SilencioNetwork/complete-voiceai-speech-dataset

🎙️ Silencio Network: Voice AI Sample Dataset

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📊 This is a sample. The full Silencio corpus contains 100,000+ hours across 170+ countries and 100+ languages.

📧 Contact: sofia@silencioai.com for custom datasets, bulk licensing, or specific language requests.

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🌍 Why Silencio Data?

Silencio data is collected in the wild from a massive, opt-in community (2M+ contributors across 180+ countries), giving you:

• ✅ Real-world accents, dialects, devices, and environments that lab or scraped datasets don't capture
• ✅ Explicit, traceable consent — every recording tied to verified opt-in (GDPR/CCPA compliant)
• ✅ Privacy-first pipelines — anonymized, PII hashed, reduced legal risk for enterprise
• ✅ Rapid scaling into hard-to-source languages and niches

📊 Full Data Availability

Category	Available	Top Languages
African Languages	25,000+ hrs	Swahili (9.7k), Nigerian English (8.1k), Hausa, Yoruba, Zulu
Asian Languages	15,000+ hrs	Hindi, Mandarin, Japanese, Korean, Vietnamese
European Languages	20,000+ hrs	German, Spanish, French, Portuguese, Polish
Emotional Speech	5,000+ hrs	Joy, anger, sadness, fear, surprise, neutral
Multi-Speaker	10,000+ hrs	Overlapping speech, turn-taking, interruptions
Conversational	30,000+ hrs	Natural dialogue, disfluencies, backchannels

Custom requests? We can source virtually any language, accent, or demographic at scale.

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🎯 This Sample Dataset

This sample covers 43 language–region configs across 20+ languages demonstrating our data quality:

Accented English

Config	Accent/Region	Splits	Samples
english_algeria	Algerian	free_speech	21
english_australia	Australian	free_speech	25
english_belarus	Belarusian	free_speech	21
english_china	Mandarin-influenced	free_speech, keywords, monologues	75
english_egypt	Egyptian	free_speech	25
english_french_speaking	French-speaking countries	free_speech	21
english_haiti	Haitian	free_speech	21
english_ireland	Irish	free_speech	21
english_jamaica	Jamaican	free_speech	25
english_kenya	Kenyan	free_speech	25
english_mandarin	Mandarin-influenced (Global)	free_speech	25
english_nigeria	Nigerian	free_speech, keywords, monologues	75
english_pakistan	Pakistani	free_speech	25
english_poland	Polish	free_speech	25
english_russia	Russian	free_speech	25
english_south_africa	South African	free_speech	17
english_uganda	Ugandan	free_speech	25
english_united_kingdom	British	free_speech	25
english_united_states	American	free_speech, keywords, monologues	75
english_ukraine	Ukrainian	free_speech	25

African Languages

Config	Language	Region	Splits	Samples
amharic_ethiopia	Amharic	Ethiopia	free_speech	51
hausa_nigeria	Hausa	Nigeria	free_speech	42
igbo_nigeria	Igbo	Nigeria	free_speech	36
yoruba_nigeria	Yoruba	Nigeria	free_speech	39

Other Languages

Config	Language	Region	Splits	Samples
french_canada	French	Canada	free_speech	25
french_global	French	Global	free_speech	25
german_germany	German	Germany	free_speech, keywords, monologues	75
gujarati_india	Gujarati	India	free_speech	18
italian_italy	Italian	Italy	free_speech	25
japanese_japan	Japanese	Japan	free_speech	25
kannada_india	Kannada	India	free_speech	25
malayalam_india	Malayalam	India	free_speech	25
mandarin_chinese_china	Mandarin	China	free_speech	25
marathi_india	Marathi	India	free_speech	25
odia_india	Odia	India	free_speech	21
portuguese_brazil	Portuguese	Brazil	free_speech	25
russian_russia	Russian	Russia	free_speech	25
spanish_mexico	Spanish	Mexico	free_speech, keywords, monologues	56
telugu_india	Telugu	India	free_speech	25
turkish_turkey	Turkish	Turkey	free_speech, monologues	32
vietnamese_vietnam	Vietnamese	Vietnam	monologues	15

Medical Domain

Config	Language	Splits	Samples
english_medical	English	medical	8
global_medical	Multilingual (EN/ES/DE)	medical	25

🚀 Quick Start

from datasets import load_dataset

# Load Nigerian English samples
ds = load_dataset("SilencioNetwork/complete-voiceai-speech-dataset", "english_nigeria")

# Access different speech types
free_speech = ds['free_speech']
keywords = ds['keywords']
monologues = ds['monologues']

# Process a sample
for sample in free_speech:
    audio = sample['audio']
    transcript = sample['transcript']
    speaker_id = sample['speaker_id']
    emotion = sample['emotions']
    print(f"Speaker {speaker_id}: {transcript[:50]}...")

🎤 Speech Types

Type	Description	Use Cases
free_speech	Unscripted speech on provided topics	Conversational AI, dialogue systems
keywords	Short isolated phrases/terms	Wake word detection, command recognition
monologues	Longer scripted passages	TTS training, ASR benchmarking

📋 Rich Metadata

Every recording includes:

• Speaker demographics: gender, ethnicity, occupation, birth year, birth place
• Linguistic info: mother tongue, dialect, language proficiency data
• Recording context: device, OS, browser, location, background noise
• Content: script, Whisper-generated transcript, emotion labels
• Technical: duration, 48kHz WAV format

🎯 Ideal For

• 🗣️ ASR training — accent-robust speech recognition
• 🔊 TTS development — diverse voice synthesis
• 😊 Emotion recognition — labeled emotional speech
• 🌍 Multilingual models — 100+ languages available
• 🎙️ Speaker verification — unique speaker embeddings
• 📊 Benchmarking — real-world robustness testing

⚖️ License

CC BY-NC 4.0 — Free for research and non-commercial use.

For commercial licensing, contact sofia@silencioai.com.

📧 Get More Data

Need the full corpus? This sample represents <0.01% of our available data.

What You Need	We Can Provide
Specific language/accent	✅ 100+ languages available
Emotional speech	✅ 5,000+ hours labeled
Multi-speaker/overlapping	✅ 10,000+ hours
Custom demographics	✅ Age, gender, occupation targeting
Bulk volume	✅ 100,000+ hours total

📧 Email: sofia@silencioai.com

🌐 Website: silencioai.com

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Citation

@dataset{silencio_network_speech_2025,
    title        = {Silencio Network Voice AI Speech Corpus},
    author       = {Silencio Network},
    year         = {2025},
    publisher    = {Hugging Face},
    license      = {CC BY-NC 4.0}
}