README.md

---
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)

<p align="left">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/69162b50b89e7abe20de4b5a/LWhs4p2lPFcyiVsP0tluu.png" width="40%">
</p>

## 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}
}
```