> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pixeltable.com/llms.txt
> Use this file to discover all available pages before exploring further.
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# Summarize podcasts and audio
This documentation page is also available as an interactive notebook. You can launch the notebook in
Kaggle or Colab, or download it for use with an IDE or local Jupyter installation, by clicking one of the
above links.
export const quartoRawHtml = [`
| Content |
Duration |
Need |
| Podcast episodes |
60 min |
Episode summary + key points |
| Meeting recordings |
30 min |
Action items + decisions |
| Interviews |
45 min |
Main topics + quotes |
`, `
| title |
transcript_text |
| Pixeltable Tour |
This conversation is powered by Google Illuminate. Check out
illuminate.google.com for more. Welcome to this discussion on Pixel
Table, a powerful tool for managing and manipulating data, especially
image data, within a database framework. We'll be exploring how it
simplifies, working with machine learning tasks, particularly object
detection. What's the core concept behind Pixel Table that makes it so
unique? Pixel Table's core strength lies in its combination of a
database system with the ...... What kind of users would benefit most
from using Pixel Table? Data scientists, machine learning engineers, and
anyone working with large data sets and complex ML pipelines would find
Pixel Table extremely beneficial. Its ability to manage data,
transformations, and model applications in a unified and persistent
environment makes it a powerful tool for streamlining workflows. This
has been a very informative discussion on Pixel Table. Thank you for
explaining its capabilities and advantages. |
`, `
| title |
summary |
| Pixeltable Tour |
The conversation discusses Pixel Table, a tool designed for managing
and manipulating image data within a database system, especially useful
for machine learning tasks like object detection. It highlights Pixel
Table's unique feature of computed columns that streamline data
transformations and model applications, making workflows more efficient
by automating tasks like data updates and API calls. The tool’s
integration with ML models and the ability to define user-defined
functions (UDFs) pr ...... lity with computed columns, allowing
automatic data transformations and model executions to streamline
workflows. 2. It enables easy integration of various machine learning
models, such as DETR and OpenAI's GPT-4-0, managing processes like image
analysis and result storage efficiently. 3. While providing significant
advantages in scalability and workflow management, Pixel Table requires
some technical expertise for database setup and may face performance
limitations based on data complexity. |
`, `
| Model |
Size |
Speed |
Accuracy |
tiny.en |
39M |
Fastest |
Good for clear speech |
base.en |
74M |
Fast |
Balanced |
small.en |
244M |
Medium |
Better accuracy |
medium.en |
769M |
Slow |
High accuracy |
`];
Transcribe audio files and generate summaries automatically using
Whisper and LLMs.
## Problem
You have podcast episodes, meeting recordings, or interviews that need
both transcription and summarization. Doing this manually is
time-consuming and doesn’t scale.
## Solution
**What’s in this recipe:**
* Transcribe audio with Whisper (runs locally)
* Generate summaries with an LLM
* Chain transcription → summarization automatically
You create a pipeline where audio is transcribed first, then the
transcript is summarized. Both steps run automatically when you insert
new audio files.
### Setup
```python theme={null}
%pip install -qU pixeltable openai-whisper openai
```
```python theme={null}
import getpass
import os
if 'OPENAI_API_KEY' not in os.environ:
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key: ')
```
```python theme={null}
import pixeltable as pxt
from pixeltable.functions import openai, whisper
```
```python theme={null}
# Create a fresh directory
pxt.drop_dir('podcast_demo', force=True)
pxt.create_dir('podcast_demo')
```
Created directory 'podcast\_demo'.
\
### Create the pipeline
Create a table with audio input, then add computed columns for
transcription and summarization:
```python theme={null}
# Create table for audio files
podcasts = pxt.create_table(
'podcast_demo/episodes', {'title': pxt.String, 'audio': pxt.Audio}
)
```
Created table 'episodes'.
```python theme={null}
# Step 1: Transcribe with local Whisper (uses GPU if available)
podcasts.add_computed_column(
transcription=whisper.transcribe(podcasts.audio, model='base.en')
)
```
Added 0 column values with 0 errors.
No rows affected.
```python theme={null}
# Extract the text from transcription result (cast to String for concatenation)
podcasts.add_computed_column(
transcript_text=podcasts.transcription.text.astype(pxt.String)
)
```
Added 0 column values with 0 errors.
No rows affected.
```python theme={null}
# Step 2: Summarize the transcript with OpenAI
summary_prompt = (
"""Summarize this transcript in 2-3 sentences, then list 3 key points.
Transcript:
"""
+ podcasts.transcript_text
)
podcasts.add_computed_column(
summary_response=openai.chat_completions(
messages=[{'role': 'user', 'content': summary_prompt}],
model='gpt-4o-mini',
)
)
```
Added 0 column values with 0 errors.
No rows affected.
```python theme={null}
# Extract summary text from response
podcasts.add_computed_column(
summary=podcasts.summary_response.choices[0].message.content
)
```
Added 0 column values with 0 errors.
No rows affected.
### Process audio files
Insert audio files and watch the pipeline run automatically:
```python theme={null}
# Insert sample audio
audio_url = 'https://github.com/pixeltable/pixeltable/raw/main/docs/resources/10-minute%20tour%20of%20Pixeltable.mp3'
podcasts.insert([{'title': 'Pixeltable Tour', 'audio': audio_url}])
```
Inserting rows into \`episodes\`: 1 rows \[00:00, 185.18 rows/s]
Inserted 1 row with 0 errors.
1 row inserted, 8 values computed.
```python theme={null}
# View transcript
podcasts.select(podcasts.title, podcasts.transcript_text).collect()
```
```python theme={null}
# View summary
podcasts.select(podcasts.title, podcasts.summary).collect()
```
## Explanation
**Pipeline architecture:**
Audio → Whisper transcription → Transcript text → LLM summarization → Summary
Each step is a computed column that depends on the previous one. When
you insert a new audio file, all steps run automatically in sequence.
**Whisper model options:**
For production with varied audio quality, use `small.en` or larger.
## See also
* [Transcribe
audio](/howto/cookbooks/audio/audio-transcribe) -
Basic audio transcription
* [Summarize
text](/howto/cookbooks/text/text-summarize) -
Text summarization patterns
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