76,000 Hours of Real Coaching, Sales & Team Calls, Fully Labeled

• coaching calls, where expert coaches work live with entrepreneurs on real problems, usually in group sessions where students take turns in the "hot seat";
• sales calls, where our sales team talks with entrepreneurs who are deciding whether to join a coaching program; and
• team and ops calls, which are real planning, training and operations meetings.

Data Product Features

• Real experts, real stakes: No actors and no scripts. People bring real businesses, real money worries and real decisions.
• Group session structure: Hot seats, Q&A and teaching blocks are marked with start and end times and the student in focus.
• Speaker roles: Every speaker is labeled coach, student or other participant. Pseudonyms are unique to each call.
• Stages, summary and themes: Each call has a summary, a main theme and supporting themes, and titled stages with times.
• Tone on every segment: A tone label (such as encouraging, frustrated, curious or relieved) and a strength of low, medium or high.
• Conversation flow: Each segment is labeled with what it does: check-in, exploration, teaching, problem statement, reframe, commitment or close.
• Coaching techniques and their effect: Each technique is labeled, with the student's emotional score before and after: up, flat, down or no response.
• Key moments: Insights, breakthroughs, resistance, redirects, topic shifts and pushback, each with a short note.
• Emotional arcs: Each speaker's path through the call, scored from -3 to +3, with notes on what triggered each shift.
• Voice measurements: Speaking speed, pitch, pitch variation, loudness and the pause before each segment.
• Quality data: Signal-to-noise ratio, clipping, speech recognition confidence and privacy checks for every call.
• Living dataset: More hours are added as we process our archive.

Distribution

• Size: About 3.3 TB of audio, plus about 21 GB of text.
• Data Volume: About 76,000 hours; 80,350 recordings; about 16.7 million transcript segments (about 220 per hour); 11 fields per segment, plus call-level data.

Usage

• AI coaching and support agents: Train agents to ask good questions, notice how someone feels, and respond with care.
• Technique-to-outcome modeling: Learn which coaching techniques help people take action, using the before-and-after emotion scores.
• AI sales assistants: Train and test sales agents on real conversations where people voice doubts, push back and decide.
• Meeting assistants: Summaries, decisions and action items from real team calls.
• Emotion recognition: Recognize feelings from both words and voice in real, multi-person conversations.
• Speech recognition and speaker separation: Improve accuracy on real Zoom calls with many speakers, microphones and accents.
• Expressive voice models: Help synthetic voices sound warm and natural.
• Evaluation sets: Test how well models follow long, real conversations.

Coverage

• Geographic Coverage: Global; most participants are in the United States, Canada, the United Kingdom and Australia.
• Time Range: 2015 - 2026.
• Demographics: Adult entrepreneurs and small-business owners, from first-time founders to experienced owners, talking with expert business coaches, a sales team and colleagues. Language: English.
• Top themes (calls that include each theme): Mindset and confidence (19,337), offer design (13,246), sales calls (6,266), launch planning (5,777), content marketing (3,905), audience building (2,857), pricing strategy (1,723), pricing objections (1,586), email marketing (1,503), goal setting (1,423).

License

AI Training Rights

• Use the Data Product to train, fine-tune, and evaluate machine learning models, including large language models.
• Incorporate Data Product content into models and commercialize resulting model outputs.
• Create derivative works (model weights, embeddings, etc.) for any lawful purpose.

• The Data Product itself may not be sold, redistributed, or shared outside of licensed usage.
• Licensee may not attempt to identify any person in the data.
• Licensee must comply with all applicable laws, including data protection and privacy regulations.

Who Can Use It

• AI labs and model builders: For training and fine-tuning conversational and voice models.
• Data scientists and ML engineers: For emotion, speech and dialogue models.
• Sales technology companies: For AI sales assistants and call-scoring tools.
• Coaching, education and support platforms: For AI coaches, tutors and support agents.
• Researchers: For studies of conversation, emotion, group dynamics and how people change.

Data Dictionary

| Column Name | Data Type | Description | Possible Values/Notes |
|-------------|-----------|-------------|-----------------------|
| call_id | string | Random ID for the call | e.g. CC-WYKCVAP5 |
| collection | string | Which collection the call belongs to | coaching_calls, sales_calls, team_calls |
| call_type | string | Kind of session | e.g. coaching_session |
| duration | number | Call length in seconds | e.g. 4289.34 |
| recording_period.year, .quarter | integer | When the call was recorded | Year and quarter only, for privacy |
| language | string | Language code | en |
| audio_quality.snr_db, .clipping, .channels | number | Signal-to-noise ratio, clipping share and channel count | |
| source_format | object | Original recording format | Container, codec, bitrate, sample rate, channels |
| asr_engine, asr_mean_confidence | string, number | Speech recognizer used and its average confidence | Confidence from 0 to 1 |
| participants[] | array | Everyone who spoke: placeholder, pseudonym, role, how the role was assigned, and confidence | Roles: coach, student, participant |
| primary_theme, secondary_themes | string, array | Main and supporting topics | e.g. mindset_confidence, offer_design, launch_planning |
| summary | string | Short summary of the whole call | |
| stages[] | array | Titled stages of the call, with start and end times, segment range and summary | |
| blocks[] | array | Session formats within the call, with the student in focus | hot_seat, qa, teaching_block |
| segments[].id | integer | Order of the segment in the call | |
| segments[].start, .end | number | Start and end time, in seconds | |
| segments[].speaker | string | Placeholder for the speaker | e.g. [Coach A], [Student S-9831] |
| segments[].text | string | What was said, with identifying details replaced | e.g. [Person 3], [Business 2], [Product 1], [Location 1] |
| segments[].tones[] | array | Tone and strength | Tone e.g. encouraging, frustrated, curious, empathetic, realization, relieved; strength: low, medium, high |
| segments[].flow_role | string | What the segment does in the conversation | e.g. check_in, exploration, teaching, problem_statement, reframe, commitment, close |
| segments[].behavior | string | Coaching technique used, if any | e.g. diagnostic_question, normalize, give_example, reframe, celebrate_win, reflect_back, commitment_confirm |
| segments[].moment | string | Key moment, if any | e.g. insight, breakthrough, resistance |
| segments[].overlap | boolean | Whether speakers talked over each other | |
| segments[].acoustics | object | Words per minute, average pitch, pitch variation, relative loudness, pause before | Can be empty for very short segments |
| emotion_notes[] | array | Emotion shifts: speaker, from tone, to tone, the segment that triggered it, and a note | |
| transitions[] | array | Turning points in the conversation, with a note | e.g. coach_redirect, topic_shift, breakthrough, student_pushback |
| emotional_arcs[] | array | Each speaker's emotional path through the call, point by point | Score from -3 to +3 |
| behavior_effects[] | array | How each technique landed: the student, their emotional score before and after, and the direction | Direction: up, flat, down, no_response |
| redaction_summary | object | Count of each kind of detail replaced | e.g. person, business, product, location, date, amount, contact |
| qc | object | Privacy and consistency checks run on the call, and their results | |
| human_checked | boolean | Whether a person has reviewed the call | Currently false for all calls |
| consent_status, consent_terms_version | string | Consent record for the call | |
| schema_version, taxonomy_version, pipeline_version | string | Versions of the file format, label set and pipeline | |