license: cc-by-nc-4.0
language:
- en
- de
- es
multilinguality:
- multilingual
task_categories:
- automatic-speech-recognition
- audio-classification
pretty_name: Multilingual Speech Sample
dataset_info:
- config_name: all_samples
features:
- name: id
dtype: int64
- name: gender
dtype: string
- name: ethnicity
dtype: string
- name: occupation
dtype: string
- name: country_code
dtype: string
- name: birth_place
dtype: string
- name: mother_tongue
dtype: string
- name: dialect
dtype: string
- name: year_of_birth
dtype: int64
- name: years_at_birth_place
dtype: int64
- name: languages_data
dtype: string
- name: os
dtype: string
- name: device
dtype: string
- name: browser
dtype: string
- name: duration
dtype: float64
- name: emotions
dtype: string
- name: language
dtype: string
- name: location
dtype: string
- name: noise_sources
dtype: string
- name: script_id
dtype: int64
- name: type_of_script
dtype: string
- name: script
dtype: string
- name: transcript
dtype: string
- name: transcription_segments
dtype: string
- name: audio
dtype: audio
- name: speaker_id
dtype: string
splits:
- name: train
num_examples: 1196
- config_name: english_united_states
splits:
- name: train
num_examples: 277
- config_name: english_nigeria
splits:
- name: train
num_examples: 265
- config_name: english_china
splits:
- name: train
num_examples: 185
- config_name: german_germany
splits:
- name: train
num_examples: 328
- config_name: spanish_mexico
splits:
- name: train
num_examples: 141
configs:
- config_name: all_samples
data_files:
- split: train
path: data/*/train-*.parquet
- config_name: english_united_states
data_files:
- split: train
path: data/english_united_states/train-*.parquet
- config_name: english_nigeria
data_files:
- split: train
path: data/english_nigeria/train-*.parquet
- config_name: english_china
data_files:
- split: train
path: data/english_china/train-*.parquet
- config_name: german_germany
data_files:
- split: train
path: data/german_germany/train-*.parquet
- config_name: spanish_mexico
data_files:
- split: train
path: data/spanish_mexico/train-*.parquet
size_categories:
- 1K<n<10K
Silencio Network: Multilingual Accent Speech Dataset (Sample)
Overview
Silencio data is valuable because it’s collected in the wild from a massive, opt-in community (1.2M users across 180+ countries), giving buyers real-world accents, dialects, devices, and environments that lab or scraped datasets don’t capture. Every recording is tied to explicit, traceable consent and processed with privacy-first pipelines (GDPR/CCPA compliant, anonymized, PII hashed), which reduces legal risk for enterprise buyers. On top of that, the same community lets us scale quickly into hard-to-source languages and niches, so clients get both authenticity today and a credible path to large volumes tomorrow.
This dataset is a crowdsourced multilingual–accented English and non-English speech dataset designed for model training, benchmarking, and acoustic analysis. It emphasizes accent variation, short-form scripted prompts, and spontaneous free speech. All recordings were produced by contributors using their own devices, with Whisper-generated transcripts provided for every sample.
The dataset is structured for direct use in ASR, TTS, accent-classification, diarization-adjacent analysis, speech segmentation, and embedding evaluation.
Languages and Accents
This dataset covers five language–region pairs (to find out more about other combinations please reach out to us):
- English (China): English spoken with Mandarin-influenced accent
- English (Nigeria): Nigerian-accented English
- English (United States): American English
- German (Germany): Native German speakers
- Spanish (Mexico): Native Mexican Spanish speakers
All recordings are stored as 48 kHz WAV files.
Speech Types
Each sample belongs to one of three categories:
- free_speech: unscripted speech on a provided topic
- keywords: short isolated prompts containing specific phrases or terms
- monologues: longer scripted passages
These values appear in the field type_of_script.
Recording Conditions
All data is crowdsourced. Contributors record themselves using their available hardware and environment; conditions therefore vary naturally across microphones, devices, and noise profiles. No studio-grade normalisation or homogenisation is applied.
Transcription
Transcriptions are machine-generated using OpenAI Whisper, preserving its segmentation structure where applicable.
Dataset Statistics
Durations are given in hours. Counts reflect samples within each (language, region, type_of_script) partition.
English (China)
| type_of_script | duration_hrs | recordings | speakers |
|---|---|---|---|
| free_speech | 0.99 | 72 | 19 |
| keywords | 0.48 | 57 | 10 |
| monologues | 0.98 | 56 | 11 |
English (Nigeria)
| type_of_script | duration_hrs | recordings | speakers |
|---|---|---|---|
| free_speech | 0.98 | 75 | 65 |
| keywords | 0.99 | 141 | 101 |
| monologues | 0.99 | 49 | 32 |
English (United States)
| type_of_script | duration_hrs | recordings | speakers |
|---|---|---|---|
| free_speech | 0.99 | 80 | 35 |
| keywords | 0.99 | 119 | 40 |
| monologues | 0.99 | 78 | 27 |
German (Germany)
| type_of_script | duration_hrs | recordings | speakers |
|---|---|---|---|
| free_speech | 0.98 | 99 | 34 |
| keywords | 0.99 | 152 | 37 |
| monologues | 0.98 | 77 | 27 |
Spanish (Mexico)
| type_of_script | duration_hrs | recordings | speakers |
|---|---|---|---|
| free_speech | 0.98 | 90 | 6 |
| keywords | 0.05 | 6 | 2 |
| monologues | 0.70 | 45 | 9 |
File Structure
data/
english_china/
train-0000.parquet
english_nigeria/
train-0000.parquet
english_united_states/
train-0000.parquet
german_germany/
train-0000.parquet
spanish_mexico/
train-0000.parquet
Each parquet contains a mixture of free_speech, keywords, and monologues.
Feature Schema
All configurations share the same feature structure:
- id: integer (unique identifier)
- speaker_id: string (hashed or anonymized speaker ID)
- gender: string (speaker gender)
- ethnicity: string (speaker ethnicity)
- occupation: float (occupation or profession, stored as float per original schema)
- country_code: string (ISO 3166-1 alpha-2 code)
- birth_place: string (country or region of birth)
- mother_tongue: string (native language)
- dialect: string (regional dialect)
- year_of_birth: int (birth year, YYYY)
- years_at_birth_place: int (years lived at birth place)
- languages_data: string (serialized language–proficiency data)
- os: string (recording operating system)
- device: string (recording device type)
- browser: string (browser used if web-based)
- duration: float (seconds) (audio length)
- emotions: string (brace-formatted emotion labels)
- language: string (primary language of the recording)
- location: string (recording location category)
- noise_sources: string (brace-formatted background noise labels)
- script_id: int (script template identifier)
- type_of_script: string {free_speech, keywords, monologues} (script category)
- script: string (text intended to be spoken)
- transcript: string (Whisper-generated transcription)
- transcription_segments: string (serialized segmentation with timing and word data)
- audio: WAV audio object (associated audio file)
Licensing
Released under CC BY-NC 4.0.
Commercial use is not permitted. Attribution to Silencio Network is required for any publication or derivative dataset.
Intended Use
Suitable for:
- accent-conditioned ASR training
- multilingual speech recognition
- TTS voicebank generation
- speaker embedding and similarity evaluation
- robustness benchmarking
- keyword-spotting models
- segmentation and VAD evaluation
Limitations
- Transcripts are automatically generated. Errors may be present.
- Crowdsourced device diversity introduces variable noise levels.
Citation
@dataset{silencio_network_speech_2025,
title = {Silencio Network Multilingual Accent Speech Corpus},
author = {Silencio Network},
year = {2025},
license = {CC BY-NC 4.0}
}