Audio Labeling services for ml
Unidata offers professional audio labeling services that provide precise annotation and labeling of audio data to enhance speech recognition, transcription, and audio analysis across diverse industries.
Our skilled annotators meticulously label audio recordings with crucial information, including speaker identification, transcription, and acoustic events, ensuring high-quality data for your projects
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What is Audio Labeling?
Audio labeling is the process of annotating and tagging audio data to enhance its usability for machine learning and artificial intelligence applications. This involves identifying and categorizing elements within audio recordings, such as speech transcription, speaker identification, and acoustic events. Audio labeling is crucial for improving the performance of speech recognition systems, transcription services, and audio analysis tools.How we deliver audio labeling services
Step 1
Consultation and Requirements
The process begins with an in-depth consultation phase, where we work closely with the client to fully understand their project requirements. During this stage, we clarify the scope of the audio labeling tasks, identify the specific types of annotations needed (such as speech-to-text transcription, speaker identification, emotion labeling, or sound classification), and establish clear objectives for the project. We also gather sample data to evaluate its complexity and discuss potential challenges like background noise, speaker overlap, or audio quality issues. This phase is critical to ensuring that both parties have a shared understanding of the project requirements, accuracy expectations, and delivery timelines before proceeding.
Step 2
Team and Roles Planning
Once we have a clear understanding of the project, we proceed to the team and roles planning phase. Here, we assemble a dedicated team that is tailored to the project’s specific needs. A project manager is appointed to oversee the entire process and serve as the primary point of contact with the client, ensuring smooth communication throughout. We allocate experienced audio labelers with specialized knowledge in the relevant domain, such as speech recognition, linguistic analysis, or sound classification. Quality assurance specialists are brought in to ensure that the labeled data meets the required standards, and data engineers are available to handle any technical challenges that arise during the project. By assigning the right people to the right roles, we ensure that the project is executed efficiently and that every task is handled by experts.
Step 3
Tasks and Tools Planning
In the tasks and tools planning phase, we break down the project into specific tasks and establish a detailed workflow. We identify the optimal annotation methods for each task, whether it involves transcribing speech, tagging sound events, or labeling different speakers. We carefully assess the complexity of the data and plan how to divide the workload across the team, ensuring that each team member is assigned tasks that match their expertise. During this phase, we also develop a strategy for managing the workflow efficiently, whether through task batching, parallel processing, or using automation tools to assist with repetitive tasks. This planning helps to minimize bottlenecks and ensures that the project runs smoothly.
Step 4
Software Selection
Next, we move on to the software selection phase, where we choose the most suitable tools for executing the audio labeling tasks. We evaluate a range of software options to find those that best align with the project requirements, such as tools that support speech-to-text, speaker diarization, or sound classification. We also consider the importance of automation features that can expedite the annotation process, such as AI-powered transcription tools. Additionally, we ensure that the chosen software integrates seamlessly with the client’s machine learning framework, making it easy to incorporate the labeled data into their existing workflow. The selection of the right software is crucial for both the efficiency and accuracy of the project.
Step 5
Project Stages and Timelines
Once the software has been selected, we create a comprehensive project plan that outlines the stages and timelines for each phase of the project. The project typically starts with an initial setup phase where we configure the software and conduct a small-scale pilot test to validate the labeling approach. This is followed by the full-scale annotation phase, during which we break the project into milestones and provide regular progress updates to the client. After the annotation phase, the project moves into the quality assurance stage, where the data is validated for accuracy. Finally, we prepare the data for delivery, formatting it according to the client’s specifications. Sharing this timeline with the client ensures transparency and keeps all stakeholders aligned on deadlines and progress.
Step 6
Annotation Tasks Execution
During the annotation tasks execution phase, our audio labelers begin processing the data according to the predefined guidelines. Labelers carefully annotate the audio data, whether they are transcribing speech, tagging speakers, or classifying sound events. Depending on the project, AI-assisted tools may be used to speed up the process, especially for large-scale projects. The project manager continuously monitors the team’s progress and ensures that the work remains on schedule and meets the required quality standards. Regular check-ins with the client help to ensure that the annotations align with their expectations, and adjustments are made if necessary.
Step 7
Quality and Validation Check
Following the execution phase, we conduct a rigorous quality and validation check to ensure the highest accuracy of the labeled data. Our quality assurance team reviews the annotations for consistency and correctness, using automated tools to detect errors, such as incorrect speaker labeling or inaccurate transcriptions. Manual checks are conducted for complex or ambiguous audio segments that require more detailed scrutiny. If any errors or discrepancies are found, they are corrected during this phase to ensure that the final dataset meets the agreed-upon standards of accuracy and quality.
Step 8
Data Preparation and Formatting
Once the data has passed the quality assurance stage, we move on to data preparation and formatting. In this phase, we convert the labeled audio data into the required format, such as text transcripts, time-stamped annotations, or categorized sound files. We ensure that the data is organized and formatted according to the client’s specific needs, making it easy for them to integrate the labeled data into their machine learning pipelines or other systems. If necessary, we apply encryption and compression to the files to ensure secure delivery.
Step 9
Prepare Results for ML Tasks
In preparation for machine learning tasks, we ensure that the labeled audio data is structured in a way that optimizes its use for model training or evaluation. We ensure consistency and accuracy across all annotations, including any required metadata such as timestamps or speaker identification. This preparation ensures that the labeled data is ready to be used effectively in machine learning models, whether for speech recognition, sound classification, or any other application.
Step 10
Transfer Results to Customer
Once the data is finalized and prepared, we securely transfer the results to the client. Depending on the client’s preferences, this can be done through secure cloud storage, such as AWS or Google Cloud, or via secure FTP transfers for sensitive data. In some cases, we may also deliver large datasets via encrypted external hard drives. We ensure that the transfer process is seamless and secure, protecting the client’s data while meeting any confidentiality or data privacy requirements.
Step 11
Customer Feedback
After the data has been delivered, we actively seek customer feedback to ensure that the client is fully satisfied with the final results. We conduct a detailed review with the client to confirm that the data meets their expectations and discuss any potential revisions or adjustments that may be needed. We also gather feedback on the overall process, including communication, quality of the work, and the efficiency of the project. Based on this feedback, we make any necessary improvements and incorporate the client’s suggestions into future projects. This collaborative approach helps us continuously improve our services and build strong, lasting partnerships with our clients.The best software for audio labeling tasks
Audacity
Audacity is a free, open-source audio editing tool that allows users to annotate, edit, and process audio files. While primarily designed for audio editing, it offers useful tools for basic audio annotation tasks, such as marking segments or labeling time-stamped events in audio files.
Key Features:
- Ability to label and annotate multiple tracks and audio segments.
- Extensive editing tools, including noise reduction and filtering, to improve audio quality before labeling.
- Free and open-source, allowing for customization.
- Supports a wide range of audio file formats.
Best For:
Small teams or individuals looking for a free and flexible tool to handle simple audio labeling and editing tasks.
Labelbox
Labelbox is a versatile data labeling platform that supports multiple types of data, including audio. It offers AI-assisted annotation tools to speed up the labeling process and includes collaborative project management features.
Key Features:
- AI-powered tools to accelerate audio labeling tasks, such as transcription and speaker identification.
- Flexible annotation tools, including word-level timestamps and event labeling.
- Built-in quality control to ensure high accuracy.
- Integration with popular machine learning frameworks for seamless data export.
Best For:
Teams seeking a comprehensive labeling solution with a focus on audio labeling alongside other data types.
Sonix
Sonix is a powerful AI-driven platform designed for automated transcription of audio and video files. It offers an intuitive interface for editing transcripts and fine-tuning the output, making it ideal for fast and accurate speech-to-text tasks.
Key Features:
- Automated transcription with high accuracy, supporting multiple languages.
- Easy-to-use transcript editor for correcting and labeling specific segments.
- Export options for integration with other tools or machine learning models.
- Features for speaker identification and timestamped annotations.
Best For:
Teams or individuals looking for a fast and efficient way to convert speech to text and annotate audio files, especially for large-scale transcription tasks.
Descript
Descript is an audio and video editing platform with advanced transcription capabilities. It allows users to label and annotate audio data as part of the editing process, making it a great tool for creating transcripts and synchronizing audio with text.
Key Features:
- Automated transcription with built-in editing tools.
- Supports collaborative annotation and editing for team projects.
- Word and phrase-level timestamping, with easy export options.
- Integration with popular platforms for seamless workflow.
Best For:
Teams looking for an intuitive, all-in-one tool for both audio editing and transcription-based annotation.
Speechmatics
Speechmatics provides high-accuracy speech-to-text transcription with advanced machine learning models. It is particularly strong in handling challenging audio environments, making it suitable for diverse audio labeling tasks across industries.
Key Features:
- Highly accurate transcription with support for multiple languages and dialects.
- Real-time and batch processing options for various use cases.
- Customizable language models to enhance accuracy for specific domains.
- Integration with cloud services and APIs for streamlined workflows.
Best For:
Organizations requiring robust, scalable transcription services for large or complex datasets, particularly in industries like media, finance, or legal.
Transcribeme
Transcribeme is a specialized transcription platform that combines AI and human transcription services for maximum accuracy. It offers a range of labeling and transcription services, with a focus on delivering high-quality text from audio files.
Key Features:
- Hybrid AI and human transcription for high-accuracy results.
- Supports various audio formats and offers custom solutions for different industries.
- Speaker identification and timestamped annotations.
- Secure platform with a strong focus on data privacy.
Best For:
Teams looking for highly accurate transcription services that combine the efficiency of AI with human oversight, especially for sensitive or complex audio data.
Rev
Rev offers a range of transcription and captioning services, with both automated and human-powered options. It provides an intuitive platform for annotating and labeling audio, ideal for generating transcripts with high levels of accuracy.
Key Features:
- Automated speech recognition alongside human transcription services for flexibility.
- Speaker identification and timestamping features for precise annotation.
- Easy-to-use editing tools for refining transcripts.
- Integration options with other platforms for seamless workflow management.
Best For:
Businesses and content creators seeking reliable transcription and captioning services for audio files of varying complexity.
Voicemod
Voicemod is an audio editing tool that offers real-time audio processing and labeling capabilities. Though designed primarily for voice manipulation, it has powerful tools for annotating, segmenting, and categorizing audio data.
Key Features:
- Real-time audio editing and manipulation tools.
- Supports tagging and labeling of different sound segments.
- Intuitive user interface, making it accessible for various audio labeling tasks.
- Integration with streaming and recording platforms for real-time processing.
Best For:
Users who need real-time audio processing and labeling, particularly in voice-related tasks like gaming, streaming, or interactive media.
Types of audio labeling services
Speech-to-Text Transcription
This form of audio labeling involves converting spoken language in audio files into written text. It is often used in applications such as voice assistants, transcription services, and speech recognition models. Transcriptions can be done verbatim (word-for-word) or with some level of cleaning to remove filler words and hesitations.
Speaker Diarization
Speaker diarization refers to labeling audio data to identify when speakers change within a conversation. The process involves segmenting the audio and assigning a unique label to each speaker. This is critical in multi-speaker environments, such as meetings, interviews, or podcasts, and helps machine learning models recognize and differentiate between speakers.
Emotion Labeling
Emotion labeling involves annotating the emotional tone of the speaker’s voice within audio data. Emotions such as happiness, anger, sadness, or neutrality are tagged to specific segments of the audio. This type of labeling is important for applications in sentiment analysis, customer service, and virtual assistants.
Sound Event Detection
In sound event detection, audio segments are labeled with specific sound events, such as clapping, coughing, sirens, or doorbells. This form of labeling is used in projects that require machine learning models to detect and classify environmental sounds, such as smart home devices, security systems, or audio surveillance.
Phoneme Labeling
Phoneme labeling involves annotating the smallest units of sound in speech, known as phonemes, which represent individual sounds that make up words. This type of annotation is typically used in linguistics research and speech synthesis technologies, such as text-to-speech systems.
Word Timing Annotation
This type of labeling provides precise timestamps for when specific words or phrases occur within the audio. It’s often used in conjunction with speech-to-text transcription to help synchronize audio with text for use in closed captioning, subtitles, or AI-driven search functions in media.
Language Identification
Language identification involves labeling audio segments with the language being spoken. This service is used in multilingual datasets where it's important to identify and distinguish different languages within a single audio file. It is commonly applied in language processing tools, call centers, and language translation applications.
Acoustic Scene Classification
Acoustic scene classification involves labeling audio data based on the environment or scene in which the sound was recorded. Examples include identifying whether a recording was made in a park, office, city street, or home environment. This type of labeling is valuable for context-aware systems and urban sound mapping.
Noise Labeling
Noise labeling annotates non-speech elements within an audio file, such as background noise, static, or other auditory interferences. This type of labeling is useful in applications where it's important to detect and filter out noise from useful audio signals, such as in hearing aids, telecommunication systems, or audio enhancement software.
Music Annotation
Music annotation involves labeling components of music tracks, such as identifying instruments, tempo, genre, or specific segments like chorus and verse. This type of labeling is widely used in music recommendation systems, audio search engines, and automated music analysis tools.Audio Labeling Use Cases
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01Healthcare
In healthcare, audio labeling annotates data from doctor-patient interactions, medical consultations, and diagnostic recordings. Labeling specific terms like symptoms, diagnoses, and medical history helps AI systems recognize important information, enabling faster and more accurate decision-making. Annotating patient conversations also assists in predicting health outcomes and streamlining administrative tasks. -
02Automotive (Autonomous Vehicles)
This technology helps to train AI to understand and respond to voice commands, environmental sounds, and traffic-related audio. Labeling sounds such as sirens, honking, or pedestrian voices enables AI to react in real time to dynamic road conditions. Annotating audio data related to in-vehicle conversations or commands also enhances the vehicle’s ability to interact with passengers and navigate safely. -
03Retail & E-commerce
This service improves customer service by analyzing voice interactions between customers and service agents. Labeling audio data from customer queries, complaints, and product inquiries helps AI understand intent and provide more accurate responses. Annotating customer feedback and product-related audio also helps enhance recommendation systems and optimize voice search capabilities on e-commerce platforms. -
04Agriculture
In agriculture, audio labeling helps to monitor and analyze sounds in farming environments, such as animal behavior, machinery noise, or environmental sounds. By labeling animal sounds, AI can detect distress signals or health issues. Annotating machinery and equipment sounds also helps identify malfunctions or inefficiencies, allowing for predictive maintenance and improved farming practices. -
05Finance
Such labeling methods are used to transcribe and analyze customer service calls, investment meetings, and financial discussions. By labeling keywords and financial terms in conversations, AI can extract valuable insights, improve customer support, and detect fraud. Annotating audio data from earnings calls and financial reports also helps in assessing market trends and making informed decisions. -
06Security & Surveillance
Audio annotation is crucial in security and surveillance systems for identifying key sounds such as gunshots, alarms, or unauthorized access. Labeling audio from surveillance cameras or emergency calls enables AI to detect and alert security teams to potential threats in real time. Annotating sounds related to suspicious activity, like breaking glass or yelling, enhances threat detection and response times. -
07Manufacturing
In manufacturing, tagging is used to monitor machinery and production lines. By annotating sounds such as machinery malfunctions, irregular vibrations, or alarms, AI can help detect potential issues before they lead to downtime. Labeling audio data from workers’ communications also assists in safety protocols and improves operational efficiency by ensuring that critical alerts are quickly recognized. -
08Entertainment & Media
In the entertainment industry, this technique is used to analyze dialogue, sound effects, and music in videos or movies. Labeling voice data, background sounds, or specific themes in dialogues helps improve transcription accuracy and enables better content categorization. Annotating music and sound effects also helps improve audio search functionality and recommendation systems for streaming platforms.
Banking Call Categorization
To automate call categorization, one of Eastern Europe’s largest banks entrusted us with sensitive voice data covering credit, debit, deposits, and balances. We built a privacy-first annotation pipeline with in-house experts, multilayer validation, and weekly reporting to ensure both compliance and accuracy—enabling faster, smarter service automation.
- Industry and use case
- Banking
- Data
- 363,000 audio files
- Project duration
- 50 days
Speaker-Segmented Audio Annotation
Automated transcription alone couldn’t handle the nuance of real conversations with background noise and interruptions. Our human-in-the-loop workflow ensured every […]
- Industry and use case
- Speech AI
- Data
- 20 hours of audio, 2 task types (segmentation and transcription)
High-Load Audio Transcription
For one of our clients, we completed the transcription of 80 hours of audio files without using pre-labeling. The project […]
- Industry and use case
- Telecom
- Data
- 80 hours per month
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