| --- |
| 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} |
| } |
| ``` |
