--- 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

## 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} } ```