Alright, let's say you've bought into the hype and are using Speechify to turn your blog posts into narrated audio for a podcast feed. Fine. The marketing makes it sound like a one-click miracle, but you and I know the real work starts when you need to get those generated MP3s into a reliable, automated pipeline that feeds your actual podcast RSS. I've just finished cobbling this together for our internal tech blog, so here's the gritty reality.
First, understand that Speechify's "integrations" are mostly just cloud storage dumps. You'll be getting audio files dumped into a Google Drive or Dropbox folder. That's your source. The goal is to watch that folder, pull new files, process metadata, and push to your podcast hosting storage (I'm using an S3 bucket behind a CDN, because it's cheap and I control it). Then, you need to update your RSS XML file. There's no magic connector for this; it's a classic "glue code" problem.
Here's the core of the pipeline I built using a simple Python script on a cron job (because not everything needs Kubernetes). It uses the Google Drive API, since that's where our Speechify outputs land.
```python
# speechify_to_podcast.py - The unglamorous workhorse.
import os
from datetime import datetime
from pathlib import Path
import boto3
from google.oauth2.service_account import Credentials
from googleapiclient.discovery import build
from googleapiclient.http import MediaIoBaseDownload
import feedgenerator
# Config - Keep this out of your repo, folks.
DRIVE_FOLDER_ID = "your_speechify_folder_id_here"
S3_BUCKET = "your-podcast-bucket"
RSS_FILE_PATH = "/var/www/podcast/feed.xml"
LOCAL_STAGING = Path("/tmp/podcast_staging")
def get_new_speechify_files():
"""Connect to Drive, list new MP3s since last run."""
# Auth setup omitted for brevity. Use a service account.
service = build('drive', 'v3', credentials=creds)
query = f"'{DRIVE_FOLDER_ID}' in parents and mimeType='audio/mpeg'"
results = service.files().list(q=query, fields="files(id, name, createdTime)").execute()
files = results.get('files', [])
# Filter for new files since last run (track last run time in a file)
return files
def process_file(file_metadata):
"""Download, sanitize filename, extract title from filename (ugh), upload to S3."""
clean_name = file_metadata['name'].replace(" ", "_").lower()
local_path = LOCAL_STAGING / clean_name
# Download via Drive API MediaIoBaseDownload...
# Upload to S3
s3 = boto3.client('s3')
s3_key = f"episodes/{clean_name}"
s3.upload_file(str(local_path), S3_BUCKET, s3_key, ExtraArgs={'ContentType': 'audio/mpeg'})
return f"https://cdn.yourdomain.com/{s3_key}"
def update_rss_feed(episode_title, audio_url, pub_date):
"""Read existing RSS, append new item, write back."""
# Use feedgenerator or manually parse/update your RSS.
# This is where you'd add your show notes, maybe pulled from the original blog post via an API.
pass
if __name__ == "__main__":
new_files = get_new_speechify_files()
for file in new_files:
audio_url = process_file(file)
# You'll need to map the filename to a proper title - I use a separate mapping file.
update_rss_feed(file['name'], audio_url, datetime.now())
print(f"Processed {len(new_files)} new episodes.")
```
The pitfalls you'll inevitably hit:
* **Metadata Poverty:** Speechify output filenames *are* your primary metadata source. You'll need a manual mapping step or a separate metadata file (JSON) to get proper episode titles, descriptions, and show notes. The audio file itself contains none of this.
* **No Pub Date Guarantees:** The `createdTime` from Drive is when Speechify uploaded it, not your blog post date. You'll need to correlate back to your source content.
* **Cost Creep:** This script runs on a cheap VM, but if you have a high volume, watch the API calls to Drive and egress from S3. It adds up.
* **Idempotency:** Make sure your script can run multiple times without creating duplicate entries in your RSS. A simple `processed_files.log` is your friend.
Is it worth it? For our use case, where we repurpose 3-4 technical posts a week into audio, it's a net positive *after* the initial setup headache. But you're not buying a solution; you're buying a new source of raw data that requires a classic ETL pipeline to make useful. Some things never change.
-- old salt