Omtera, as a strategic partner of ElevenLabs, helps businesses transform advanced AI audio technologies into secure, scalable, and measurable business processes. ElevenLabs Speech to Text, also known as STT or speech recognition, reduces teams’ manual transcription workload by automatically converting conversations in audio and video content into written text.
Manually transcribing meeting recordings, analysing customer conversations, preparing subtitles for videos, or categorising call centre conversations can require significant time and operational resources. In addition, steps such as separating speakers, writing specialised terms correctly, adding time codes, and processing different languages make the process more complex when traditional methods are used.
ElevenLabs automates this process with the Speech to Text models in the Scribe family. The platform offers capabilities such as file-based transcription, speaker diarization, word-level timestamps, audio event tagging, multilingual speech recognition, and real-time audio stream processing.
ElevenLabs Speech to Text is an automatic speech recognition solution that converts conversations in audio or video files into machine-processable text. ElevenLabs’ primary model family in this field is called Scribe.
Speech to Text is not simply a basic dictation tool that writes down the words it hears one after another. It analyses the audio signal, identifies the spoken language, recognises words, adds punctuation, and separates different speakers when the required configuration is used. In this way, a raw audio recording becomes a data source that can be searched, summarised, reported, and used in other enterprise systems.
According to ElevenLabs’ current documentation, Scribe v2 was developed to produce accurate transcriptions in more than 90 languages. The model is designed to manage conditions encountered in real-world audio, such as long recordings, tone changes, pauses within speech, and extended periods of silence.
This structure is particularly valuable for teams with the following needs:
ElevenLabs Scribe analyses the audio stream or media file sent to the system and converts speech into structured text. Depending on the method of use, this process can be carried out through the ElevenLabs web interface or the ElevenLabs API.
The user can upload an audio or video file to the ElevenLabs Speech to Text interface. In an API-based project, the file is sent to the relevant Speech to Text endpoint by the application’s backend system.
ElevenLabs provides an API endpoint for transcribing audio and video files. For large-scale or long-running processes, an asynchronous processing model can also be used, allowing results to be received through a webhook. In recordings with multiple audio channels, the channels can be transcribed separately or combined in chronological order.
The model analyses the speech patterns in the recording and generates text in supported languages. Automatic language detection can reduce operational complexity on platforms with users from different countries.
For example, a global customer support team can process Turkish, English, and French conversations within the same workflow. Once the transcriptions have been created, they can be categorised according to criteria such as language, topic, customer type, or conversation outcome.
ElevenLabs Scribe is not limited to providing only a plain-text output. Depending on the use case and selected settings, the following information can also be generated:
This structure makes it easier to use the transcription in video editing tools, analytics systems, search engines, or customer experience platforms.
Scribe v2 provides speech recognition support in more than 90 languages. This feature reduces the need for companies operating in different markets to establish a completely separate transcription infrastructure for every language.
Multilingual support is particularly important for international customer service, media localisation, research interviews, and global training content. However, the accuracy of the results may be affected by factors such as recording quality, background noise, microphone level, the speaker’s accent, and the industry terminology used.
For this reason, enterprise use cases should be tested with real recordings representing different language and audio conditions.
Speaker diarization identifies who is speaking and when within a recording. The system can distinguish different voices and label text segments according to each speaker.
For example, a customer conversation can be structured as follows:
Customer: I would like to learn the delivery date of my order.
Representative: Could you share your order number?
Customer: Of course, my order number is 87432.
This structure does more than make the text easier to read. It also enables customer and representative statements to be analysed separately. This allows businesses to measure customer issues, representative response quality, speaking time, or the frequency with which certain expressions are used.
Word-level timestamps match the words in a transcription with their positions in the audio file. This feature can be used for subtitle generation, video editing, in-conversation search, and the automatic creation of media clips.
A podcast team can use timestamps to find the exact second when a specific topic was discussed in a long episode. A social media team can then select an important sentence and turn that section into short-form video content.
Scribe can also identify certain audio events outside spoken conversation. Laughter, applause, or other meaningful sounds can be labelled within the text. This feature provides richer output, particularly for media content, user research, and interviews in which emotional context is important.
For example, simply writing the sentence “I really liked this feature” may not be sufficient. Whether the sentence was spoken with laughter, hesitation, or a particular change in tone can affect the meaning of the comment.
Keyterm prompting enables the model to prioritise specific words and expressions. According to ElevenLabs’ product guide, users can add lists of words and phrases to guide the model towards uncommon expressions such as proper names, brand names, or technical terms.
This feature is important in the following areas:
For example, a technology company can add the relevant product name to the keyterm list to prevent it from being confused with a standard word. This can reduce the need for manual corrections after transcription.
While Scribe v2 can be used to process prerecorded audio and video files, Scribe v2 Realtime focuses on converting live audio streams into text instantly.
According to ElevenLabs’ current product and model pages, Scribe v2 Realtime operates in more than 90 languages and targets low latency of approximately 150 milliseconds. The system can continuously receive audio segments through WebSocket and send provisional and final transcription results while the speaker continues talking.
Real-time Speech to Text can be used in the following applications:
For example, while a customer is speaking with a voice agent, Scribe v2 Realtime converts the audio into text. The application analyses this text to determine the customer’s request, runs the necessary workflow, and generates a spoken response using Text to Speech technology.
Latency in this process does not depend solely on the Speech to Text model. Network connectivity, the size of audio packets, the location of the application server, other AI models used, and the integration architecture also affect total response time. For this reason, end-to-end latency testing should be performed in real-time projects.
Project managers and team leaders may be forced to choose between taking notes and focusing on the conversation during a meeting. Speech to Text converts meeting recordings into written text, making it easier to extract decisions, action items, and responsibilities.
An example workflow can be established as follows:
This approach enables teams not only to produce transcriptions but also to transform conversations into actionable tasks.
Customer conversations are a valuable first-party data source for businesses. However, manually listening to hundreds or thousands of calls is not sustainable.
Calls can be converted into text using ElevenLabs Speech to Text, allowing the following topics to be analysed:
For example, an e-commerce company can transcribe weekly call recordings and automatically label conversations related to delivery delays. When these results are shared with the operations team, issues involving a specific region, carrier, or product category can be identified earlier.
When marketing teams publish a webinar only as a video, they may limit the value that can be obtained from the content. Once the webinar conversation has been converted into text with Speech to Text, the following content can be created:
In this way, a single recording can be transformed into multiple content assets that can be used across different channels. Word-level timestamps make it faster to locate the relevant video sections.
Product teams, researchers, and marketing professionals gain important insights from customer interviews. However, manually transcribing and coding these interviews can take weeks.
Once interviews have been converted into written text with Speech to Text, teams can analyse recurring problems, expressions used by customers, purchase motivations, and product expectations. Speaker diarization allows the moderator’s questions and the participant’s answers to be separated.
Providing subtitles for users with hearing impairments is an important component of digital accessibility. Speech to Text can support automatic subtitle generation for videos and live broadcasts.
However, automatically generated subtitles should be reviewed by a person before publication, particularly for legal, medical, financial, or reputation-sensitive content.
The ElevenLabs API enables Speech to Text capabilities to be integrated into web applications, mobile applications, call systems, and data-processing workflows.
The first step is to create an API key through an ElevenLabs account. The API key should not be added directly to frontend code or a public repository. It should be stored in a server-side environment variable or a secure secrets management system.
The application sends the audio or video file to be transcribed to the Speech to Text endpoint. During the request, the model, language, speaker diarization, or other processing options can be specified.
The basic logic for file-based processing is as follows:
.svg)
import osfrom elevenlabs.client import ElevenLabsclient = ElevenLabs( api_key=os.environ["ELEVENLABS_API_KEY"])with open("meeting-recording.mp3", "rb") as audio_file: transcript = client.speech_to_text.convert( file=audio_file, model_id="scribe_v2" )print(transcript)Because SDK versions and parameter names may change over time, the latest ElevenLabs API documentation should be checked before the code is moved into production.
The transcription result can be displayed directly to the user or processed for use in other systems. For example, the output can be:
A WebSocket connection is used for live transcription. The client or server sends audio to ElevenLabs in small segments and receives transcription results instantly through the connection.
For security reasons, a permanent API key should not be transferred to the browser. In client-side streaming projects, secure authentication, temporary access mechanisms, and data-transfer policies should be planned at the beginning of the architecture process.
The success of a Speech to Text project does not depend solely on model selection. The data flow, security, integration, user experience, and measurement plan should be considered together.
“Converting audio into text” is not a sufficiently clear objective on its own. The business should first answer the following question:
What business decision or action will be taken after the transcription has been created?
For example, if the purpose is to analyse customer conversations, the categories to be measured should be determined. If the purpose is to generate meeting notes, the system into which decisions and tasks will be transferred should be planned.
Demo recordings often do not represent real operational conditions. The pilot dataset should include different speakers, accents, microphones, background noise conditions, and conversation durations.
The evaluation should not focus only on overall accuracy. Product names, numbers, dates, email addresses, and critical industry terms should be checked separately.
The success of the project can be measured through the following metrics:
Audio recordings may contain personal data, trade secrets, or sensitive customer information. For this reason, businesses should determine which data will be sent, where it will be stored, who will have access to it, and how long it will be retained.
In enterprise projects, access controls, encryption, logging, personal-data masking, deletion procedures, and regional regulations should be evaluated together. ElevenLabs offers enterprise API and platform options, but the structure used should also be reviewed according to the organisation’s legal and technical requirements.
Although ElevenLabs offers powerful Speech to Text models, the correct use case, data flow, and integration architecture must be established to generate business value from these models.
Omtera, as a strategic partner of ElevenLabs, helps companies approach their AI audio investments not simply as a tool purchase but as a measurable transformation project. Omtera’s ElevenLabs service approach can include discovery, solution design, implementation, API integration, governance, and scaling stages.
The main areas in which Omtera can contribute include:
For example, simply converting audio into text may not be sufficient in a call centre project. Transcripts may need to be separated into customer and representative sections, personal information may need to be masked, conversation topics may need to be categorised, results may need to be written to the CRM, and performance may need to be reported in a quality dashboard.
Omtera supports the positioning of ElevenLabs capabilities within this end-to-end workflow, helping make the technology usable in daily operations.
Poor microphone quality, heavy background noise, and people speaking at the same time can affect transcription accuracy. Where possible, audio levels should be standardised and a separate channel should be used for each speaker.
Keyterm prompting should be used for recordings that contain brand, product, person, or technical names. This can reduce incorrect transcription of critical words.
Although Speech to Text systems can provide high accuracy, their results should not be assumed to be error-free. Legal records, health content, financial information, or publicly available subtitles should be reviewed by a person before publication.
When calculating the total cost of ownership, businesses should consider data storage, integration development, monitoring, security, human review, and maintenance costs in addition to transcription usage. Because ElevenLabs pricing may change over time, current prices should be confirmed on the official pages during project planning.
ElevenLabs Speech to Text enables businesses to transform audio and video recordings into searchable, measurable data that can be connected to automation workflows. It offers a broad range of use cases, from meeting notes and call centre analysis to video subtitles and real-time voice agent applications.
However, selecting the correct model alone is not sufficient for a successful implementation. Audio quality, specialised terms, integration architecture, security policies, human review, and success metrics should be planned together. Omtera’s strategic partnership with ElevenLabs and implementation expertise can help companies transform Speech to Text technology from a controlled pilot project into a scalable enterprise solution.
Are you ready to integrate ElevenLabs Speech to Text into your business processes? Schedule a brief meeting with Omtera and take the first step in your ElevenLabs project today.
What is ElevenLabs Speech to Text?
ElevenLabs Speech to Text is an AI-based speech recognition solution that converts conversations in audio and video recordings into written text. Scribe models offer features such as multilingual transcription, speaker diarization, and word-level timestamps.
How many languages does ElevenLabs Scribe support?
According to ElevenLabs’ current documentation, Scribe v2 and Scribe v2 Realtime support speech recognition in more than 90 languages. The level of support and performance should be tested according to the use case.
Can ElevenLabs convert Turkish audio into text?
Yes. Turkish is among the languages supported by ElevenLabs Speech to Text. The quality of the results may be affected by the audio recording, accent, background noise, and specialised terminology used.
Can ElevenLabs transcribe live conversations?
Yes. Scribe v2 Realtime is designed to convert live audio streams into text with low latency through a WebSocket-based API.
Can ElevenLabs distinguish between different speakers?
Yes. The speaker diarization feature helps identify different speakers within a recording and present transcript sections with speaker labels.
Can ElevenLabs be used to prepare subtitles for videos?
Yes. Speech in a video file can be converted into text, and timestamps can be used to prepare subtitles or captions. Manual quality control before publication is recommended for publicly available content.
What is the difference between Speech to Text and Text to Speech?
Speech to Text converts spoken audio into written text. Text to Speech converts written text into AI-generated audio. When the two technologies are used together, voice agents and conversational applications can be created.
Can ElevenLabs Speech to Text be integrated through an API?
Yes. An HTTP API can be used for file-based transcription, while a WebSocket API can be used for real-time transcription. During integration, the API key should be stored securely, and policies for processing personal data should be planned.
How does Omtera support an ElevenLabs implementation?
Omtera can support businesses with use-case analysis, pilot design, API integration, data-flow implementation, security planning, quality testing, and scaling processes.
.webp)
