Beginner Audio Processing Tutorial

Run the maintained FLEURS tutorial to download multilingual speech, transcribe it with a NeMo ASR model, calculate Word Error Rate (WER) and duration, filter samples, and write a JSONL manifest.

What You Will Do

The checked-in tutorial builds this pipeline:

1. CreateInitialManifestFleursStage downloads and prepares a FLEURS split.
2. ASRStage and NeMoASRAdapter generate pred_text.
3. GetPairwiseWerStage writes wer_pct.
4. GetAudioDurationStage writes duration.
5. PreserveByValueStage filters by the configured WER threshold.
6. AudioToDocumentStage and JsonlWriter write the filtered JSONL output.

Maintained Example Files

The working tutorial is maintained in one place:

tutorials/audio/fleurs/
├── README.md
├── main.py
├── pipeline.yaml
└── fleurs_tutorial.ipynb

main.py loads pipeline.yaml through Hydra and manages the Ray client and selected executor. Use this entrypoint instead of copying the pipeline into a second script.

Prerequisites

• Python 3.11, 3.12, or 3.13
• NeMo Curator installed with audio_cuda12 for GPU inference or audio_cpu for CPU inference
• Internet access for the FLEURS split and ASR checkpoint
• Sufficient disk space for the selected language and split
• An NVIDIA GPU with approximately 4 GB or more VRAM is recommended; CPU fallback is supported but substantially slower

From the repository root, install one audio extra and activate the environment:

# GPU (recommended)
uv sync --extra audio_cuda12
source .venv/bin/activate
# For CPU-only inference, use this instead:
# uv sync --extra audio_cpu

Run the Default Pipeline

Run the maintained entrypoint from the repository root:

python tutorials/audio/fleurs/main.py \
  --config-path . \
  --config-name pipeline \
  raw_data_dir="${PWD}/example_audio/fleurs"

The defaults process the Armenian (hy_am) development split with nvidia/stt_hy_fastconformer_hybrid_large_pc, keep samples with wer_pct <= 5.5, use the Xenna backend, and write results under ${raw_data_dir}/result/${lang}/.

CPU fallback

Set the ASR stage’s GPU requirement to zero:

python tutorials/audio/fleurs/main.py \
  --config-path . \
  --config-name pipeline \
  raw_data_dir="${PWD}/example_audio/fleurs-cpu" \
  stages.1.resources.gpus=0

CPU inference is intended for functional testing and is typically 10–50 times slower than GPU inference.

Understand the ASR Capacity Contract

The supplied pipeline.yaml keeps the required ASR capacity settings together:

max_inference_duration_s must be less than or equal to max_audio_sec_per_actor. If you reduce the actor budget to address memory pressure, reduce the inference-duration ceiling as needed to preserve that invariant.

Customize the Run

Use Hydra overrides without editing pipeline.yaml:

python tutorials/audio/fleurs/main.py \
  --config-path . \
  --config-name pipeline \
  raw_data_dir="${PWD}/example_audio/fleurs-en" \
  lang=en_us \
  data_split=dev \
  stages.1.model_id=nvidia/parakeet-tdt-0.6b-v2 \
  wer_threshold=25.0 \
  backend=ray_data

Common overrides:

Refer to tutorials/audio/fleurs/pipeline.yaml for the complete configuration contract and tutorials/audio/fleurs/README.md for model, backend, and performance guidance.

Inspect the Results

For the default language, output is written under:

example_audio/fleurs/
├── hy_am/                 # Downloaded FLEURS data and source manifest
└── result/
    └── hy_am/             # Filtered JSONL output shards

Each output row contains the top-level fields produced by the maintained pipeline:

{
  "audio_filepath": "/absolute/path/to/audio.wav",
  "text": "ground truth transcription",
  "pred_text": "ASR model prediction",
  "wer_pct": 2.5,
  "duration": 4.2
}

Inspect the output from the repository root:

find "${PWD}/example_audio/fleurs/result" -name '*.jsonl' -print
find "${PWD}/example_audio/fleurs/result/hy_am" -name '*.jsonl' -exec jq -c . {} + | head -n 5

Troubleshooting

Next Steps

• Custom Audio Manifests
• ASR Inference
• Quality Assessment
• Text Integration