audioFile string | duration float64 | numberOfSpeakers int64 | language string | environment string | location dict | venueType string | backgroundSounds string | topics list | conversationType string | mainConversationTopic string | emotionTone string | data dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
conversation_1.m4a | 23.859 | 3 | deu | outdoor_urban | {
"city": "Munich",
"country": "Germany",
"lat": "48.137154",
"long": "11.576124"
} | speech, music, traffic | [
"Playing Football",
"Bayern Munich Match Result",
"Kane Goals",
"Sané Performance"
] | casual_chat | Sports | friendly | {
"transcript": "Habt ihr gestern Fußball angeschaut? Nein. Ne, du nicht. Und? Bayern hat vier zwei gewonnen. Vier. Zwei? Gegen? Sind doch eh Scheiße. Gegen irgendein brasilianisches Team. Ah, Flamengo. Flumenzeno oder so was. Wie haben sie gewonnen? Vier zwei. Wettgeschossen. Cane zwei. Ah ja. Best. Danke. Nee, hat ... | |
conversation_2.m4a | 77.079 | 3 | deu | outdoor_urban | {
"city": "Munich",
"country": "Germany",
"lat": "48.137154",
"long": "11.576124"
} | speech, traffic | [
"Inter Miami",
"Soccer World Cup Anticipation",
"World Cup Schedule and Venue",
"Club World Cup Game Cancellations",
"Player Motivation for Tournaments",
"Vacation Time and Player Workload",
"Bayern Munich Bundesliga Break",
"Sustainable Development"
] | casual_chat | Sports | friendly | {
"transcript": "Inter Mailand bot, äh, Inter Miami bot dir richtig hin. Ja, aber Inter Miami ist noch madig. Ja. Ja, und heute ich auf die WM. (lacht) Alles Fußball? Ja, sure. Aber... Wann jetzt die WM? Kommendes Jahr. Soll es nicht in Englisch sein? Freust du dich jetzt schon. In Amerika. Das Problem ist, dass sie ... | |
conversation_3.m4a | 59.44 | 2 | deu | outdoor_urban | {
"city": "Munich",
"country": "Germany",
"lat": "48.137154",
"long": "11.576124"
} | speech, music, traffic | [
"Running in Bavaria",
"Travel Time and Being on the Road",
"Sports Enthusiasm and Compensation",
"Golf vs. Football as Professional Sports"
] | casual_chat | Sports | friendly | {
"transcript": "Begeistert, den ganzen Tag 'n Wein hinterherzurennen. Mhhhm Jeden Tag. Ich könnte es schon machen, muss ich ehrlich sagen. Meinst du? Ja, das ist so geil. Du bist ein Drittel des Jahres wahrscheinlich unterwegs. Mhhhm Du kriegst halt immer geile Massages und so, aber Du machst halt eine Sportart für ... | |
conversation_4.m4a | 49.62 | 3 | deu | outdoor_urban | {
"city": "Munich",
"country": "Germany",
"lat": "48.137154",
"long": "11.576124"
} | speech, music, crowd, traffic | [
"Tennis Skill Level",
"Sports Comparison",
"Tournament Substitute Players",
"Football vs Tennis Player Comparison",
"AI Recording",
"Fishing Guide River Plate Story"
] | casual_chat | Sports | friendly | {
"transcript": "Und der ist schon lange. Der ist ja geil. Beste dreißig. Der ist keine Spielerwelt, der ist schon hart. Prozentual ist wahrscheinlich, ich weiß nicht, wo ist es prozentual schwieriger, dich da reinzukommen? Definitiv im Tennis und Golf. Du kannst randomly auch mal ein Turnier gewinnen als Ersatzspiel... | |
conversation_5.m4a | 56.479 | 3 | deu | outdoor_urban | {
"city": "Munich",
"country": "Germany",
"lat": "48.137154",
"long": "11.576124"
} | speech, music, crowd, traffic | [
"River Plate Youth Academy Program",
"Player Selection Process",
"Injury Impact on Career"
] | casual_chat | Sports | friendly | {
"transcript": "Riverplate, 400 Spieler aus Argentinien und sponsert die Essen, School, alles. Die haben ein Jahr sich zu beweisen. Und von diesem einen Jahr, weiß ich nicht, werde halt 10% übernommen, für den Kader, Jugendkader und so, und dann andere 10% rausgehauen. Also super kompetitiv. Von denen schaffen es wi... |
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Check out the documentation for more information.
Conversations Dataset
A collection of transcribed conversations focusing on sports-related discussions, particularly about football, tennis, and other athletic topics. The dataset includes detailed speaker information, background sounds, and precise timing for each utterance.
Dataset Description
This dataset contains natural conversations recorded in urban outdoor settings in Munich, Germany. Each conversation includes:
- Audio files in M4A format
- Detailed transcriptions with speaker turns
- Temporal information (start/end times for each utterance)
- Speaker metadata (gender, speaker ID)
- Environmental context (location, background sounds)
- Conversation metadata (topics, emotion tone, type)
Key Features
- Language: German (deu)
- Environment: Outdoor urban settings
- Location: Munich, Germany
- Number of Conversations: 5
- Average Duration: ~47 seconds per conversation
- Number of Speakers: 2-3 per conversation
- Background Sounds: Includes music, traffic, and crowd noise
- Conversation Type: Casual chat
- Primary Topics: Sports (football, tennis, golf)
Data Format
Each conversation entry contains:
{
"audioFile": "string",
"duration": "float",
"numberOfSpeakers": "int",
"language": "string",
"environment": "string",
"location": {
"city": "string",
"country": "string",
"lat": "string",
"long": "string"
},
"backgroundSounds": "string",
"topics": ["string"],
"conversationType": "string",
"mainConversationTopic": "string",
"emotionTone": "string",
"data": {
"transcript": "string",
"detailedConversation": [
{
"speaker": "string",
"text": "string",
"start": "float",
"end": "float",
"speakerGender": "string"
}
]
}
}
Usage
This dataset can be useful for:
- Speech recognition model training (German language)
- Conversation analysis
- Speaker diarization
- Natural language processing tasks
- Urban background noise analysis
- Sports-related language processing
Loading the Dataset
from datasets import load_dataset
dataset = load_dataset("maxF6YsK/conversations")
Dataset Statistics
- Total number of conversations: 5
- Total duration: ~235 seconds
- Average speakers per conversation: 2.6
- Main topics: Sports discussions (football, tennis, golf)
- Emotion tone: Friendly
- Conversation type: Casual chat
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