Open adrianco opened 2 months ago
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
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:
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)
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)
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)
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)
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")
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)
The final step is to ingest this data into your RAG system, which typically involves:
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)
By following these steps, you can download, process, and ingest podcast content into a RAG system, enabling efficient retrieval and analysis.
See issue #8 for an updated and more modular approach to developing this functionality, with somewhat different generated code.
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.