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feat: Implement performance optimizations in speaking_route.py
- Added asynchronous processing for post-assessment tasks to reduce processing time. - Introduced shared instances for G2P and ThreadPoolExecutor to improve resource management. - Implemented caching for G2P results to avoid redundant computations. - Enhanced IPA assessment processing with parallel execution for character analysis, phoneme scoring, and focus phonemes analysis. - Created a performance testing script to validate optimizations and measure improvements. - Documented optimization strategies and performance metrics in PERFORMANCE_OPTIMIZATION.md.c9fd875 -
Enhance Vietnamese feedback generation with actionable insights and specific improvement strategies. Refine overall feedback based on score ranges, provide detailed guidance for problematic words and phonemes, and suggest clear next steps for users to improve their pronunciation skills.
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Refactor code structure for improved readability and maintainability
85fa45c
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Implement enhanced pronunciation assessment system with Wav2Vec2 support
- Added Wav2Vec2CharacterASR class for character-level ASR using Wav2Vec2 model. - Integrated SimpleG2P for grapheme-to-phoneme conversion. - Developed WordAnalyzer for analyzing word-level pronunciation accuracy. - Created PhonemeComparator to compare reference and learner phoneme sequences. - Introduced SimpleFeedbackGenerator for generating actionable feedback in Vietnamese. - Implemented SimplePronunciationAssessor as the main interface for pronunciation assessment. - Added test scripts for backward compatibility and enhanced features. - Verified enhanced features and method availability in the new system.aa2c910
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feat: refactor Wav2Vec2 character ASR to support quantization and improve model loading
c5ca6dc
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feat: implement Wav2Vec2 character-level ASR with ONNX and Transformers support, add phoneme comparison and feedback generation
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fix: update import statement for BaseModel in agent.py and add timing logs in speaking_controller.py
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Add Wav2Vec2 model conversion and inference to ONNX format
- Implemented Wav2Vec2ONNXConverter class for converting Wav2Vec2 models to ONNX format, including model loading, conversion, and verification. - Added Wav2Vec2ONNXInference class for performing inference using the converted ONNX model. - Included methods for softmax calculation and transcription of audio files. - Added utility functions for creating compatible models and exporting with fallback options for different ONNX opset versions. - Introduced optimization function for ONNX models. - Created helper function to convert numpy types to native Python types for better compatibility.df380ff