Introduction

I accumulate hours of podcasts on technical subjects, and manually jotting down notes is painful. I set up a local pipeline that transcribes audio files and then summarizes them—no cloud services, guaranteed privacy, and I save tons of time reviewing content.

Why Local Audio Pipelines

  • Offline Capability: Transcribe and summarize anywhere, even on the train.
  • Cost Savings: No per-minute fees for transcription APIs.

Pipeline Overview

  1. Choose a Speech-to-Text Model – Options like Whisper from OpenAI (local), Coqui STT, or VOSK.
  2. Audio Preprocessing – Normalize sample rates and split long audio into chunks.
  3. Transcription – Run STT model locally on audio chunks.
  4. Summarization – Feed transcripts into an LLM summarizer.

I’ll go through each step with code snippets and tweaks I use for robust results.

1. Choosing & Running the STT Model

I prefer OpenAI’s Whisper model locally via whisper.cpp or the openai-whisper pip package if I have GPU.

pip install openai-whisper

import whisper

model = whisper.load_model('base')  # or 'small', 'medium'

# Transcribe a single file
result = model.transcribe('meeting.mp3', fp16=False)
print(result['text'])

For large files, I break them into 30s segments to avoid memory blowups.

2. Audio Preprocessing & Chunking

Why: Large audio can exceed model limits.

from pydub import AudioSegment

def split_audio(path, chunk_length=30*1000):  # ms
    audio = AudioSegment.from_file(path)
    chunks = []
    for i in range(0, len(audio), chunk_length):
        chunk = audio[i:i+chunk_length]
        chunk_path = f"chunks/chunk_{i//chunk_length}.wav"
        chunk.export(chunk_path, format='wav')
        chunks.append(chunk_path)
    return chunks

audio_chunks = split_audio('meeting.mp3')
print(f"Split into {len(audio_chunks)} chunks.")

I usually normalize to 16kHz mono, but Whisper handles common formats well.

3. Transcription Loop

Batch process each chunk and combine results.

all_transcripts = []
for chunk in audio_chunks:
    res = model.transcribe(chunk, fp16=False)
    all_transcripts.append(res['text'])

full_transcript = '\n'.join(all_transcripts)
with open('transcript.txt', 'w') as f:
    f.write(full_transcript)

print("Transcription complete. Length:", len(full_transcript))

At this point I skim the transcript for errors (common around names) and correct them in the text file before summarization.

4. Summarization

Why: Long transcripts aren’t fun to read—summaries get to the point. My Model: I use facebook/bart-large-cnn or a local Llama2 instance if I need longer contexts.

from transformers import pipeline

summarizer = pipeline('summarization', model='facebook/bart-large-cnn', device=0)

# Chunk transcript if >3k tokens
def summarize_transcript(text):
    return summarizer(text, max_length=150, min_length=40, do_sample=False)[0]['summary_text']

summary = summarize_transcript(full_transcript)
print("Meeting Summary:\n", summary)

# Save summary
with open('meeting_summary.txt', 'w') as f:
    f.write(summary)

Sometimes I split the transcript into 5k-token chunks and summarize each, then run a final summarization over the chunk-summaries for a multi-stage pipeline.

Wrapping Up

With this, I can download a podcast, run python transcribe_and_summarize.py podcast.mp3, and get both the full transcript and a short summary in minutes. Next, I’ll plug this into my translation pipeline so I can get subtitles in multiple languages.