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self._base_model = await self.model_manager.get_model("whisper_base", {})
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# Load domain-specific adapter
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adapter = await self.model_manager.load_lora_adapter(domain)
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# Apply adapter to base model
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self._base_model.load_adapter(adapter)
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self._current_adapter = domain
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async def auto_detect_domain(self, audio_file: Path) -> str:
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"""Automatically detect content domain"""
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# Use keyword analysis or content classification
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# Return detected domain (technical, medical, academic, general)
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pass
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async def transcribe_with_domain(self, audio_file: Path, domain: str) -> TranscriptionResult:
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"""Transcribe with domain-specific model"""
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await self.load_domain_adapter(domain)
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return await self._base_model.transcribe(audio_file)
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```
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## 🔄 Data Flow Architecture
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### Parallel Processing Flow
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```
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┌─────────────────┐
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│ Audio File │
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└─────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────┐
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│ Parallel Processing Pipeline │
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│ ┌─────────────────┐ ┌─────────────────────────────┐ │
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│ │ Transcription │ │ Diarization │ │
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│ │ Pipeline │ │ Pipeline │ │
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│ │ │ │ │ │
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│ │ • Fast Pass │ │ • Embedding Extraction │ │
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│ │ • Refinement │ │ • Speaker Clustering │ │
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│ │ • Enhancement │ │ • Segment Validation │ │
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│ └─────────────────┘ └─────────────────────────────┘ │
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└─────────────────────────────────────────────────────────┘
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│ │
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▼ ▼
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┌─────────────────┐ ┌─────────────────────────────┐
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│ Transcription │ │ Diarization │
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│ Result │ │ Result │
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└─────────────────┘ └─────────────────────────────┘
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│ │
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└───────────┬───────────────┘
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▼
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┌─────────────────────────────┐
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│ Merge Service │
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│ │
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│ • Align timestamps │
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│ • Combine speaker labels │
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│ • Validate consistency │
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└─────────────────────────────┘
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▼
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┌─────────────────────────────┐
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│ Final Transcript │
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│ │
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│ • High accuracy text │
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│ • Speaker identification │
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│ • Confidence scores │
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└─────────────────────────────┘
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```
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### State Management Flow
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```python
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class ProcessingJobManager:
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"""Manage processing job lifecycle and state transitions"""
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async def create_job(self, media_file_id: UUID, config: PipelineConfig) -> ProcessingJob:
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"""Create new processing job"""
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job = ProcessingJob(
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id=uuid4(),
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media_file_id=media_file_id,
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pipeline_config=config,
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status=TaskStatus.QUEUED,
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created_at=datetime.utcnow()
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)
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await self._save_job(job)
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return job
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async def execute_job(self, job: ProcessingJob) -> None:
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"""Execute processing job with state management"""
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try:
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# Update status to processing
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await self._update_job_status(job.id, TaskStatus.PROCESSING)
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# Execute pipeline stages
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for stage in job.pipeline_config.stages:
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await self._update_job_stage(job.id, stage)
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result = await self._execute_stage(job, stage)
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if not result.success:
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raise ProcessingError(f"Stage {stage} failed: {result.error}")
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# Mark as completed
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await self._update_job_status(job.id, TaskStatus.COMPLETED)
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except Exception as e:
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await self._update_job_status(job.id, TaskStatus.FAILED, str(e))
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raise
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```
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## 🚀 Performance Optimization Strategies
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### 1. Memory Optimization
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```python
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class MemoryOptimizer:
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"""Memory optimization strategies for v2"""
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def __init__(self):
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self.max_memory_gb = 8
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self.current_usage_gb = 0
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async def optimize_model_loading(self, model_config: Dict) -> Dict:
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"""Apply memory optimizations to model loading"""
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# 8-bit quantization
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if model_config.get("quantized", True):
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model_config["torch_dtype"] = torch.int8
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# Gradient checkpointing for large models
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if model_config.get("model_size") == "large":
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model_config["gradient_checkpointing"] = True
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# Model offloading for very large models
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if self.current_usage_gb > self.max_memory_gb * 0.8:
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model_config["device_map"] = "auto"
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return model_config
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async def cleanup_unused_models(self) -> None:
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"""Clean up unused models to free memory"""
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unused_models = self._identify_unused_models()
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for model in unused_models:
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await self._unload_model(model)
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```
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### 2. CPU Optimization
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```python
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class CPUOptimizer:
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"""CPU optimization for parallel processing"""
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def __init__(self):
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self.cpu_count = os.cpu_count()
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self.optimal_worker_count = min(self.cpu_count, 8)
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async def configure_worker_pool(self) -> AsyncWorkerPool:
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"""Configure optimal worker pool size"""
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return AsyncWorkerPool(
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max_workers=self.optimal_worker_count,
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thread_name_prefix="trax_worker"
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)
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async def optimize_audio_processing(self, audio_file: Path) -> Path:
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"""Optimize audio for processing"""
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# Convert to optimal format (16kHz mono WAV)
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# Apply noise reduction if needed
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# Chunk large files appropriately
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pass
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```
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### 3. Pipeline Optimization
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```python
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class PipelineOptimizer:
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"""Optimize pipeline execution for performance"""
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async def execute_parallel_stages(self, job: ProcessingJob) -> Dict[str, Any]:
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"""Execute independent stages in parallel"""
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# Identify parallel stages
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parallel_stages = self._identify_parallel_stages(job.pipeline_config)
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# Execute in parallel
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tasks = []
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for stage in parallel_stages:
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task = asyncio.create_task(self._execute_stage(job, stage))
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tasks.append(task)
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# Wait for completion
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results = await asyncio.gather(*tasks, return_exceptions=True)
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return dict(zip(parallel_stages, results))
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def _identify_parallel_stages(self, config: PipelineConfig) -> List[str]:
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"""Identify stages that can run in parallel"""
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# Transcription and diarization can run in parallel
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# Enhancement must wait for transcription
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# Merging must wait for both transcription and diarization
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pass
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```
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## 💻 CLI Interface Architecture
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### Enhanced CLI Interface
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```python
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class TraxCLI:
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"""Enhanced CLI interface for Trax v2"""
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def __init__(self):
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self.progress_reporter = ProgressReporter()
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self.batch_processor = BatchProcessor()
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self.logger = setup_logging()
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async def transcribe_single(self, file_path: Path, config: PipelineConfig) -> None:
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"""Transcribe a single file with enhanced progress reporting"""
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# Validate file
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self._validate_file(file_path)
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# Create processing job
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job = await self._create_job(file_path, config)
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# Process with real-time progress
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await self._process_with_progress(job)
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# Display results
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self._display_results(job)
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async def transcribe_batch(self, directory: Path, config: PipelineConfig) -> None:
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"""Process batch of files with enhanced progress reporting"""
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# Validate directory
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files = self._validate_directory(directory)
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# Create batch job
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batch_job = await self._create_batch_job(files, config)
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# Process batch with progress
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await self._process_batch_with_progress(batch_job)
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# Display batch results
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self._display_batch_results(batch_job)
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def _validate_file(self, file_path: Path) -> None:
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"""Validate single file for processing"""
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if not file_path.exists():
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raise FileNotFoundError(f"File not found: {file_path}")
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if file_path.stat().st_size > 500 * 1024 * 1024: # 500MB
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raise ValueError(f"File too large: {file_path}")
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if file_path.suffix.lower() not in ['.mp3', '.mp4', '.wav', '.m4a', '.webm']:
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raise ValueError(f"Unsupported format: {file_path.suffix}")
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def _validate_directory(self, directory: Path) -> List[Path]:
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"""Validate directory and return list of supported files"""
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if not directory.exists():
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raise FileNotFoundError(f"Directory not found: {directory}")
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supported_extensions = {'.mp3', '.mp4', '.wav', '.m4a', '.webm'}
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files = [
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f for f in directory.iterdir()
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if f.is_file() and f.suffix.lower() in supported_extensions
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]
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if not files:
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raise ValueError(f"No supported files found in: {directory}")
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return files
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```
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### Progress Reporting
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```python
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class ProgressReporter:
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"""Real-time progress reporting for CLI"""
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def __init__(self):
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self.start_time = None
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self.current_stage = None
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async def report_progress(self, job: ProcessingJob) -> None:
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"""Report real-time progress for a job"""
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if self.start_time is None:
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self.start_time = time.time()
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# Calculate progress
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elapsed = time.time() - self.start_time
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progress = job.progress_percentage
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# Display progress bar
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self._display_progress_bar(progress, elapsed)
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# Display current stage
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if job.current_stage != self.current_stage:
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self.current_stage = job.current_stage
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self._display_stage_info(job.current_stage)
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# Display performance metrics
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self._display_performance_metrics(job)
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def _display_progress_bar(self, progress: float, elapsed: float) -> None:
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"""Display ASCII progress bar"""
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bar_length = 50
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filled_length = int(bar_length * progress / 100)
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bar = '█' * filled_length + '-' * (bar_length - filled_length)
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print(f"\rProgress: [{bar}] {progress:.1f}% ({elapsed:.1f}s)", end='', flush=True)
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def _display_stage_info(self, stage: str) -> None:
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"""Display current processing stage"""
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print(f"\n🔄 {stage.title()}...")
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def _display_performance_metrics(self, job: ProcessingJob) -> None:
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"""Display performance metrics"""
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if hasattr(job, 'performance_metrics'):
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metrics = job.performance_metrics
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print(f" CPU: {metrics.get('cpu_usage', 0):.1f}% | "
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f"Memory: {metrics.get('memory_usage_gb', 0):.1f}GB | "
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f"Speed: {metrics.get('processing_speed', 0):.1f}x")
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```
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## 🔧 Configuration Management
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### Pipeline Configuration
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```python
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@dataclass
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class PipelineConfig:
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"""Configuration for v2 processing pipeline"""
|
|
|
|
# Pipeline version
|
|
version: str = "v2" # v1, v2, v2+
|
|
|
|
# Model selection
|
|
transcription_model: str = "distil-large-v3"
|
|
enhancement_model: str = "deepseek"
|
|
diarization_model: str = "pyannote"
|
|
|
|
# Quality settings
|
|
accuracy_threshold: float = 0.995 # 99.5%
|
|
confidence_threshold: float = 0.8
|
|
|
|
# Domain settings
|
|
domain: Optional[str] = None # technical, medical, academic, general
|
|
auto_detect_domain: bool = True
|
|
|
|
# Performance settings
|
|
enable_quantization: bool = True
|
|
parallel_processing: bool = True
|
|
max_workers: int = 8
|
|
|
|
# Diarization settings
|
|
enable_diarization: bool = False
|
|
min_speaker_count: int = 2
|
|
max_speaker_count: int = 10
|
|
|
|
# Enhancement settings
|
|
enable_enhancement: bool = True
|
|
enhancement_prompts: Dict[str, str] = field(default_factory=dict)
|
|
```
|
|
|
|
### Environment Configuration
|
|
|
|
```python
|
|
class TraxV2Config:
|
|
"""Configuration management for Trax v2"""
|
|
|
|
def __init__(self):
|
|
self.load_from_env()
|
|
self.load_from_file()
|
|
self.validate()
|
|
|
|
def load_from_env(self):
|
|
"""Load configuration from environment variables"""
|
|
self.openai_api_key = os.getenv("OPENAI_API_KEY")
|
|
self.deepseek_api_key = os.getenv("DEEPSEEK_API_KEY")
|
|
self.huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
|
|
|
|
# Performance settings
|
|
self.max_memory_gb = int(os.getenv("TRAX_MAX_MEMORY_GB", "8"))
|
|
self.max_workers = int(os.getenv("TRAX_MAX_WORKERS", "8"))
|
|
self.enable_quantization = os.getenv("TRAX_ENABLE_QUANTIZATION", "true").lower() == "true"
|
|
|
|
def validate(self):
|
|
"""Validate configuration"""
|
|
if not self.openai_api_key:
|
|
raise ConfigurationError("OPENAI_API_KEY is required")
|
|
if not self.deepseek_api_key:
|
|
raise ConfigurationError("DEEPSEEK_API_KEY is required")
|
|
if not self.huggingface_token:
|
|
raise ConfigurationError("HUGGINGFACE_TOKEN is required for diarization")
|
|
```
|
|
|
|
## 🧪 Testing Architecture
|
|
|
|
### Unit Testing Strategy
|
|
|
|
```python
|
|
class TraxV2TestSuite:
|
|
"""Comprehensive test suite for Trax v2"""
|
|
|
|
def test_multi_pass_pipeline(self):
|
|
"""Test multi-pass transcription pipeline"""
|
|
pipeline = MultiPassTranscriptionPipeline(self.test_config)
|
|
result = await pipeline.process(self.test_audio_file)
|
|
|
|
assert result.accuracy_estimate >= 0.995
|
|
assert result.processing_time_ms < 25000 # <25 seconds
|
|
assert len(result.segments) > 0
|
|
|
|
def test_diarization_service(self):
|
|
"""Test speaker diarization"""
|
|
diarization = SpeakerDiarizationService(self.test_config)
|
|
result = await diarization.diarize(self.test_multi_speaker_file)
|
|
|
|
assert result.speaker_count >= 2
|
|
assert result.confidence_score >= 0.9
|
|
assert len(result.speaker_segments) > 0
|
|
|
|
def test_lora_adapter_manager(self):
|
|
"""Test domain adaptation"""
|
|
adapter_manager = LoRAAdapterManager()
|
|
await adapter_manager.load_domain_adapter("technical")
|
|
|
|
result = await adapter_manager.transcribe_with_domain(
|
|
self.test_technical_file, "technical"
|
|
)
|
|
|
|
assert result.domain_used == "technical"
|
|
assert result.accuracy_estimate > 0.99
|
|
```
|
|
|
|
### Integration Testing Strategy
|
|
|
|
```python
|
|
class TraxV2IntegrationTests:
|
|
"""Integration tests for complete v2 pipeline"""
|
|
|
|
async def test_complete_v2_pipeline(self):
|
|
"""Test complete v2 pipeline with diarization"""
|
|
job_manager = ProcessingJobManager()
|
|
|
|
# Create job
|
|
job = await job_manager.create_job(
|
|
self.test_file_id,
|
|
PipelineConfig(version="v2+", enable_diarization=True)
|
|
)
|
|
|
|
# Execute job
|
|
await job_manager.execute_job(job)
|
|
|
|
# Verify results
|
|
assert job.status == TaskStatus.COMPLETED
|
|
assert job.transcript.accuracy_estimate >= 0.995
|
|
assert job.transcript.speaker_count >= 2
|
|
assert job.processing_time_ms < 25000
|
|
|
|
async def test_cli_batch_processing(self):
|
|
"""Test CLI batch processing with multiple files"""
|
|
cli = TraxCLI()
|
|
|
|
# Process batch of files
|
|
test_directory = Path("test_files")
|
|
config = PipelineConfig(version="v2", enable_diarization=True)
|
|
|
|
await cli.transcribe_batch(test_directory, config)
|
|
|
|
# Verify all files processed
|
|
results = await self._get_batch_results()
|
|
assert len(results) == len(list(test_directory.glob("*.mp3")))
|
|
assert all(result.status == "completed" for result in results)
|
|
```
|
|
|
|
## 📊 Performance Monitoring
|
|
|
|
### Metrics Collection
|
|
|
|
```python
|
|
class PerformanceMonitor:
|
|
"""Monitor and collect performance metrics"""
|
|
|
|
def __init__(self):
|
|
self.metrics: Dict[str, List[float]] = defaultdict(list)
|
|
|
|
async def record_metric(self, metric_name: str, value: float):
|
|
"""Record a performance metric"""
|
|
self.metrics[metric_name].append(value)
|
|
|
|
async def get_performance_report(self) -> Dict[str, Dict]:
|
|
"""Generate performance report"""
|
|
report = {}
|
|
|
|
for metric_name, values in self.metrics.items():
|
|
report[metric_name] = {
|
|
"count": len(values),
|
|
"mean": statistics.mean(values),
|
|
"median": statistics.median(values),
|
|
"min": min(values),
|
|
"max": max(values),
|
|
"std": statistics.stdev(values) if len(values) > 1 else 0
|
|
}
|
|
|
|
return report
|
|
|
|
async def check_performance_targets(self) -> Dict[str, bool]:
|
|
"""Check if performance targets are met"""
|
|
targets = {
|
|
"processing_time_5min": 25000, # <25 seconds
|
|
"accuracy_threshold": 0.995, # 99.5%+
|
|
"memory_usage_gb": 8, # <8GB
|
|
"diarization_accuracy": 0.9 # 90%+
|
|
}
|
|
|
|
results = {}
|
|
for target_name, target_value in targets.items():
|
|
if target_name in self.metrics:
|
|
current_value = statistics.mean(self.metrics[target_name])
|
|
results[target_name] = current_value <= target_value
|
|
|
|
return results
|
|
```
|
|
|
|
## 🔄 Migration Strategy
|
|
|
|
### From v1 to v2
|
|
|
|
```python
|
|
class TraxV2Migration:
|
|
"""Migration utilities for upgrading from v1 to v2"""
|
|
|
|
async def migrate_database_schema(self):
|
|
"""Migrate database schema for v2 features"""
|
|
|
|
# Add new tables
|
|
await self._create_speaker_profiles_table()
|
|
await self._create_processing_jobs_table()
|
|
|
|
# Modify existing tables
|
|
await self._add_v2_columns_to_transcripts()
|
|
await self._add_v2_columns_to_media_files()
|
|
|
|
async def migrate_existing_transcripts(self):
|
|
"""Migrate existing v1 transcripts to v2 format"""
|
|
|
|
v1_transcripts = await self._get_v1_transcripts()
|
|
|
|
for transcript in v1_transcripts:
|
|
# Update schema
|
|
transcript.pipeline_version = "v1"
|
|
transcript.merged_content = transcript.raw_content
|
|
|
|
# Save updated transcript
|
|
await self._save_transcript(transcript)
|
|
|
|
async def validate_migration(self) -> bool:
|
|
"""Validate successful migration"""
|
|
|
|
# Check schema
|
|
schema_valid = await self._validate_schema()
|
|
|
|
# Check data integrity
|
|
data_valid = await self._validate_data_integrity()
|
|
|
|
# Check functionality
|
|
functionality_valid = await self._test_v2_functionality()
|
|
|
|
return schema_valid and data_valid and functionality_valid
|
|
```
|
|
|
|
---
|
|
|
|
*This architecture document provides the technical foundation for implementing Trax v2, focusing on high performance and speaker diarization while maintaining the simplicity and determinism of the single-node design.*
|