Neural Networks for Audio-to-Text Transcription: A Guide to the Best Services and Technologies

Transcribing an hour-long interview once took days and was hell on earth. But now? You don't have to relive that dream ever again. Audio transcription AI can finish recordings in just minutes. And the quality? You would not have ever guessed two years ago that the quality would be equal to something you would find in a Sci-Fi film. We have been on the hunt for the perfect audio transcription solution for the last 18-months and tried 23 different services. The content has ranged from studio quality podcasts to a recording of two people talking over the sound of a coffee maker.

Overall, there isn't a definite answer to the "best" transcription service — not because they are all created equal, but because it depends on what you want to solve for.

Top 6 AI Tools for Speech-to-Text Conversion

Transcription AI has divided the space into "AI tools" for coders, "AI tools" for business, and "AI tools" for everyday home and personal use. In total we were able to find six transcription services that will help meet 90% of the scenarios that you will probably encounter. To test these services, we used one piece of content that consisted of a 40 minute live webinar featuring two presenters, including background music in between speakers and an abundance of professional jargon used by both presenters, and frankly we were surprised by the outcomes we found for many of the services we tested.

Here is a sampling of six AI transcription services that span the globe:

1. Whisper (OpenAI) — The Gold Standard for Open Source Accuracy

Best choice for: Whisper (OpenAI) is the "Gold Standard" for open source AI transcription services and will provide you with the highest level of accuracy in every language for recordings that contain noise; with the capability of operating on your own dedicated server where your data will never be released externally.

Whisper is most likely the "Gold Standard" for transcribing audio and video because they have developed a solution that can recognize and transcribe 99 different languages and because it can deliver accurate text from audio or video where a considerable amount of background "noise" exists, (both of which are factors confirmed by multiple community tests conducted by Hugging Face). The Whisper transcription model produces outputs in accordance with the model's training using over 680,000 hours of speech (i.e. v1/v2). Speech models can recognize and identify a wide range of things, including dialects, accents and medical language, etc. When it’s run on your local machine, you’ll keep your data.

Price: This is one of the cheapest APIs out there. The cost is $0.006/minute to use the API, and the local version has no charge — but you need a good graphics processing unit (GPU; i.e. an NVIDIA RTX 3060 or better) or else you are going to have to wait forever to get any results. If you are going to use the model for 500 hours/month, it is less expensive to rent a server with Whisper installed, which will cost you $120 - $150/month rather than $3,000 for API calls.

Our verdict: If you are using content in different languages or need the highest level of accuracy possible without sacrificing quality, then there are no other options available. The only downside is that there is no delivery with built-in speaker separation. This can be done using pyannote.audio along with 5 lines of code. No big deal.

2. Yandex SpeechKit — The Leader for the Russian Language

Best choice for: All Russian language projects where accurate and reliable text-to-timer/recorded time stamps are required and interviews with several individuals need to be transcribed.

Yandex SpeechKit analyzes and interprets audio data beyond words. This service will also identify pauses, intonation, laughter and background noise. It provides timestamped results for all segments. Yandex SpeechKit is one of only 2 organizations in Russia to provide high-quality diarization (the other being Charla). Yandex SpeechKit can distinguish between as many as 8 different voices in a single audio file and accurately identify them by speaker. During our roundtable evaluation using 4 participants, overall accuracy for identifying speakers was 91%. Speaker identification was not accurate when two individuals were speaking simultaneously.

Price: The first 90 minutes of use each month are free, then the cost is ₽1.20/minute, up to 1000 hours of use. If you obtain a contract for more than 5,000 hours on a corporate level, then your price per hour will be seventy rubles. Additionally, during the trial period, you will have access to all the services offered by the company, including using the company’s own customer relationship management application programming interface (CRM API).

Our verdict: In our opinion, if your work includes any type of journalistic work or creating subtitles for video content created in Russian or transcribing your client phone calls, then this is an excellent option. If at least 80% of the content you are working with is the Russian language, then this is your starting point. You should not try to make this more complicated than it really is.

3. Charla — A Russian Service with a Focus on Security

Best choice for: Charla is the best resource for processing personal data under this law (152-FZ), creating many hours of video content such as long-form lectures and webinars and conducting collaborative work with your team.

Charla is a safe harbor for its customers. The servers that store your data will reside in the Russian Federation. This is critical for compliance with the 152-FZ law. The accuracy claim for both Russian and English audio transcriptions is approximately 93%. One of the key features of Charla is that you may upload a file of up to 5 Gigabytes in size, and there are no limits on the amount of time the audio will be processed. With many of Charla's competitors, you will be required to split up long-form audio recordings before you can upload them to the system.

In addition to everything that Charla has to offer with its "dry" application program interface (API), it also provides you with a built-in editing tool where you can play the original audio while reading along with the corresponding text. In addition, you can create a summary of your meeting by simply populating a box on the screen. You may upload both files and links to audio and video stored in various internet cloud storage locations, such as Youtube, Google Drive, Yandex.Disk and Rutube.

Price: Charla provides three different pricing tiers: free (limited), paid, starting at 792 roubles per month for an associated package of hours. The number of audio minutes will not expire, which provides another excellent incentive to retain Charla as your sole service provider.

Our verdict: In conclusion, this is the ideal transcription service for Russian companies, educational institutions and researchers. Charla provides you with a safe way to process all your documents and allow you to work with others in an efficient way that requires no special software to use their API.

4. Google Pinpoint — The Choice for Professionals and Journalists

Best choice for: Google Pinpoint was built specifically for journalists working with leaks and archives; it not only transcribes audio/video recordings, but also indexes each audio or video recording.

You can search by keywords, filter by date(s), filter by speaker(s), and create a collection of excerpts from the audio/video recordings. The user interface of Google Pinpoint allows for audio/video recordings (that were once a chaotic array of data) to become a well-structured database of knowledge.

Google Pinpoint's primary differentiator from other software programs in the market is its ability to process terabytes of data. For example, in one project run by the OCCRP (which used 470 hours of recorded conversations as evidence), Google Pinpoint was able to produce maps of the connections of 89 different people to each other based on the audio/video recordings they had entered into the system. A team of 5 journalists would have spent at least 3 months creating these maps without Google Pinpoint; however, with using only a few clicks in Google Pinpoint, that same work would take 10 hours at the most.

Price: Free for non-profit work or media companies. Commercial companies use Google Pinpoint with prior approval; however, many independent editors and media companies have been able to use Google Pinpoint.

Our verdict: Overall Assessment: Google Pinpoint is a very effective tool for journalists or professionals who are working in an archive where you need to look through hundreds of hours worth of information to find a "needle" in the "haystack" of information. For typical day-to-day tasks, Google Pinpoint is overkill; however, if you are doing investigations, it is an essential tool.

5. Riverside.fm — Studio Quality for Podcasters

Best choice for: Riverside.fm is one of the best podcast recording platforms in the industry. It also has an outstanding transcription module.

Riverside.fm is unique in that it records each speaker separately during an audio/video recording and is seamless when exporting because it creates separate text files for each audio/video recording location. In fact, in our test of recording an audio/video recording (3 hour podcast with 2 hosts and 1 guest), the accuracy of the transcription was approximately 97%. Transcription errors only occur when multiple speakers on a split-second basis finish their sentences at the same time.

The transcriber supports over 100 languages but enhances transcription for conversationally styled conversations. Scientific reports could drop to 85-88% accuracy, however this still rates as above average. Bonus features include auto-punctuation, removal of filler words (um, uh, like), and a 30-40% savings when editing.

Price: Pricing is $19/month for 5 hours of transcribing under the Creator plan; $29/month for 20 hours under the Pro plan. Annual subscription provides 20% discount. Teams of 3+ should subscribe to the Business Plan ($39/month/person) for unlimited transcription as a more cost-efficient way to organize team projects with unlimited access to transcription.

Our verdict: Final decision: podcast hosts are able to include transcription as a part of the recording process, eliminating the need to create and transfer files back and forth. Exporting the separate text tracks with each speaker separately is an additional feature for which many companies charge, but not this company.

6. Telegram Bots and Quick Converters (SpeechNotes, BotHub)

Best choice for: Best option for instant transcription of voicemail (audio) messages or recordings less than 5 minutes long; no time available to log into service.

The "quickest solution" category resides solely within the messenger app. Bots like @transcriber_bot or @voicy take audio, and send back converted text between 10-30 seconds without requiring you to sign-up. Average transcription rate is 80-85% accuracy on voice calls made with clean audio quality. Transcriptions that are recorded with noise or an accent fall to the 70% range, which is a great option for someone who receives voice notes from co-workers, but not acceptable for anyone using them professionally.

Some browser tools like SpeechNotes operate using Web Speech API (the same technology in Google Chrome) to dictate transcription directly through your microphone or speaker. The accuracy of these services are equal to Google Speech-to-Text; however, they do have an added limitation: you must be physically playing/recording audio while dictating; therefore, uploading already saved recorded audio files is not allowed. Nevertheless, everything is local, there is no server-side component involved, and all versions of this product cost you nothing.

Price: Bots are often available for free but will limit you to 3 – 5 files daily. You can temporarily remove limitations by paying $3 – $5 a month for premium versions. Browser-based tools are available for free but contain advertisements and other ad-supporting technology.

Our verdict: Overall, I'd rank them "B" for their use with small audio files as long as they get the job done — usually the answer is going to be better than listening to a three-minute voice file for the third time, which is the main benefit of having access to chatbots.

Comparison of Popular AI Transcription Services

Best Solutions for Transcribing Voice Messages

Voice messages have changed from a simple addition to being a major headache. When you receive 15 voice messages per day that are approximately one minute long each, it’s becoming less about actually listening and making that final decision on whether to listen at all. This phenomenon is referred to as "Voice Message Fatigue" when 64% of survey respondents said they would wait hours before hearing a voice note because they “don’t have time right now” (do you relate?).

Built-in iOS and Android functions. With iOS 17, Apple added transcription capabilities to iMessage messages. As long as the sender is also an iOS user, a transcription is automatically generated and displayed underneath the audio file (this accuracy average is 85–90% for English Language, lower for Russian due to model/Machine Learning limits on Russian). Android 14 added similar functionality to text messages through RCS (Rich Communication Services) only, and does not work with Telegram or WhatsApp.

Telegram bots for forwarding. Bots such as @voicy or @transcriber_bot act as intermediaries by accepting forwarded voice notes and producing text versions. Using a simple step — forward your voice note to either of these bots to receive your text back; you can complete this process in approximately 15 seconds for any voice note. However, for multiple voice notes (e.g., 10), this creates an inconvenience since each will require a separate request. The accuracy of the returned text will depend on which engine the bot uses, with @voicy using Google’s Speech-to-Text engine and @transcriber_bot using its own.

WhatsApp and browser extensions. Transcription extensions for WhatsApp can be found, such as the Transcriber For WhatsApp extension available on the Chrome Web Store. They can intercept the audio files in the web version of WhatsApp and append a “Transcribe” button. With this method, audio data never leaves the web browser, since they function via Web Speech API. However, transcription extensions only work with the desktop version; they do not work with mobile devices.

How to Transcribe Audio to Text Using AI: 4 Simple Steps

The transcription process appears linear, in that you simply upload a file and receive a return of the transcribed text. However, the overall quality of transcription will depend approximately 40% on user actions performed before and after the process of transcription. If you did not prepare your recorded files correctly, you might as well just be getting a whole bunch of gibberish. If you skip post-processing, every single sentence you produce will require re-proofreading yourself.

Step 1: File Preparation and Audio Cleaning.

AI loves clean sound. In comparison with mp3s that compress sound file, WAV or FLAC compressed files provide a clean full audio file. If your original source has a file of type mp3 and the bitrate is less than 128kbps, convert this mp3 file to a WAV file using either Audacity or CloudConvert in about 30sec; this will build accuracy to be 5%-8% better.

In terms of % accuracy, background noise has a more significant negative effect on errors than accents do. If you are capturing audio and the place of origin is either a café or a vehicle, run your audio file with a noise reduction filter; if using Audacity, the "noise reduction" filter would require you to do the following: 1) select a fragment that has pure noise; 2) set the noise profile; and 3) apply the previously created noise profile to the entire audio file. This process will take a couple of minutes and provides WER (words error rate, how many errors were made) from about 15% down to about 6%, this was based on interviews conducted from the streets.

The length of a file also matters. When transcribing audio for your service, service providers break the audio down into 10-30 minutes chunks; errors occur frequently when the audio files have been taped back together due to the join (or seam). One way you can avoid losing the continuity of your conversations during recording of an interview in advance is by breaking your files at logical boundaries: the end of one topic and the beginning of another. This should help keep the flow of the conversation intact.

Step 2: Choosing the Right Tool for the Task.

There is no one "best" tool for all uses. There are tools that will be optimal for your specific usage. For example, if you had a one hr Russian 2-speaker interview, Yandex SpeechKit or Charla would be appropriate solutions to use since the tool would be trained on the local phonetics, can separate the speakers, and provide the time stamps on timeline. For producing items that involve multiple languages, or you are using technical terms, Whisper would be the best tool to use since Whisper will provide better context for your transcript. If transcription is a part of a workflow that includes the production of a podcast — recording, transcribing, cutting out clips and creating additional material — Riverside.fm is the best option because they provide an integrated solution for each step of the process. Google Pinpoint is the only product that can help with archiving hundreds of hours of content since their indexing and search capabilities are more valuable than just doing transcription on its own if you were to build out the transcription process on a piece-meal basis.

Step 3: Uploading and Setting Parameters.

Before starting the upload process, select the language manually even if the service offers automatic detection. Automated detection fails in approximately 12–15% of the time due to failures in identifying foreign words when working with Russian speech. Yandex SpeechKit and Whisper both allow for the selection of a specific dialect and therefore Russian (Russia) is more likely to provide you with accurate recognition of words produced using the general Russian model.

If a recording has more than one voice, enable the speaker diarization function. Yandex, Riverside, Charla and Whisper (using the pyannote.audio module) all provide this feature, and the absence of it from a transcription would result in wall-to-wall text with no indication of who spoke what, making manual correction tedious.

How the transcription will be output also affects how easy the transcription will be to work with, SRT or VTT files with time codes are needed for producing subtitles; TXT or DOCX files are needed for creating an article; and JSON files with speaker markup are needed for CRM integrations where recordings can be associated with a client record.

Step 4: Post-processing and Text Verification.

Even with a transcription model that has a 95% accuracy rate, an output product will contain errors related to terminology, names and numbers. The first review of the transcription should focus on ensuring the accuracy of important information such as amounts, dates and names. The second review should focus on cleaning up the text of any extraneous information, such as repeated words, extra spaces or improper capitalization. You can automate this phase by combining it with a Large Language Model. Simply upload the transcript from Whisper or Yandex to ChatGPT/Claude along with the instruction "Correct spelling, punctuation, remove unnecessary words, keep the original meaning," and the model will produce a clean version of the transcript for you in about 30 seconds. You'll need to perform a quick final review.

When you are transcribing a longer recorded interview, it’s helpful to produce a condensed version right after the transcription has been produced. ChatGPT can reduce your 40-minute interview into a one-page bullet list. This is advantageous if you need the transcription for fact-checking rather than for publication.

Transcription Accuracy: Metrics and Factors

When a company claims to have “95% accuracy” for their service, this does not mean that 95 words out of 100 were recognized correctly. The metric for transcription accuracy for speech-to-text is WER (Word Error Rate); that is, the percentage of errors found in the transcription includes three types of error: substitution, deletions, and insertions.

The WER of 5% means there are five errors in 100 total recognized words.

What is WER?

Word Error Rate = (S+D+I) / N

Where:
S = Number of substitutions,
D = Number of deletions,
I = Number of insertions,
N = Total number of words.

Example: Original: "We launched a new product"; Recognized: "We launched new product"; Error: (deletion of the indefinite article “a”) D = 1. WER = (0+1+0) / 5 = 20% — A single error, but significant in terms of accuracy of the overall transcription due to the word in question.

Another indicator of transcription accuracy is the CER (Character Error Rate), which looks at the percentage of incorrect characters; however, due to the nature of the logographic language (language that uses visual symbols like characters) the CER is not typically used for Russian.

Factors That Kill Accuracy

One of the biggest offenders is background noise. If there are multiple people talking or music playing in the background, it is very likely that Whisper will mishear these sounds as speech sounds. In one experiment with a recording made in a cafe with a noise/signal ratio of 12dB, the number of transcribed words decreased from 96% to 84%; therefore, approximately 1 out of 6 transcribed words will have been transcribed incorrectly.

Audio compressing at low bit rates also has a negative impact on accuracy. An MP3 file at 64kbps sounds fine to the listener but causes a significant amount of detail to be lost for Whisper's model. For example, in the tests conducted on these types of recordings, the word error rate was 3-5% higher than for a WAV file. If you have control over how the transcription is made, you should record at least 128kbps or use a lossless (WAV or FLAC) encoding format.

Another reason that causes problems for Whisper's model is rapid speech; generally speaking, a speaker would need to exceed 200 words/minute in order to be considered a rapid speaker. An average rate of speech is 140-160 words/minute and a news anchor will approximately speak at 180-200 words/minute. If a speaker is speaking quickly (220-240 words/minute) like they would in a pitch or startup presentation (example 230 words/minute) Whisper would miss approximately 12% of mono-syllabic transcribed words as they become combined as part of the ongoing stream.

Use of very specialized slang also has the potential to confuse Whisper's model by forcing the model to create a word from a similar-sounding word that is not in their dictionary. For example, the professional term "refactoring" may be translated to "refactor ring" while "deployment" may be translated to "deploy meant."

Another issue related to accuracy caused by very poor acoustics is reverberation, which occurs when sounds echo and obscure the boundaries of words. For example, if you record in an empty room with tiles, the resulting sound will have a tail, which indicates the amount of time between reflections.

Frequently Asked Questions About Speech-to-Text Technologies

Is it safe to upload confidential recordings to the AI cloud?

This depends on the threat model you have in place and the service being used. When cloud platforms such as Yandex, Google, and the Whisper API process audio files using application, they do so by processing records in their servers. All providers generally state that they do not store these files, and log data as well as government requests demonstrate that this is not the case, and data can sit dormant within these logs, or can be subject to government requests for data.

Where it comes to working with Confidential Information, such as Trade Secrets, Medical Data, and/or Legal Data (GDPR/152-FZ), the Cloud presents a risk. The alternative is a local deployment of the models used for transcription.

Practical Approach: The Cloud is generally safe for everyday use of transcribing audio files; however, for the purpose of Transcribing Confidential Information, you should use only Local Models or on-Premise Solutions.

Can transcription be used for processing personal data under 152-FZ?

Yes, with restrictions. Services that have data processing servers located in the Russian Federation (Yandex, Charla, etc.) are obliged to comply with 152-FZ; however, with relation to Sensitive Data (e.g. Biometrics and Health Data) a separate Agreement is required and often these services are used to process Sensitive Data in a protected (i.e. on-Premise) environment. Services that are foreign (Whisper API, Google, etc.) may not guarantee compliance with 152-FZ in that the data may be subject to border crossing when processed.

Can AI understand dialects and accents?

Yes, in the sense that AI would “Recognize” rather than “Understand.” Models learn to process data based upon a predetermined standard; any deviation from the standard degrades the overall accuracy of the model. For example, if a Southern Russian uses a slightly different accent than a Moscow representative, the WER (Word Error Rate) could be as high as 12-15% for recording with the Southern Russian dialect and as low as 5% for the Moscow dialect; this holds true if using a Caucasian accent since the usage of Caucasian-based speaker’s WER could be a higher range from 18-22%.

In relation to Dialect usage, for example, the “Tsokanye” mode of speech utilized in the North of Russia is a deviation from the standard model, and as such, if this becomes your predominant audience when using a speech recognition/transcription model to process recordings, it would greatly benefit from a Fine-Tune of the model for use with this audience.

One alternative to ensuring accuracy of models utilizing various dialects is to use “multi-pass” Transcription — this would involve running the initial transcript through the main model then running it through a dialect-accent-specific model followed by comparison of the overall results (by confidence score). This procedure does slow processing time by approximately 2x (i.e. 1 hr to process requires 2 hr for processing) but the end result produces an 8-12% increase in accuracy.

What is the difference between automatic and manual transcription?

Automatic Transcription is defined as the algorithm-based processing of transcription — Speed of processing can range from Real-time to 10x the speed of Real-time (i.e. processing an Hour of audio can be done on a GPU in approximately 6 minutes). Accuracy for Automatic Transcription typically ranges from 85% to 98%. Costs for Automatic Transcription typically range from ₽0.70 to ₽5.00 per minute of processed transcription.

Manual Transcription is defined as Human-based Alternative Transcription — Manual Transcription requires approximately 4-6 hours to process an Hour of audio; Accuracy for Manual Transcription is typically 99% to 100%. The costs for Manual Transcription typically range from ₽150.00 to ₽600.00 per minute of processed transcription.

The Hybrid approach utilizes Automation + Manual Proofreading — This method would produce transcription within 10 minutes by the model followed by editing within 30-40 minutes. Therefore, this method would produce a total processing time of approximately 50 minutes in comparison to the 5 hour average processing time using Manual Transcription, while lowering costs to approximately ₽30.00 to ₽80.00 per minute. The Hybrid approach is very effective for processing 70% of the typical transcriptions in that the Automation Model can be utilized to produce the original draft and the Accuracy/Quality Improvements can be made by a Human Proof Reader.

Are there any completely free services without limits?

There are no Completely Free Services with No Limits as the infrastructure to run the services has a financial cost associated with it. But there are solutions for basic needs. Check out automation templates to find ready-made free options - ASCN.AI.