> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nemo/curator/llms.txt. > For full documentation content, see https://docs.nvidia.com/nemo/curator/llms-full.txt. > Process audio data using ASR inference, quality assessment, audio analysis, and text integration for high-quality speech datasets # Process Data for Audio Curation Process audio data you've loaded into `AudioTask` objects using NeMo Curator's comprehensive audio processing capabilities. NeMo Curator provides a specialized suite of tools for processing speech and audio data as part of the AI training pipeline. These tools help you transcribe, analyze, filter, and integrate audio datasets to ensure high-quality input for ASR model training and multimodal applications. ## How it Works NeMo Curator's audio processing capabilities are organized into five main categories: 1. **ASR Inference**: Transcribe audio using NeMo Framework's pretrained ASR models 2. **Quality Assessment**: Calculate and filter based on transcription accuracy metrics 3. **Quality Filtering**: Segment, filter, and diarize raw audio into clean single-speaker training segments 4. **Audio Analysis**: Extract audio characteristics like duration and validate formats 5. **Text Integration**: Convert processed audio data to text processing workflows Each category provides GPU-accelerated implementations optimized for different speech curation needs. The result is a cleaned and filtered audio dataset with high-quality transcriptions ready for model training. *** ## ASR Inference Transcribe audio files using NeMo Framework's state-of-the-art ASR models with GPU acceleration. Use pretrained NeMo ASR models for accurate speech recognition pretrained multilingual gpu-accelerated Efficiently process large audio datasets with configurable batch sizes batch-inference memory-optimization scalable ## Quality Assessment Evaluate and filter audio quality using transcription accuracy and audio characteristics. Filter audio samples based on Word Error Rate thresholds accuracy quality-metrics filtering Filter audio samples by duration ranges and speech rate metrics duration speech-rate range-filtering ## Quality Filtering Compose VAD, band, UTMOS, SIGMOS, and speaker-separation stages to extract clean single-speaker training segments from raw audio. End-to-end pipeline of preprocessing, segmentation, and filtering stages vad mos-scoring diarization Single composite stage that decomposes into the full filtering pipeline from a YAML config composite yaml-config end-to-end ## Audio Analysis Extract and analyze audio file characteristics for quality control and metadata generation. Calculate precise audio duration using soundfile library soundfile precision metadata Validate audio file formats and detect corrupted files validation error-handling format-support ## ALM Data Curation Curate training data for audio language models by extracting fixed-duration windows from diarized audio segments. Construct candidate training windows from consecutive segments with quality filtering windowing speaker-count bandwidth Remove redundant overlapping windows based on configurable thresholds deduplication overlap-ratio target-duration ## Text Integration Convert processed audio data to text processing workflows for multimodal applications. Convert AudioTask objects to DocumentBatch for text processing format-conversion pipeline-integration multimodal