Build a Transistor.fm Transcript Generator

A podcast episode can be published in minutes and still take hours to turn into usable video captions. Manual transcription creates delays, inconsistent timestamps, and extra editing work.

A better system pulls published episode media from Transistor.fm, sends it to a speech-to-text engine, then returns a transcript, WebVTT file, and SRT file. The key is separating what Transistor provides from what your application must build.

Key Takeaways

• Transistor provides podcast data, episode records, RSS feeds, and media URLs through its platform.
• Transcription, word-level timestamps, caption formatting, and storage belong in your application.
• Use an idempotent job pipeline so the same episode isn’t processed twice.
• Store both the raw transcript and formatted caption files.
• Treat audio downloads, API keys, and generated media as protected production data.

Separate Transistor Features From Your Generator

Transistor.fm is the content source in this workflow. It isn’t automatically a speech-to-text service.

The current Transistor API documentation covers API access to podcast data and episode records. Transistor also publishes RSS feeds that contain episode metadata and an enclosure URL for the media file. Those are the confirmed inputs you can use.

Your application adds the rest of the process:

Don’t assume a Transistor episode is a video file. Many podcast feeds contain audio enclosures. Your generator can accept audio because speech-to-text services process audio tracks, but a video editor needs a separate video asset with captions attached.

If the source is an MP4 file, extract or process its audio track. If the source is an MP3 or M4A file, generate the transcript and apply the resulting SRT or VTT file to the video stored elsewhere.

The Transistor support documentation is useful for checking feed behavior, publishing settings, and account-specific limits. Confirm the current API fields before you lock your integration to a response property such as media_url.

Use a Job Pipeline Instead of One Large Request

A production-ready Transistor transcript generator should run as an asynchronous job. Audio files can be large, and transcription may take longer than a normal HTTP request.

Use this sequence:

1. Discover the episode. Query the Transistor API or parse the show’s RSS feed.
2. Create a job. Store the show ID, episode ID, source URL, and current status.
3. Download the media. Stream the file into temporary storage.
4. Transcribe the audio. Request segment and word timestamps.
5. Create caption files. Convert timestamps into SRT and WebVTT.
6. Store the outputs. Save the transcript JSON and caption files.
7. Publish the result. Return a status endpoint or notify the user.

The API request can look like this in a Node.js service:

const response = await fetch(
  "https://api.transistor.fm/v1/episodes?show_id=" + showId,
  {
    headers: {
      "x-api-key": process.env.TRANSISTOR_API_KEY,
      "Accept": "application/json"
    }
  }
);


if (!response.ok) {
  throw new Error(`Transistor request failed: ${response.status}`);
}


const payload = await response.json();
const episodes = payload.data || [];

Treat this as an integration pattern, not a permanent schema guarantee. Read the current Transistor documentation for authentication, pagination, endpoint paths, and field names before deployment.

For a feed-based integration, parse each <item> and locate its <enclosure> element. Use the episode GUID or a stable Transistor episode ID as the job key. Don’t use the episode title because titles can change.

Your database needs enough information to restart a failed job:

episode_id
show_id
source_url
source_hash
status
transcription_provider
transcript_path
srt_path
vtt_path
error_message
created_at
updated_at

Set a unique constraint on episode_id and source_hash. If the publisher edits or replaces the media file, the hash lets you create a new processing version without destroying the older output.

The safest workflow is repeatable. A retry should continue the same job, not create a second copy of every transcript.

Download and Validate Episode Media

Media URLs come from an external system. Don’t pass them directly into a downloader without validation.

Your worker should check the URL scheme, redirect behavior, content type, content length, and maximum allowed file size. Add request timeouts and stop reading when the configured byte limit is reached.

Server-side request forgery is a real concern. A user-controlled URL can point to internal cloud services or private network addresses. Restrict outbound requests through an allowlist, a proxy, or network egress rules. If your application accepts RSS feeds from users, validate both the feed URL and every enclosure URL.

Stream the file instead of loading it into memory:

const media = await fetch(sourceUrl, {
  redirect: "follow",
  signal: AbortSignal.timeout(120000)
});


if (!media.ok) {
  throw new Error(`Media download failed: ${media.status}`);
}


const contentType = media.headers.get("content-type") || "";
if (!contentType.startsWith("audio/") &&
    !contentType.startsWith("video/")) {
  throw new Error("Unsupported media type");
}

Store the original file in private object storage with an expiration policy. You usually don’t need to keep every raw episode forever. Keep the transcript and captions longer than the source media when storage costs matter.

If the provider returns a video file, you can extract a normalized audio track with FFmpeg. The FFmpeg format documentation covers supported inputs and output containers.

ffmpeg -i episode.mp4 -vn -ac 1 -ar 16000 -c:a pcm_s16le episode.wav

A normalized mono WAV file reduces format problems. It also gives your transcription worker a predictable input.

Generate Word Timestamps and Segments

A plain transcript is not enough for video. You need timestamps that connect words or sentence segments to the audio timeline.

Speech-to-text providers differ in their response formats. OpenAI’s speech-to-text documentation covers supported models, file handling, and timestamp options. The open-source Whisper repository is another option when you want to run transcription on your own infrastructure.

Build your application around a provider-neutral response:

{
  "language": "en",
  "duration": 1842.6,
  "segments": [
    {
      "start": 12.4,
      "end": 18.9,
      "text": "Welcome to the show.",
      "words": [
        { "start": 12.4, "end": 13.1, "word": "Welcome" },
        { "start": 13.2, "end": 13.5, "word": "to" }
      ]
    }
  ]
}

Segments are enough for a readable transcript. Word timestamps give you better control for animated captions, search highlighting, and editing tools.

Clean the result before generating files. Remove empty segments, clamp negative timestamps to zero, and make sure each end time is greater than its start time. Preserve the raw provider response separately so you can reformat captions without paying for transcription again.

Caption timing needs practical rules. Keep each caption short enough to read. Split long segments at punctuation or a natural pause. Avoid displaying a new caption for a fraction of a second unless the speaker is moving quickly.

The WebVTT API reference documents the browser caption format and its cue structure.

Create SRT and WebVTT Files

SRT works well with video editors and many publishing platforms. WebVTT works well in browsers and HTML5 video players.

Use one internal time format, such as milliseconds, then convert it during export:

function timestamp(seconds, separator) {
  const totalMs = Math.max(0, Math.round(seconds * 1000));
  const hours = Math.floor(totalMs / 3600000);
  const minutes = Math.floor((totalMs % 3600000) / 60000);
  const secs = Math.floor((totalMs % 60000) / 1000);
  const ms = totalMs % 1000;


  const base = [hours, minutes, secs]
    .map(value => String(value).padStart(2, "0"))
    .join(":");


  return `${base}${separator}${String(ms).padStart(3, "0")}`;
}


function toSrt(segments) {
  return segments.map((segment, index) => {
    const start = timestamp(segment.start, ",");
    const end = timestamp(segment.end, ",");
    const text = segment.text.trim();


    return `${index + 1}n${start} --> ${end}n${text}n`;
  }).join("n");
}


function toVtt(segments) {
  const cues = segments.map(segment => {
    const start = timestamp(segment.start, ".");
    const end = timestamp(segment.end, ".");
    return `${start} --> ${end}n${segment.text.trim()}`;
  });


  return `WEBVTTnn${cues.join("nn")}n`;
}

This basic converter assumes the transcription provider already returned usable segments. A stronger caption layer adds line wrapping, minimum cue duration, maximum characters per line, and overlap correction.

Keep the transcript JSON as the source record. Store SRT and VTT as derived files. That design lets you change caption rules later without rerunning the speech model.

Include language metadata and a version number in each output. A caption file created with English punctuation rules should not overwrite a later Spanish transcription.

Add Status Tracking and Failure Recovery

Your API should return job status instead of waiting for transcription to finish.

Useful statuses include queued, downloading, transcribing, formatting, completed, and failed. Add attempts, last_error, and next_retry_at fields for worker control.

Retry temporary failures such as timeouts, rate limits, and provider outages. Don’t retry invalid media, rejected authentication, or unsupported file types without changing the input.

Use a queue such as BullMQ, SQS, Cloud Tasks, or a similar worker system. Keep the web request responsible for creating the job. Let the worker handle downloads and transcription.

Add structured logs for the episode ID, job ID, provider request ID, duration, file size, and processing time. Don’t log API keys, signed media URLs, or transcript text unless your data policy permits it.

A simple result endpoint can return:

{
  "episode_id": "12345",
  "status": "completed",
  "transcript": "https://storage.example/transcript.json",
  "srt": "https://storage.example/episode.srt",
  "vtt": "https://storage.example/episode.vtt"
}

Use short-lived signed URLs when transcripts contain private conversations. Apply access controls before exposing episode metadata to users outside the podcast team.

Connect Captions to the Video Workflow

The generator ends when it produces reliable files. The video workflow begins when an editor or player consumes them.

For a browser player, load the VTT file with a text track. For editing software, provide the SRT download. For social video production, keep the word-level JSON available for burned-in captions and animated text.

Test captions against real episodes. Check speaker changes, music sections, long pauses, names, acronyms, and overlapping dialogue. Podcast audio often includes intros and advertisements, so decide whether those sections should remain in the public transcript.

Add a manual correction screen if accuracy matters. Let an editor change text and timestamps without reprocessing the entire episode. Save revisions separately from the machine transcript so you retain an audit trail.

The output should be useful even when transcription isn’t perfect. Searchable text, stable timestamps, downloadable captions, and a clear correction path are more valuable than an opaque one-click result.

Conclusion

A reliable Transistor.fm transcript generator has a narrow job: discover the episode, obtain its media, transcribe the audio, and produce timestamped output. Transistor supplies the podcast data and published media. Your application supplies speech recognition, caption formatting, storage, and job control.

Build the pipeline around stable episode IDs, private files, retries, and provider-neutral transcript data. Once those pieces are in place, the same system can support podcast archives, video editing, searchable episode pages, and accessible web playback without processing the same audio twice.