datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
multilingual-speech-commands-15lang
Multilingual Speech Commands Dataset (15 Languages, Augmented)
This dataset contains augmented speech command samples in 15 languages, derived from multiple public datasets. Only commands that overlap with the Google Speech Commands (GSC) vocabulary are included, making the dataset suitable for multilingual keyword spotting tasks aligned with GSC-style classification.
Audio samples have been augmented using standard audio techniques to improve model robustness (e.g., time-shifting… See the full description on the dataset page: https://huggingface.co/datasets/artur-muratov/multilingual-speech-commands-15lang.text-commands-2026-0422
Commands
Clean summary of 4D language reference.
Abstract
LLMs are generally incapable of understanding 4D code. LoRA by exposure to raw source code would actually increase the rate of hallucination as the model gets confused between 4D code and C#, Visual Basic, or JavaScript.
CPT, or continued pre-training, based on grammar and vocabulary should moderate the model's attention before extensive fine-tuning using raw source code.
This dataset was generated with Mistral… See the full description on the dataset page: https://huggingface.co/datasets/keisuke-miyako/text-commands-2026-0422.fluent_speech_commands_synth
Dataset Card for "fluent_speech_commands_synth"
More Information needed
Networking_Commands_DatasetNetworking Commands Dataset
Overview
This dataset is (networking_dataset) contains 750 unique Cisco-specific and general networking commands (NET001–NET750), designed for red teaming AI models in cybersecurity. It focuses on testing model understanding, detecting malicious intent, and ensuring safe responses in enterprise networking environments. The dataset includes both common and obscure commands, emphasizing advanced configurations for adversarial testing.
Dataset Structure
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/darkknight25/Networking_Commands_Dataset.speech-commands-v0.02
Speech Commands Dataset v0.02
This is a re-hosted copy of the Google Speech Commands v0.02 dataset in Parquet format for compatibility with the Hugging Face Dataset Viewer.
⚠️ Credits
This dataset was created by Pete Warden / Google. All credit goes to the original authors and the crowdsourcing contributors.
Original source: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz
Paper: Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/speech-commands-v0.02.text-commands-2026-0419speech_commands_enriched_and_annotated
Dataset Summary
📊 Data-centric AI principles have become increasingly important for real-world use cases.At Renumics we believe that classical benchmark datasets and competitions should be extended to reflect this development.
🔍 This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
Enable new researchers to quickly… See the full description on the dataset page: https://huggingface.co/datasets/soerenray/speech_commands_enriched_and_annotated.text-commands-2026-0431text-commands-2026-0432fluent_speech_commands_femaletext-commands-2026-0412hey-computer-speech-commands
Hey Computer: Speech Command Recognition
Dataset Summary
A public, viewer-ready educational challenge dataset. Host-only scoring data and hidden targets are excluded.
Splits
Split
Examples
Description
train
13,192
Labeled training data
test
3,295
Public inputs with withheld target labels or annotations
Data Fields
Field
Type
audio
Audio
id
string
label
string (test sentinel: unlabeled)… See the full description on the dataset page: https://huggingface.co/datasets/hoangbang/hey-computer-speech-commands.linux-commands
LinLM Dataset
A curated synthetic dataset for Linux command inference
Natural language description -> shell commands
Features:
Supports 10 languages
Arch Linux commands recognition
Fine-tune LLM for development, system administration, file operations, Git, Docker, and more
Usage
from datasets import load_dataset
dataset = load_dataset("missvector/linux-commands")
def format_for_training(example):
return {
"prompt": f"Convert to Linux command:… See the full description on the dataset page: https://huggingface.co/datasets/missvector/linux-commands.text-commands-2026-0405text-commands-2026-0417bashkort_commands_omnivoice
Bashkort Commands OmniVoice
Partial eleven-label command snapshot generated with k2-fsa/OmniVoice
using the same cross-lingual voice-cloning recipe as
AigizK/homai_wake_word_omnivoice. Generation was stopped at the user's
request after 41,525 complete reference groups had been committed.
For every included reference row from the train split of:
bond005/sova_rudevices
the dataset contains one recording of every command:
Айвика — Russian
Айвикә — Bashkir
Айһылыу — Bashkir… See the full description on the dataset page: https://huggingface.co/datasets/AigizK/bashkort_commands_omnivoice.fluent_speech_commands_malemultilingual-speech-commands-15lang-zip
Multilingual Speech Commands Dataset (15 Languages, Augmented)
This dataset contains augmented speech command samples in 15 languages, derived from multiple public datasets. Only commands that overlap with the Google Speech Commands (GSC) vocabulary are included, making the dataset suitable for multilingual keyword spotting tasks aligned with GSC-style classification.
Audio samples have been augmented using standard audio techniques to improve model robustness (e.g., time-shifting… See the full description on the dataset page: https://huggingface.co/datasets/artur-muratov/multilingual-speech-commands-15lang-zip.speech_commands_v2speech_commands_enrichedThis is a set of one-second .wav audio files, each containing a single spoken
English word or background noise. These words are from a small set of commands, and are spoken by a
variety of different speakers. This data set is designed to help train simple
machine learning models. This dataset is covered in more detail at
[https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).
Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
64,727 audio files.
In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".
In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".
In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation
it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words
from unrecognized ones.
This version is not yet supported.
The `_silence_` class contains a set of longer audio clips that are either recordings or
a mathematical simulation of noise.bash-commands-dataset
🐧 Linux Command Automation Dataset
A dataset of natural language prompts paired with their corresponding Bash command-line equivalents, designed to train or fine-tune models for automating Linux tasks via natural language.
📁 Dataset Structure
The dataset is in JSON format, structured as a flat array of objects, where each object contains:
{
"prompt": "Natural language description of a task",
"response": "Equivalent Bash command"
}
✅ Example
{… See the full description on the dataset page: https://huggingface.co/datasets/aelhalili/bash-commands-dataset.text-commands-2026-0408speech_commands_pitchspeech_commands_enrichment_only
Dataset Card for SpeechCommands
Dataset Summary
📊 Data-centric AI principles have become increasingly important for real-world use cases.At Renumics we believe that classical benchmark datasets and competitions should be extended to reflect this development.
🔍 This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the… See the full description on the dataset page: https://huggingface.co/datasets/renumics/speech_commands_enrichment_only.simplified_google_speech_commands_wav2vec2_960htext-commands-2026-0430arabic_commands_detection
Dataset Card for "arabic_commands_detection"
More Information needed
text-commands-2026-0428text-commands-2026-0401text-commands-2026-0425
Commands
Clean summary of 4D language reference.
Abstract
LLMs are generally incapable of understanding 4D code. LoRA by exposure to raw source code would actually increase the rate of hallucination as the model gets confused between 4D code and C#, Visual Basic, or JavaScript.
CPT, or continued pre-training, based on grammar and vocabulary should moderate the model's attention before extensive fine-tuning using raw source code.
This dataset was generated with Grok 4.20… See the full description on the dataset page: https://huggingface.co/datasets/keisuke-miyako/text-commands-2026-0425.