> This page is for version 26.09 · v1.4.0. > For other versions, use one of these documentation indexes: > - Latest · v1.4.0 (26.09) (default): https://docs.nvidia.com/nemo/curator/latest/llms.txt > - Main · preview: https://docs.nvidia.com/nemo/curator/main/llms.txt > - 26.09 · v1.4.0: https://docs.nvidia.com/nemo/curator/v26.09/llms.txt > - 26.07 · v1.3.0: https://docs.nvidia.com/nemo/curator/v26.07/llms.txt > - 26.04 · v1.2.0: https://docs.nvidia.com/nemo/curator/v26.04/llms.txt > - 26.02 · v1.1.0: https://docs.nvidia.com/nemo/curator/v26.02/llms.txt > - 25.09 · v1.0.0: https://docs.nvidia.com/nemo/curator/v25.09/llms.txt > 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 AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/curator/_mcp/server. > Step-by-step guide to setting up and running your first audio curation pipeline with NeMo Curator # Quickstart for NeMo Curator Audio Curation Use this quickstart to run NeMo Curator on a FLEURS audio dataset and inspect transcription-quality results. For complete setup details, refer to the [Installation Guide](/get-started/installation). ## Prerequisites Prepare the following environment before running the audio pipeline: * Python 3.11, 3.12, or 3.13 on x86\_64 Linux. Ubuntu 20.04 or 22.04 is recommended. * `packaging` 22.0 or later and Git. * `uv` for package management. * An `ffmpeg` executable on `PATH` for stages that resample or convert audio. The Python extras do not install it; audio workflows are easiest to reproduce in a [locally built container with FFmpeg](/reference/infra/container-environments#add-ffmpeg-for-audio-or-video). For Python package installs, refer to [FFmpeg installation options](/get-started/installation#install-ffmpeg-for-audio-and-video-source-installs). * An NVIDIA Volta-class or newer GPU with compute capability 7.0 or later and CUDA 12 is recommended for ASR inference; the example also supports CPU execution. > **Warning** > > Audio curation requires x86\_64 Linux. The `audio_cpu` and `audio_cuda12` extras omit dependencies for arm64/aarch64, including NeMo ASR and diarization. Use an amd64 host or container for ASR, diarization, and tagging workflows. ## Quickstart Steps Install Curator, run the maintained FLEURS pipeline, and inspect its output. 1. Install NeMo Curator. A locally built container is recommended for audio workflows so FFmpeg is available on every worker. #### Build an Image (Recommended) Follow [Build an Image](/reference/infra/container-environments#build-an-image), then add FFmpeg using [Add FFmpeg for Audio or Video](/reference/infra/container-environments#add-ffmpeg-for-audio-or-video). This packages the audio system dependency in the image used by each worker. #### Source Installation The repository clone tracks development. For a released version, check out its published release tag before syncing. ```bash curl -LsSf https://astral.sh/uv/0.12.1/install.sh | sh source $HOME/.local/bin/env git clone https://github.com/NVIDIA-NeMo/Curator.git cd Curator uv sync --extra audio_cuda12 --all-groups source .venv/bin/activate ``` Use `audio_cpu` for CPU-only processing. Replace `audio_cuda12` with `audio_cpu` in the `uv sync` command to use CPU-only processing. #### PyPI Installation For a specific release, verify that its package version is available on PyPI and matches the release notes. ```bash curl -LsSf https://astral.sh/uv/0.12.1/install.sh | sh source $HOME/.local/bin/env uv venv source .venv/bin/activate echo "transformers==4.55.2" > override.txt uv pip install "nemo-curator[audio_cuda12]" --override override.txt ``` The audio extra installs NeMo Toolkit and its dependent audio-processing libraries. It does not install the `ffmpeg` executable. 2. Open a repository checkout. If you do not already have one, clone the source revision that matches your Curator installation. ```bash if [ ! -d .git ]; then git clone https://github.com/NVIDIA-NeMo/Curator.git cd Curator fi ``` 3. Run the maintained FLEURS pipeline from the repository root. ```bash python tutorials/audio/fleurs/main.py \ --config-path . \ --config-name pipeline \ raw_data_dir="${PWD}/example_audio/fleurs" ``` The checked-in configuration downloads the Armenian (`hy_am`) development split, runs FastConformer ASR, calculates word error rate and duration, and filters at `wer_pct <= 5.5`. The configuration sets `max_audio_sec_per_actor=240.0`, `max_inference_duration_s=120.0`, `local_bucketing=true`, and `use_cuda_graph_decoder=false`. The 120-second inference limit is within the 240-second actor budget. For CPU execution, install the `audio_cpu` extra and set the GPU resource request to zero. Refer to the [Beginner Audio Processing Tutorial](/curate-audio/tutorials/beginner) for supported overrides and details about the checked-in configuration. ```bash python tutorials/audio/fleurs/main.py \ --config-path . \ --config-name pipeline \ raw_data_dir="${PWD}/example_audio/fleurs-cpu" \ stages.1.resources.gpus=0 ``` 4. Inspect the generated files and JSONL records. The output includes `audio_filepath`, `text`, `pred_text`, `wer_pct`, and `duration` fields. ```text example_audio/fleurs/ ├── hy_am/ # Downloaded Armenian data and source manifest └── result/ └── hy_am/ # Filtered JSONL output shards ``` ```json { "audio_filepath": "/absolute/path/to/audio.wav", "text": "ground truth transcription", "pred_text": "asr model prediction", "wer_pct": 2.5, "duration": 4.2 } ``` ## Next Steps Use these guides to adapt the pipeline to your audio datasets: #### [Custom Audio Manifests](/curate-audio/load-data/custom-manifests) Load your own audio datasets. #### [Audio Quality Assessment](/curate-audio/process-data/quality-assessment) Filter data using quality metrics. #### [Audio and Text Integration](/curate-audio/process-data/text-integration) Combine audio and text workflows.