Top 10 Audio Upscaling Services for AI Training

As AI models become increasingly sophisticated, the need for high-quality training data, particularly in audio form, has skyrocketed. Whether you’re building speech recognition engines, voice assistants, emotion detection systems, or generative audio models, the fidelity of your training data can make or break performance.

But many organizations face the same hurdle: low-quality audio.

That’s where audio upscaling services come in. These solutions help enhance, clean, or regenerate audio to studio-quality levels, even when starting from noisy, compressed, or degraded recordings. Some platforms also offer dataset creation and labeling services, streamlining the process from raw input to ready-for-training data.

In this post, we’ve compiled the top 10 audio upscaling services that are purpose-built or highly effective for AI training environments. Whether you’re a startup building your first speech model or an enterprise managing multilingual data pipelines, you’ll find value in this list.

1. Twine AI

Twine AI offers a full-service custom data collection solution for sourcing, re-recording, and delivering studio-quality audio datasets across 160+ languages. Instead of trying to clean bad data, Twine lets you replace low-quality audio with new, human-recorded samples that meet high-resolution specs (48 kHz+).

Their team manages everything: contributor recruitment, script distribution, recording QA, file formatting, and delivery. This is ideal for clients building speech or audio ML models that demand clarity, naturalness, and linguistic variety.

Why clients choose Twine:

• Replace noisy or compressed audio with native-quality re-recordings
• Ethical sourcing with full QA, bias mitigation, and audit trails
• Supports TTS, ASR, NLU, speech emotion, and multilingual datasets
• Scales from a few hours of audio to thousands

2. Defined.ai

Defined.ai offers curated, enterprise-grade audio datasets, as well as custom audio data collection services. They work with clients to capture clean, linguistically diverse voice recordings that are suitable for training high-performance ASR and voice AI models.

Their services include noise-free studio recordings, natural speech, phone call simulation, and speaker diversity, all tailored to your target application. They also support re-recording existing text corpora into clean audio formats.

Why clients choose Defined.ai:

• Multilingual voice data collection in controlled environments
• Fully managed recording, annotation, and delivery
• Ideal for call center AI, speech analytics, and conversational models

3. Alegion

Alegion provides human-in-the-loop data preparation services, including audio enhancement, transcription, segmentation, and speaker labeling. They specialize in taking noisy or raw voice data and producing structured, model-ready datasets with high annotation accuracy.

They work closely with enterprises to identify quality issues in audio corpora and apply enhancement or cleaning via expert workflows.

Why clients choose Alegion:

• Managed labeling services for audio, voice, and speech datasets
• Audio segmentation and enhancement support
• Quality control and audit-ready outputs
• Dedicated project management and workforce

4. Appen

Appen is one of the world’s largest data service providers for AI training, with extensive capabilities in speech data collection, transcription, and re-recording. They offer managed services for clients needing high-resolution, multilingual voice recordings, especially for conversational AI.

While their platform supports crowd-based tasks, Appen’s enterprise services include recording in studio conditions, dialect control, and post-processing to meet ML model specs.

Why clients choose Appen:

• Custom data collection across hundreds of demographics
• Capabilities in accent balancing, age range diversity, and QA
• Supports TTS, ASR, wake word, and command recognition datasets

5. Pangeanic

Pangeanic provides audio cleaning, enhancement, and transcription services as part of its AI data offering. Though traditionally known for translation and NLP, they work with clients to preprocess speech corpora, remove noise, and produce structured multilingual audio datasets.

They also offer voice anonymization, audio segmentation, speaker diarization, and dataset conversion, helping organizations upscale or repurpose legacy audio assets for modern AI models.

Why clients choose Pangeanic:

• End-to-end audio preprocessing services
• Clean, tag, and format existing audio into training-ready data
• Supports anonymization and GDPR compliance
• Multilingual and multicultural speech support

6. Clickworker

Clickworker manages large-scale voice data collection and enhancement projects using a vetted crowd workforce and custom QA layers. Their voice-related services include speech re-recording, enhancement, dialect balancing, and tagging — helping companies scale dataset improvement efforts efficiently.

They also support voice command datasets, conversational prompts, and multilingual corpora creation.

Why clients choose Clickworker:

• Cost-effective, crowd-based data collection
• Managed QA, annotation, and file formatting
• Scales across geographies and languages
• Ideal for command recognition and short-form speech

7. Gengo by Lionbridge

Gengo, backed by Lionbridge, offers managed services for transcription, enhancement, and audio alignment, with a focus on language data for NLP and speech AI. They do not collect new audio at scale like Twine or Appen, but they specialize in preparing raw audio data through cleaning, segmentation, and multilingual transcription.

Why clients choose Gengo AI:

• Human-in-the-loop transcription with integrated enhancement
• Strong multilingual capabilities
• Managed dataset delivery in ML-friendly formats
• Quality control and human review options

8. DataForce

DataForce by TransPerfect delivers custom AI datasets, including audio collection, enhancement, and annotation services. Their global infrastructure enables studio-quality re-recording, audio cleanup, and dialect targeting across hundreds of locales.

They also offer metadata tagging, speech emotion classification, and filtering, making them a strong partner for building high-quality, production-grade voice datasets.

Why clients choose DataForce:

• Multilingual, high-fidelity voice dataset delivery
• Flexible QA pipelines and custom project support
• Integrated transcription and labeling services
• Enterprise-grade security and compliance

Final Thoughts

As AI models increasingly depend on audio for comprehension, interaction, and decision-making, the quality of that audio becomes a critical success factor. Whether you’re building a speech recognition system, training a voice assistant, or developing multilingual audio interfaces, clean, high-resolution audio data is foundational.

While tools and APIs are useful, many teams lack the time, expertise, or internal resources to manage audio enhancement at scale. That’s where audio upscaling service providers offer significant value, combining technical precision, linguistic expertise, and scalable operations to deliver training-ready datasets.

Investing in professionally curated or re-recorded audio not only improves model accuracy and reliability, but also reduces downstream challenges such as bias, transcription errors, and signal inconsistencies.

Before engaging a service provider, it’s important to:

• Assess your dataset’s limitations — is it noisy, low-res, synthetic, or misaligned?
• Define your target output — clarity, consistency, multilingual coverage, speaker diversity, etc.
• Consider compliance and ethical sourcing — particularly for commercial or public-facing AI.

With the right partner, you can turn imperfect or incomplete voice data into a robust asset — one that supports your AI’s long-term success in real-world deployment.