Process podcast for ingestion

[adrianco]

Podcasts are usually conversations so voice recognition is needed to identify the author and extract question and answer pairs from the transcript. Similar to video ingestion.

[ksmotiv8]

it may be worthwhile to just create one issue to "recognize & label voices in an audio" and link it to both this issue and #1

[adrianco]

5

[adrianco]

I asked ChatGPT how to do this https://chatgpt.com/share/9b8628da-410b-4ced-a6bd-55d523f24815

Given a podcast URL, I'd like to download the audio file and the episode description and save them.. Then process the audio to identify the speakers and tag questions and answers for ingestion into a RAG.

To achieve the task of downloading a podcast audio file, extracting the episode description, and processing the audio to identify speakers and tag questions and answers for ingestion into a Retrieval-Augmented Generation (RAG) system, we'll break down the process into several steps. Here is a detailed guide on how to accomplish each part of this task:

Step 1: Download the Podcast Audio and Description

1. Get the Podcast URL: Ensure you have the URL of the podcast episode you wish to download.
2. Extract Audio and Description:
   • Use libraries such as yt-dlp (an enhanced fork of youtube-dl) to download the audio file and extract metadata, including the description.

Here's a Python script that accomplishes this:

import yt_dlp

def download_podcast(url, output_folder='podcast_downloads'):
    ydl_opts = {
        'format': 'bestaudio/best',
        'outtmpl': f'{output_folder}/%(title)s.%(ext)s',
        'postprocessors': [{
            'key': 'FFmpegExtractAudio',
            'preferredcodec': 'mp3',
            'preferredquality': '192',
        }],
        'writeinfojson': True,  # To get metadata including description
    }

    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        result = ydl.extract_info(url, download=True)
        audio_file = ydl.prepare_filename(result).replace('.webm', '.mp3')
        info_json = audio_file.replace('.mp3', '.info.json')

    return audio_file, info_json

# Example usage
url = 'https://example.com/podcast-episode-url'
audio_file, info_json = download_podcast(url)

Step 2: Extract Episode Description

After downloading, the description can be extracted from the JSON metadata file:

import json

def get_episode_description(info_json):
    with open(info_json, 'r', encoding='utf-8') as file:
        metadata = json.load(file)
        description = metadata.get('description', '')
    return description

# Example usage
description = get_episode_description(info_json)
print(description)

Step 3: Speaker Diarization

Speaker diarization involves identifying and separating different speakers in an audio file. We'll use pyannote.audio, a pre-trained speaker diarization library.

First, ensure you have the required libraries installed:

pip install pyannote.audio torch

Here's a script to perform speaker diarization:

from pyannote.audio import Pipeline

def diarize_speakers(audio_file):
    pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
    diarization = pipeline(audio_file)
    return diarization

# Example usage
diarization = diarize_speakers(audio_file)
print(diarization)

Step 4: Transcribe and Identify Questions and Answers

Use a transcription service like OpenAI's Whisper or Google Cloud Speech-to-Text to convert audio into text. After transcription, we can identify questions and answers using natural language processing (NLP) techniques.

Here's an example using OpenAI's Whisper:

pip install openai-whisper

import whisper

def transcribe_audio(audio_file):
    model = whisper.load_model("base")
    result = model.transcribe(audio_file)
    return result['text']

# Example usage
transcription = transcribe_audio(audio_file)
print(transcription)

Step 5: Tag Questions and Answers

Once you have the transcription, use NLP techniques to identify and tag questions and answers.

import re

def tag_questions_answers(transcription):
    # Simple regex pattern for questions
    questions = re.findall(r'(.*?\?)', transcription)
    answers = re.split(r'(.*?\?)', transcription)[1:]  # Split text based on questions

    # Pair questions with answers
    qa_pairs = []
    for i in range(len(questions)):
        question = questions[i]
        answer = answers[i * 2 + 1] if i * 2 + 1 < len(answers) else ''
        qa_pairs.append({'question': question, 'answer': answer.strip()})

    return qa_pairs

# Example usage
qa_pairs = tag_questions_answers(transcription)
for pair in qa_pairs:
    print(f"Q: {pair['question']}")
    print(f"A: {pair['answer']}\n")

Step 6: Save the Processed Information

Finally, save the audio, description, and tagged Q&A into files or a database for ingestion into a RAG system:

import os

def save_results(audio_file, description, qa_pairs, output_folder='processed_podcast'):
    os.makedirs(output_folder, exist_ok=True)

    # Save description
    with open(f"{output_folder}/description.txt", 'w', encoding='utf-8') as file:
        file.write(description)

    # Save Q&A pairs
    with open(f"{output_folder}/qa_pairs.txt", 'w', encoding='utf-8') as file:
        for pair in qa_pairs:
            file.write(f"Q: {pair['question']}\n")
            file.write(f"A: {pair['answer']}\n\n")

    # Move audio file
    os.rename(audio_file, f"{output_folder}/{os.path.basename(audio_file)}")

# Example usage
save_results(audio_file, description, qa_pairs)

Step 7: Ingest into RAG

The final step is to ingest this data into your RAG system, which typically involves:

1. Indexing the Transcription: Use a tool like Elasticsearch or FAISS to index the transcription for retrieval.
2. Storing Q&A Pairs: Save Q&A pairs in a database for structured retrieval.
3. Linking Audio and Metadata: Ensure the audio file and metadata are linked for contextual retrieval.

Here's a general outline for ingestion (details depend on your specific RAG implementation):

# Example code snippet for ingestion (specifics depend on your RAG implementation)
def ingest_into_rag(transcription, qa_pairs, audio_file):
    # Index transcription
    index_transcription(transcription)

    # Store Q&A pairs
    store_qa_pairs(qa_pairs)

    # Link audio file
    link_audio_metadata(audio_file, transcription)

# Call ingestion function
ingest_into_rag(transcription, qa_pairs, audio_file)

Tools and Libraries Used:

• yt-dlp: For downloading audio and extracting metadata.
• PyAnnote Audio: For speaker diarization.
• OpenAI Whisper: For transcription.
• Regex/NLP: For identifying questions and answers.
• Elasticsearch/FAISS: For indexing in RAG.

Notes:

• Ensure you have the correct API keys and access permissions for any external services.
• Modify regex patterns and NLP processing based on the complexity of your podcast content.

By following these steps, you can download, process, and ingest podcast content into a RAG system, enabling efficient retrieval and analysis.

[adrianco]

See issue #8 for an updated and more modular approach to developing this functionality, with somewhat different generated code.