nvidia-nemo--speech/tools/speech_data_explorer

Speech Data Explorer

Dash-based tool for interactive exploration of ASR/TTS datasets.

Features:

• dataset's statistics (alphabet, vocabulary, duration-based histograms)
• navigation across dataset (sorting, filtering)
• inspection of individual utterances (waveform, spectrogram, audio player)
• errors' analysis (Word Error Rate, Character Error Rate, Word Match Rate, Mean Word Accuracy, diff)
• comparison of two ASR models using interactive word-level accuracy plot
• read manifests and audio directly from S3-compatible storage (including AIStore)
• support for tarred audio datasets with efficient byte-range reads via DALI index files

Quick Start

Install the requirements:

pip install -r requirements.txt

Run with a local manifest:

python data_explorer.py path_to_manifest.json

S3 / AIStore Support

Speech Data Explorer can read manifests and audio files directly from S3-compatible object storage, including NVIDIA AIStore (AIS).

Using an S3 config file

python data_explorer.py s3://bucket/manifest.json --s3cfg ~/.s3cfg[default]

Using AIStore with environment variables

export AIS_ENDPOINT=http://ais-gateway:8080
export AIS_AUTHN_TOKEN=your_token
python data_explorer.py s3://bucket/manifest.json --s3cfg AIS

Sharded paths (_OP_/_CL_ syntax)

Manifests and tar files are often split into numbered shards. Instead of listing every shard explicitly, use the _OP_start..end_CL_ range pattern. The tool expands it into individual paths automatically:

s3://bucket/manifest__OP_0..255_CL_.json
→  s3://bucket/manifest_0.json
   s3://bucket/manifest_1.json
   ...
   s3://bucket/manifest_255.json

Multiple ranges in a single path produce a cartesian product — useful when shards are spread across several buckets or directories:

s3://store_OP_1..2_CL_/audio__OP_0..1_CL_.tar
→  s3://store1/audio_0.tar
   s3://store1/audio_1.tar
   s3://store2/audio_0.tar
   s3://store2/audio_1.tar

Tarred audio

When audio is stored in tar archives locally or on S3, use --tar-base-path to point to the tar files. DALI index files are used automatically (if available at <tar_dir>/dali_index/) for fast byte-range lookups:

python data_explorer.py /data/manifests/manifest.json \
    --tar-base-path /data/tarred/audio.tar

python data_explorer.py s3://bucket/manifests/manifest__OP_0..255_CL_.json \
    --tar-base-path s3://bucket/tarred/audio__OP_0..255_CL_.tar \
    --s3cfg ~/.s3cfg[default]

You can also specify a custom DALI index location:

python data_explorer.py s3://bucket/manifest.json \
    --tar-base-path s3://bucket/tarred/audio__OP_0..255_CL_.tar \
    --dali-index-base s3://bucket/tarred/dali_index/ \
    --s3cfg ~/.s3cfg[default]

Comparing Two ASR Models

Single manifest with two prediction fields

If your manifest contains two pred_text_* fields (e.g. pred_text_contextnet and pred_text_conformer):

python data_explorer.py path_to_manifest.json \
    -nc pred_text_contextnet pred_text_conformer

Two separate manifests

You can also pass two separate manifests (order-invariant). Each manifest must contain a plain pred_text field, and -nc names the models:

python data_explorer.py manifest_model_A.json manifest_model_B.json \
    -nc pred_text_model_A pred_text_model_B

Manifest Format

JSON manifest file should contain the following fields:

• audio_filepath — path to audio file (local path, or filename inside a tar archive when using --tar-base-path)
• duration — duration of the audio file in seconds
• text — reference transcript

Errors' analysis requires pred_text (ASR transcript) for all utterances.

Any additional field will be parsed and displayed in the Samples tab.

Additional Options

Flag	Description
--vocab	Vocabulary file to highlight OOV words
--port	Serving port (default: 8050)
--estimate-audio-metrics / -a	Estimate audio metrics
--base-path	Base path for relative audio paths in the manifest
--tar-base-path	Local or S3 path to tarred audio files (supports sharded _OP_..._CL_ patterns)
--dali-index-base	Local or S3 path to DALI index directory for fast tar lookups
--s3cfg / -s3c	S3 config file and section, or AIS for AIStore env vars
--force / -f	Tolerate manifest entries with missing required fields
-nc / --names_compared	Two field names for model comparison
--show_statistics / -shst	Field name to show statistics for
--debug / -d	Enable debug mode