Podcast Transcribe

Overview

Automate the full pipeline of podcast content acquisition: discover RSS feed, download all episodes, transcribe audio to text via AuralWise API, and generate structured content overviews for each episode. Output includes audio files, Show Notes, full transcripts (.txt / .md / .srt), and AI-generated episode summaries with extracted books, concepts, and keywords.

Workflow

Step 1: Discover RSS Feed

Determine the podcast's RSS Feed URL. Use one of these methods in priority order:

1. User provides URL directly — proceed to Step 2.
2. Apple iTunes Search API — search by podcast name:

   GET https://itunes.apple.com/search?term={podcast_name}&media=podcast&limit=5
   

   Extract feedUrl from results. See references/rss_discovery.md for details.
3. Platform-specific patterns — e.g., Ximalaya albums use https://www.ximalaya.com/album/{id}.xml.

Verify the RSS Feed is accessible by fetching it and confirming episode count.

Step 2: Configure AuralWise API Key

The transcription service requires an AuralWise API Key. Handle this interactively:

1. Check if API Key is already configured — look for a .env file in the working directory containing AURALWISE_API_KEY=asr_.... If found and valid, skip to Step 3.
2. If no API Key — ask the user:

   "语音转写需要 AuralWise API Key。请到 https://auralwise.cn/refid=asgbifle 注册并获取 API Key（在 Settings → API Key 管理页面生成，格式为 asr_ 开头）。"

3. Write the Key to .env — create {working_dir}/.env with:

   AURALWISE_API_KEY=asr_user_provided_key
   

4. Verify the Key — call GET https://api.auralwise.cn/v1/account with header X-API-Key: {key} to confirm balance and concurrency.
5. If user only wants downloads (no transcription) — use --download-only flag, skip API Key requirement.

See references/auralwise_api.md for full API documentation.

Step 3: Run the Pipeline

Execute the bundled pipeline script. The script handles RSS parsing, audio downloading, transcription submission/polling, and multi-format output saving.

Prerequisites:

• Python 3 with requests installed
• The pipeline script: scripts/podcast_pipeline.py

Basic command:

python3 scripts/podcast_pipeline.py \
  --rss-url "https://example.com/feed.xml" \
  --podcast-name "播客名称" \
  --env-file .env

Test mode (recommended first run) — process 2 shortest episodes:

python3 scripts/podcast_pipeline.py \
  --rss-url "https://example.com/feed.xml" \
  --podcast-name "播客名称" \
  --env-file .env \
  --test 2

Download only (no API Key needed):

python3 scripts/podcast_pipeline.py \
  --rss-url "https://example.com/feed.xml" \
  --podcast-name "播客名称" \
  --download-only

Key CLI parameters:

Output directory structure:

{output_dir}/
├── 音频/              # Downloaded audio files (.m4a)
├── 文稿/              # Show Notes (.md)
├── 文字稿/            # Transcripts
│   ├── EP01-xxx.txt          # Plain text
│   ├── 带时间戳_EP01-xxx.md  # Markdown with timestamps
│   └── EP01-xxx.srt          # SRT subtitles
├── pipeline_state.json  # Resume state
└── pipeline.log         # Run log

Cost estimation: AuralWise optimize tier = 0.27 CNY/hour. For a podcast with ~170 hours of audio, total cost is ~46 CNY.

Step 4: Generate Content Overviews

After transcripts are generated, create a structured overview Markdown file for each episode. This is an AI-generated summary — read the .txt transcript and produce a 概览_{filename}.md file in the same 文字稿/ directory.

Overview file format:

# {Episode Title}

## 播客信息

| 字段 | 内容 |
|------|------|
| 播客 | {Podcast name} |
| 期号 | EP{number} |
| 时长 | {duration} |
| 发布日期 | {date} |

## 内容摘要

{3-5 paragraph summary of what this episode covers}

## 提到的书籍

| 书名 | 作者 | 在本期中的角色 |
|------|------|----------------|
| {Book title} | {Author} | {How it's referenced} |

## 核心概念速查

| 概念 | 一句话解释 |
|------|-----------|
| {Concept} | {Brief explanation} |

## 关键案例

| 案例 | 说明 | 对应理论 |
|------|------|----------|
| {Case} | {Description} | {Related concept} |

## 金句提炼

> {Notable quote 1}
> {Notable quote 2}

## 检索关键词

`keyword1` `keyword2` `keyword3` ...

Overview generation guidelines:

• Read the full .txt transcript file to understand the episode content
• Extract all book titles mentioned (look for patterns like "《书名》", "the book", author names)
• Identify 5-15 core concepts discussed in depth
• Pull 2-5 memorable quotes verbatim from the transcript
• Generate 10-20 searchable keywords wrapped in backticks for easy full-text search
• Keep summaries concise but information-dense — the goal is fast retrieval, not replacement of the original transcript
• Save as 概览_{original_txt_filename_without_ext}.md in the same 文字稿/ directory

Step 5: Resume / Batch Processing

The pipeline supports 断点续传 (resume). If interrupted:

• Re-run the same command — completed episodes are automatically skipped
• State is tracked in pipeline_state.json
• If AuralWise balance runs out mid-run, remaining episodes are downloaded but transcription is skipped
• After recharging, re-run to process remaining episodes

Important Notes

• Audio URL accessibility: AuralWise transcribes by fetching the audio URL directly — no need to download then upload. Verify URLs are publicly accessible.
• Ximalaya feeds: Audio URLs from Ximalaya RSS feeds are publicly accessible and work directly with AuralWise.
• Xiaoyuzhou (小宇宙): Their API requires login tokens. Use the iTunes Search API to find the same podcast's RSS feed on Ximalaya or Apple Podcasts instead.
• Rate limiting: AuralWise has a default concurrency limit of 3. The script handles 429 responses with retry.
• Encoding: All output files are UTF-8 encoded. Filenames are sanitized to avoid OS-level issues.

Resources

• scripts/podcast_pipeline.py — Main pipeline script (parameterized, supports any RSS feed)
• scripts/.env.example — Template for API Key configuration
• references/auralwise_api.md — Full AuralWise API documentation
• references/rss_discovery.md — Methods for discovering podcast RSS feeds