ReadyAi/5000-podcast-conversations-with-metadata-and-embedding-dataset

🗂️ ReadyAI - 5,000 Podcast Conversations with Metadata and Embedding Dataset

ReadyAI, operating subnet 33 on the Bittensor Network is an open-source initiative focused on low-cost, resource-minimal pipelines for structuring raw data for AI applications. This dataset is part of the ReadyAI Conversational Genome Project, leveraging the Bittensor decentralized network.

AI runs on structured data — and this dataset bridges the gap between raw conversation transcripts and structured, vectorized semantic tags.

You can find more about our subnet on GitHub here.

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Full Vectors Access

➡️ Download the full 45 GB conversation tags embeddings from here

For large-scale processing and fine-tuning.

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📦 Dataset Versions

In addition to the full dataset, two smaller versions are available:

• Small version

  • Located in the small_dataset folder.
  • Contains 1,000 conversations with the same file structure as the full dataset.
  • All filenames are prefixed with small_.

• Medium version

  • Located in the medium_dataset folder.
  • Contains 2,500 conversations
  • Also using the same structure and medium_ prefix for all files.

These subsets are ideal for lightweight experimentation, prototyping, or benchmarking.

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📋 Dataset Overview

This dataset contains annotated conversation transcripts with:

• Human-readable semantic tags
• Embedding vectors contextualized to each conversation
• Participant metadata

It is ideal for:

• Semantic search over conversations
• AI assistant training (OpenAI models, fine-tuning)
• Vector search implementations using pg_vector and Pinecone
• Metadata analysis and tag retrieval for LLMs

The embeddings were generated with the text-embedding-ada-002 model and have 1536 dimensions per tag.

📂 Dataset Structure

The dataset consists of four main components:

1. data/bittensor-conversational-tags-and-embeddings-part-*.parquet — Tag Embeddings and Metadata

Each Parquet file contains rows with:

Column	Type	Description
c_guid	int64	Unique conversation group ID
tag_id	int64	Unique identifier for the tag
tag	string	Semantic tag (e.g., "climate change")
vector	list of float32	Embedding vector representing the tag's meaning in the conversation's context

✅ Files split into ~1GB chunks for efficient loading and streaming.

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2. tag_to_id.parquet — Tag Mapping

Mapping between tag IDs and human-readable tags.

Column	Type	Description
tag_id	int64	Unique tag ID
tag	string	Semantic tag text

✅ Useful for reverse-mapping tags from models or outputs.

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3. conversations_to_tags.parquet — Conversation-to-Tag Mappings

Links conversations to their associated semantic tags.

Column	Type	Description
c_guid	int64	Conversation group ID
tag_ids	list of int64	List of tag IDs relevant to the conversation

✅ For supervised training, retrieval tasks, or semantic labeling.

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4. conversations_train.parquet — Full Conversation Text and Participants

Contains the raw multi-turn dialogue and metadata.

Column	Type	Description
c_guid	int64	Conversation group ID
transcript	string	Full conversation text
participants	list of strings	List of speaker identifiers

✅ Useful for dialogue modeling, multi-speaker AI, or fine-tuning.

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🚀 How to Use

Install dependencies

pip install pandas pyarrow datasets

Download the dataset

import datasets

path = "ReadyAi/5000-podcast-conversations-with-metadata-and-embedding-dataset"
dataset = datasets.load_dataset(path)

print(dataset['train'].column_names)

Load a single Parquet split

import pandas as pd

df = pd.read_parquet("data/bittensor-conversational-tags-and-embeddings-part-0000.parquet")
print(df.head())

Load all tag splits

import pandas as pd
import glob

files = sorted(glob.glob("data/bittensor-conversational-tags-and-embeddings-part-*.parquet"))
df_tags = pd.concat((pd.read_parquet(f) for f in files), ignore_index=True)

print(f"Loaded {len(df_tags)} tag records.")

Load tag dictionary

tag_dict = pd.read_parquet("tag_to_id.parquet")
print(tag_dict.head())

Load conversation to tags mapping

df_mapping = pd.read_parquet("conversations_to_tags.parquet")
print(df_mapping.head())

Load full conversations dialog and metadata

df_conversations = pd.read_parquet("conversations_train.parquet")
print(df_conversations.head())

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🔥 Example: Reconstruct Tags for a Conversation

# Build tag lookup
tag_lookup = dict(zip(tag_dict['tag_id'], tag_dict['tag']))

# Pick a conversation
sample = df_mapping.iloc[0]
c_guid = sample['c_guid']
tag_ids = sample['tag_ids']

# Translate tag IDs to human-readable tags
tags = [tag_lookup.get(tid, "Unknown") for tid in tag_ids]

print(f"Conversation {c_guid} has tags: {tags}")

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📦 Handling Split Files

Situation	Strategy
Enough RAM	Use pd.concat() to merge splits
Low memory	Process each split one-by-one
Hugging Face datasets	Use streaming mode

Example (streaming with Hugging Face datasets)

from datasets import load_dataset

dataset = load_dataset(
    "ReadyAi/5000-podcast-conversations-with-metadata-and-embedding-dataset",
    split="train",
    streaming=True
)

for example in dataset:
    print(example)
    break

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📜 License

MIT License
✅ Free to use and modify

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✨ Credits

Built using contributions from Bittensor conversational miners and the ReadyAI open-source community.

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🎯 Summary

Component	Description
parquets/part_*.parquet	Semantic tags and their contextual embeddings
tag_to_id.parquet	Dictionary mapping of tag IDs to text
conversations_to_tags.parquet	Links conversations to tags
conversations_train.parquet	Full multi-turn dialogue with participant metadata