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ESPNET
End-to-end speech processing for all tasks
Apache-2.0
ABOUT
Speech processing research and development involves a fragmented ecosystem — separate toolkits for ASR, TTS, speaker diarization, and voice conversion, each with its own configuration format, preprocessing pipeline, and evaluation scripts that don't interoperate. ESPnet provides a single framework with consistent recipes, configurations, and experiment management across the full range of speech tasks, making it easy to train, evaluate, and deploy models for any speech application without learning multiple toolkits.
INSTALL
pip install espnetINTEGRATION GUIDE
1. Train a custom ASR model on domain-specific audio data using standardized recipes and configurations
2. Build a text-to-speech system with multiple voice variants from a single trained model
3. Create a live speech translation pipeline that transcribes and translates audio in real time
4. Develop a speaker diarization system that identifies who spoke when in meeting recordings
TAGS
pythonspeechasrttsspeech-recognitionaudiodeep-learningend-to-end