LOKAL (transcriptions)

LOKAL is a user-friendly application to do AI transcriptions locally (i.e. on your own computer).

• Low cost. Payment is voluntary, and very small.
• High quality. Quality is on-par with on-cloud alternatives.
• Privacy-aware. LOKAL avoids the need to send audio data to third parties. It is also possible to run transcriptions entirely offline.
• Open-source. Commercial use is allowed under the license terms.

Why/when to use LOKAL?

Interviews, focus groups, statements, and other oral stories can hold biometric data, personal identifiable information (PII), and sensitive details. The risks of sharing such data with untrusted 3rd parties is significant, including a potential for audio "deepfakes".

LOKAL runs locally on your computer.¹ It can avoid you the need to send audios out.

Installation

Prerequisites

The following software needs to be installed in the device/computer where you plan to use LOKAL.

• FFmpeg.
  • Some features require FFmpeg to be installed on the computer you're using LOKAL.
  • Installation guidance for FFmpeg is available here.
  • If you absolutely cannot install FFmpeg, LOKAL might still work for you if you use audios in .wav format and Systran's 'Faster Whisper' models. Other file types and models require FFmpeg.

No-code installation

If you want to save time, use the standalone version of LOKAL.

1. Download the executable (.exe) installation file available here.
2. Double-click.
3. Install.
4. Use.

ALTERNATIVE Python installation

You can also use LOKAL as a pure Python app. Installation and usage are much harder, but you get more freedom and can tinker with the code. See instructions at the end of this README.

Instructions to use LOKAL

LOKAL offers users a number of choices. It's a long list, but once you open the app you'll see thing are fairly intuitive.

Required selections

Main selection

• Select audio. Press select audio to choose the audio to transcribe.
  • If you do NOt have FFmpeg installed on your computer, only .wav files are supported.
  • If you have FFmpeg, most formats are supported (click the "all files" dropdown on the browser window if you do not see your file).

GPU & Timestamps

• GPU Enable GPU mode.
  • Turn on to enable settings applicable to GPUs.
  • If you do not have a GPU, leave the button as it is. LOKAL defaults to settings applicable to regular CPUs.
• Timestamps. Enable timestamps
  • Turn on to enable timestamps on final transcript.
  • If you do not need timestamps, leave the button as it is. LOKAL defaults to a transcript without timestamps due to historical reasons (it started as a research tool and timestamps can be very annoying in this context).

T&Cs

• Terms and conditions. Use the checkbox to agree to the terms and conditions of usage.
  • Click on the link to read terms and conditions.
  • Agree to terms and conditions using the checkbox.

Model

• Model (family). Use the dropwodn to choose the family of models to use for transcription.
  • Currently, LOKAL offers two model families, OpenAI's Whisper and Systran's take on Whisper, called "faster-whisper".
  • OpenAI's Whisper requires FFmpeg. Systran's Faster Whisper does not.
  • The goal is to add more options.
• Model (size). Use the dropdown to specify the model size to use for transcription.
  • Models come in different sizes. The larger the model, the better the output. The larger the model, the longer the transcription takes.
  • Rule of thumb. If you can understand the conversation easily, one of the smaller models might be enough.

Approach

• Approach. Use the dropdown to choose between three different approaches to transcription, meant for different types of conversations.
  • Simple. A straightforward word-by-word transcription. Best for complex/dynamic conversations with many speakers and interruptions, where it is very difficult to track of all speaker changes.
  • Segmentation. A transcription split into paragraphs roughly following pauses and changes of speakers. Best for semi-structured conversations like interviews and focus groups, where tracking speaker changes is easier.
  • Diarisation. A transcription split into blocks roughly corresponding to segments by specific speakers (with speaker labels). Best for structured conversations where speakers have clear turns and speaker changes are clear, like panels or seminars.

Language:

• Language. Use the dropdown to select the language for transcription.
  • If left blank, the model tries to identify the language by itself.
  • Specifying the language might make it easier for the model and speed things a little.

Optional selections

Optional hyper-parameters appear and dissapear depending on user selections. All such fields have a preset value. There is no need to even touch them. That said, users can also adjust them at will.

• OPTIONAL PROMPT

  • This option is shown when the user selects the original 'OpenAI: Whisper' family of models.
  • It can be used to upload a text file containing terms that the model might struggle to identify, such as names, names of places, jargon, and acronyms.
  • Prompts are not infalible. They can help quite a bit, but they are not as effective as fine-tuning a model for a specific sector/industry.

• Ignore short segments.

  • Appears when users choose 'segmentation'.
  • Use to set a threshold to ignore short words/comments like fillers or interruptions.
  • If treshold is too high, it might ignore parts of sentences before/after pauses.

• Ignore short pauses.

  • Appears when users choose 'segmentation' or 'diarisation'.
  • Use to set a threshold to ignore short pauses that might cause excessive paragraph breaks in the final transcript.
  • If this hyper-parameter is set too high, the models might ignore legitimate changes between speakers.

• Number of speakers.

  • Appears when users choose 'diarisation'.
  • Enables users to enter the exact number of speakers.
  • There is no need to set a number. The diarisation model can try and guess the number of speakers. That said, results can be better if the model knows the number of speakers in advance.

Maximising privacy

LOKAL is not a guarantee of privacy. That said, LOKAL can help you manage privacy in the following ways:

• LOKAL avoids you the need to send audios to third-party service providers. This can help reduce risk exposure, especially if dealing with sensitive or easily identifiable audios.
• LOKAL allows you to run transcriptions offline. Running a few mock transcriptions while online is needed to trigger the download of models. After your chosen models have downloaded, however, you can disconnect from the Internet and everything should works just fine.
• If you need or want maximal privacy, you can also copy LOKAL's installation (LOKAL installs to '~/<-username->/AppData/Local/Programs/LOKAL') to an island computer after triggering the download of models.

Known limitations

LOKAL aims to assist humans with preparatory tasks rather than replace humans. Reducing the costs and time of initial speech-to-text conversion allows users to assign more resources on editing.

For the same reason, users remain in control and are responsible for the final quality of transcriptions. Users should therefore keep in mind all AI models and systems have limitations.

LOKAL's limitations include but are not limited to:

• All models may present potentially high error rates.
• The segmentation and diarisation approaches and models used in these approaches can struggle with overlapping speakers.
• The transcription models can struggle with names, places, acronyms, and potentially, accents. Users must not use outputs without editing them.
• The transcription models can struggle with conversations taking place in different languages – an idea is to do multiple runs with different language selections and then join the results.

Having troubles?

We made LOKAL as easy to install and use as possible. However do get in touch if you feel stuck: [email protected].

License

LOKAL (transcriptions) is a product by polyzentrik.com, released under an Apache 2.0 open source license. The code for LOKAL is available via GitHub.

LOKAL implements a voluntary activation approach with a soft free trial:

• Core functionality works regardless of whether LOKAL is activated
• Activation enables visual styles
• Activation is also needed to keep the user interface pretty after five free trial transcriptions

Activation is possible using the invoice number you get after making a very small voluntary payment.

.

.

.

OPTIONAL Python installation (advanced coding required)

Skip this section if not a coder. It is REALLY hard.

You can also use LOKAL as a pure Python app.

Assuming you have Python (3.11.0) installed on your computer, you need to:

1. Clone this repository.
   • The main branch has the latest released version.
   • The dev branch has latest commits, to be shipped on next release.
   • For guidance, see https://docs.github.com/en/repositories/creating-and-managing-repositories/cloning-a-repository and https://stackoverflow.com/questions/1778088/how-do-i-clone-a-single-branch-in-git.
2. Create an environment around it.
   • For guidance, see https://docs.python.org/3/library/venv.html.
3. Install required libraries using pip install -r requirements.txt.
   • For guidance, see https://pip.pypa.io/en/stable/user_guide/.
4. Check this file for some tiny but necessary library adjustments needed for progress bars to render.
5. Run LOKAL using python lokal.py or python -m lokal.
   • For guidance, see https://pythonbasics.org/execute-python-scripts/.

.

.

.

---

Footnotes

Footnotes

1. An Internet connection is needed for initial model download. The first time you choose a model, the model downloads. After, it is possible to perform transcriptions offline. Resetting models is possible from within LOKAL, which deletes all models in memory and forces a re-download next time a model is used. ↩