runtime/omnivoice-python/omnivoice-source/docs/data_preparation_advanced.md

Advanced Data Preparation

The advanced pipeline adds denoising and prompt noise augmentation on top of the basic tokenization workflow. Each stage is optional.

Prerequisites

• Denoising: Sidon model checkpoints (feature_extractor_cuda.pt, decoder_cuda.pt) from https://huggingface.co/sarulab-speech/sidon-v0.1/tree/main.
• Noise augmentation: noise + RIR tar shards with data.lst manifests

Pipeline Overview

Step 1 (optional): Denoise
  Raw audio → Sidon denoiser → clean audio

Step 2: Tokenize (with optional noise augmentation)
  Clean audio + noise augment on prefix → audio tokenizer → tokens

Denoise

Use the Sidon speech enhancement model to remove background noise from raw audio.

export CUDA_VISIBLE_DEVICES="0,1,2,3"
python -m omnivoice.scripts.denoise_audio \
    --input_jsonl data.jsonl \
    --tar_output_pattern data/denoised/audios/shard-%06d.tar \
    --jsonl_output_pattern data/denoised/txts/shard-%06d.jsonl \
    --feature_extractor_path /path/to/sidon_feature_extractor_cuda.pt \
    --decoder_path /path/to/sidon_decoder_cuda.pt \
    --target_sample_rate 24000 \
    --batch_duration 200.0

What it does:

1. Reads your JSONL manifest
2. Runs Sidon denoiser on each audio file
3. Outputs denoised audio as custom WebDataset tar/jsonl shards
4. Generates a data.lst manifest in data/denoised/

You can also pass --input_manifest /path/to/data.lst if you already have a custom webdataset format dataset. The next step would be passing the generated data.lst file with --input_manifest to omnivoice.scripts.extract_audio_tokens for tokens extraction.

Tokenize with noise augmentation

Adds environmental noise and room reverb to prompt audio during tokenization, making the model robust to noisy reference audio at inference time. Note that in our model, we only add noise augmentation for a small proportion of data, making sure the model can also generate good audio with clean reference audio.

You need two additional datasets in WebDataset format:

• Noise recordings: environmental noise tar shards with a data.lst manifest
• Room impulse responses (RIR): RIR tar shards with a data.lst manifest

export CUDA_VISIBLE_DEVICES="0,1,2,4"
python -m omnivoice.scripts.extract_audio_tokens_add_noise \
    --input_jsonl data.jsonl \
    --tar_output_pattern data/tokens/shard-%06d.tar \
    --jsonl_output_pattern data/txts/shard-%06d.jsonl \
    --tokenizer_path eustlb/higgs-audio-v2-tokenizer \
    --noise_manifest data/noise_shards/data.lst \
    --rir_manifest data/rir_shards/data.lst \
    --nj_per_gpu 3

You can also pass --input_manifest /path/to/data.lst if you already have a custom webdataset format dataset.