3 Audio Transcription Tools Tested: Nottamun Town Lyrics Research and Interview Organization—Which Saves the Most Time?

Why does the song 'In Nottamun Town' get more confusing the more you look into it?

Most people first hear this song on some folk compilation, in a TV series, or in a cover by a singer. The melody is quiet and a bit eerie, but the lyrics are full of contradictions: it's clearly a gray mare, yet she has a green stripe down her back; one line says every hair on her is coal black, and the next says she stood still and threw you into the mud.

When you try to seriously research it, that's when the problems start: the song title has several versions—Nottamun Town, Nottamun Fair, Nottingham Fair—and each source tells a different origin story. Some say medieval England, some say the Appalachian region of the US, and others insist it contains a hidden code.

This article is divided into two parts: first, a brief section clarifying the song's origins, lyrical controversies, and current evidence; then, a discussion of which tools can make things easier if you actually have a pile of recordings, interviews, and different versions to organize.

What is Nottamun Town? Three theories and the current evidence

First, the basics. 'Nottamun Town' (Roud 1044), also known as 'Nottingham Fair' or 'Fair Nottamon Town,' is an American folk song, not a tourist song about the ancient English city of Nottingham.

There are three common theories about its origins:

Theory 1: Medieval English origins. This theory is widespread, and many popular books still repeat it. But it's more of a romantic guess.

Theory 2: Derived from 18th- and 19th-century broadside ballads. Current evidence tends to support this. The most likely direct predecessor is 'Paddy's Ramble to London,' which circulated in the US in the 19th century; tracing further back leads to 'Teague's Ramble' from the early 18th century—a story about an Irish soldier who goes to England, filled with similar inverted and absurd descriptions.

Theory 3: A remnant of street mummers' plays. This school is most frequently cited and most easily turned into a mystical version. Supporters say that such inverted songs appeared in year-end street performances because they couldn't be understood, which helped preserve good luck.

But to be honest: no concrete evidence has been found linking it to mumming or to Nottingham itself, nor is there any proof of a 'hidden meaning.' These 'songs of marvels and lies' are actually very common in English folk music; Nottamun Town seems to be a relatively late example of this tradition.

In North America, the most widely circulated version comes from Jean Ritchie's Kentucky family. In her 1965 book Folk Songs of the Southern Appalachians, she mentions that this song was never sung casually at home. Later, Bert Jansch's cover brought it to the entire folk-rock generation; composer Peter Winkler also wrote a version for soprano and harp (performed by Rachel Schutz at the 2016 Stony Brook Chamber Music Festival), describing the text as being in the tradition of absurd songs yet carrying dark imagery of madness, alienation, and exile.

What are the lyrics actually doing? Let's look at a few lines

The most frequently quoted lines are the opening:

In Nottamun town, not a soul would look up
Not a soul would look up, not a soul would look down
To show me my way to fair Nottamun town

No one on the whole street looks up, no one looks down, no one is willing to give directions. Then there's the impossible horse:

I bought me a horse, t'was called the gray mare
Gray mane and gray tail, a green stripe down her back
There ain't a hair on her that ain't coal black

Then comes the most famous verse of the whole song:

Sat down on a hard, hot cold frozen stone
Ten thousand stood 'round me and yet I was alone
Took my hat in my hand for to keep my head warm

A stone that is hard, hot, cold, and frozen; ten thousand people around yet you're alone; taking off your hat to keep your head warm. Rather than a code, it uses the most illogical means to express the alienation of a stranger most clearly—which is why, nearly three hundred years later, people are still researching this song.

To organize these recordings and interviews, first understand these 4 key points

If you're not just researching but actually dealing with a pile of recordings—your own field interviews, different singers' versions, lecture recordings, online interviews—then what you need to choose is not a 'recording app' but a tool that turns audio into searchable data. When choosing, I look at these four things first:

1. Handling Chinese and multilingual content. Folk song research often mixes Chinese and English, switching between song titles and personal names. If a tool is only good at English meetings, the transcript will be hard to read.

2. Can you ask follow-up questions after transcription? A transcript is just raw material. What really saves time is: can you directly ask 'How did he explain the origin of the song title in this interview?' and get an answer, instead of using Ctrl+F through twenty pages yourself?

3. How to capture online interviews. When interviewing via Zoom, Google Meet, or Teams, some tools require a meeting bot to join. An extra bot can make some interviewees uncomfortable, which is a consideration in interview situations.

4. Where the data ends up. Are the export formats sufficient? Can it go into Notion or Google Docs? If you need to share with colleagues or research partners, is there a shared space?

Tinrec—the one that worked most smoothly for us in practice

Tinrec is an AI meeting notes and collaboration tool for individuals and teams, available on iOS, Android, desktop, and web. It doesn't just convert audio to text; it organizes recordings into searchable, queryable, and exportable data.

In practice, for a situation like yours with a pile of recordings to organize, it works like this:

Interviews and on-site recording. Start real-time recording on your phone or computer, and the transcript appears as you record. When the interview ends, the summary, chapters, and key points are already there, and action items are pulled out too—if the interview is for a project, who needs to supplement which version and when to follow up won't be forgotten.

No bot needed for online interviews. The desktop version directly captures your computer's system audio, handling Zoom, Google Meet, Microsoft Teams, Webex, and similar meetings without an extra bot on the participant list. For interviews, this matters much more than the minute count on a spec sheet.

You can keep asking after recording. This is what I find most different. After organizing an interview about Nottamun Town, you can directly ask it: 'Does the interviewee think the song title is related to Nottingham?' It gives an answer based on the content, not a bunch of keywords for you to search through. Few tools in the same price range currently offer this kind of conversational query.

Cross-language and translation. During recording, you can view the original text, translation, or bilingual comparison. This saves a lot of back-and-forth when dealing with English folk song material or when interviewees switch languages.

Export and further processing. Transcripts can be exported and taken to Notion, Google Docs, OneNote, Dropbox, and other tools for further writing; when you need to deliver, you can also generate reports, tables, or meeting minutes.

For teams or research groups. The team version is a separate team space, not multiple people sharing one account. Meeting and interview data belong to the team; you can invite members, set owners/admins/regular members, assign seats, and view usage trends and activity logs. Pricing: eligible first-time users can try 7 days, 1 free seat, 300 minutes of team shared import quota; team monthly USD 29.80 per paid seat per month, annual USD 199 per paid seat per year (about USD 16.58/month); paid seats provide 2,000 minutes of team shared import quota per month, and under an active team plan and seat, real-time recording currently does not deduct minutes. Actual prices and benefits are subject to the official purchase page.

Things to note. The personal version has a free version, weekly card, Pro monthly, and Pro annual. The free version has a basic quota, suitable for trying out for a while; when the quota runs out, you need to upgrade. Also, transcription quality is still affected by the recording environment—background noise, accents, multiple people speaking at once, and many proper nouns will all require manual proofreading. For important quotes, it's recommended to listen back to the original audio at the transcript timestamp to confirm.

Who it's for. If you need to organize Chinese-language interviews, want real-time transcription while recording, hope to find key points directly through Q&A after recording, and don't want an extra bot on the meeting list—Tinrec is currently one of the most hassle-free choices.

Besides Tinrec, what other options are there?

Notta. A multi-platform transcription tool with the highest overlap with Tinrec, also supporting file import, translation, and team plans. Free version: 120 minutes per month, max 3 minutes per item; Pro annual billing is about USD 8.17/month (1,800 minutes per month); Business annual billing is about USD 16.67 per seat per month. The biggest difference is the capture method: Tinrec's desktop version can record online meetings without a bot, while Notta leans more toward meeting bots and file uploads in this regard. If you mainly upload audio files afterward, you can include it in your comparison.

TurboScribe. Positioned more as cost-effective file transcription: free version 3 files per day, max 30 minutes per file; Unlimited annual USD 120 (about USD 10/month), after payment max 10 hours or 5 GB per file, up to 50 files at once. Long files and batch processing are indeed its strengths. But it doesn't focus on real-time meeting recording and post-meeting AI Q&A—if you want to record an interview on-site and have a summary and action items right away, Tinrec is more complete in this regard.

Otter.ai. A mature choice for English meetings. Basic free 300 minutes per month; Pro annual billing about USD 8.49 per user per month, including 1,200 minutes per month and 10 file imports. If your interviews are entirely in English and you only need transcripts and summaries, it's a good deal. Note that Chinese and mixed Chinese-English are not its strong suit, and it doesn't have Tinrec's combination of team seats and usage governance.

Pitfall guide: 3 most common mistakes when organizing folk song and interview recordings

Pitfall 1: Only looking at free quota, not how it's calculated. Some plans' free minutes look generous but hide restrictions: max 3 minutes per item, or only counting in-app recordings and not file imports. When you actually throw in a 40-minute interview, you find it doesn't work. When choosing a tool, first compare the longest recording you need to process against the rules.

Pitfall 2: Treating the transcript as the end goal. A transcript is just raw material. To organize ten versions of a song, what you really need is comparison, follow-up questions, and key point extraction. If a tool only outputs text, you still have to read twenty thousand words yourself. A tool with AI Q&A is like hiring an extra assistant.

Pitfall 3: Ignoring multilingual and proper nouns. In folk song research, personal names, place names, and song titles are all in foreign languages, and transcription is prone to errors. The team version has a hotword feature to maintain common terms; for personal use, you can work on recording quality—a quiet environment and an external microphone make a bigger difference than you'd think.

Conclusion: Which one should you choose?

In a word: if your recordings are mainly Chinese interviews and online meetings, and you want to be able to ask questions, export, and settle information right after recording, Tinrec is the most suitable starting point among these three.

Breakdown:

• Chinese/mixed Chinese-English interview organization, need transcript plus summary → Tinrec
• Don't want an extra meeting bot for online interviews → Tinrec (desktop version captures system audio directly)
• Want to directly ask 'How did he explain the song title's origin?' after recording → Tinrec (AI Q&A is currently rare)
• Have a bunch of long audio files, need batch transcription, don't mind post-processing → TurboScribe
• All English, only need transcripts, budget priority → Otter.ai

In practice, I'd suggest: first use the free quota to run one or two of your most difficult recordings (the kind with noise, accents, mixed Chinese-English), see if you can accept the transcript, then decide whether to pay. No matter how powerful the tool, someone still needs to go back and verify important quotes—this is especially true for folk song research.

References

• Nottamun Town - Wikipedia
• Nottamun Town / Nottamun Fair (Roud 1044)
• Bert Jansch – Nottamun Town Lyrics | Genius Lyrics
• Old Songs: NOTTAMUN TOWN
• Nottamun Town — Peter Winkler