How VoiceCheap Achieved 2x Faster Processing, 3x Better Quality & Happy Users with LALAL.AI

AI-assisted dubbing tools grow in popularity, and so does the growing criticism: many say they lack the warmth, nuance, and authenticity of human-translated content. But for VoiceCheap, authenticity isn’t just the voice but preserving the full audio experience, including background sounds, ambiance, and timing. We spoke to Kevin Rousseau, the technical founder and CEO of VoiceCheap, to understand how his team is building a smarter, faster way to localize video content and why stem separation is the foundation of it all.

In this conversation, Kevin shared how LALAL.AI became an essential part of their dubbing pipeline, helping them deliver high-quality, multilingual content in 30+ languages that feels and sounds just as real as the original.

VoiceCheap's Mission Is to Break Language Barriers for Content Creators Worldwide

Launched in May 2024, a French startup VoiceCheap offers AI-driven video dubbing and translation services. It enables users to translate content into over 30 languages while preserving the original speaker’s voice tone and style. The platform handles the entire localization pipeline: transcription, contextual translation, voice cloning, dubbing, and lip-sync.

VoiceCheap is currently run by a small but highly efficient team of three, based in Paris. Since its launch a year ago, the startup has entered a strong growth phase, with thousands of registered users and a steadily increasing number of paying customers. To date, it has successfully processed thousands of videos, attracting both individual creators and business clients as its user base continues to expand.

Their primary clients are content creators, YouTubers, training vendors, and especially businesses selling courses internationally.

"We work with a large French company selling trading courses that needs to localize their content for global markets. The main concern for all clients is maintaining authenticity while scaling their content internationally."

VoiceCheap simplifies the dubbing process by taking a video, either uploaded directly or via a link, and extracting its audio for stem separation with LALAL.AI. It then transcribes the speech, translates it contextually using AI, and generates a cloned voice in the target language with ElevenLabs. The translated voice is synced with the original timing using SmartSync, mixed with the preserved background sounds, and optionally lip-synced before the final dubbed video is rendered and delivered.

"VoiceCheap's mission is to break language barriers for content creators worldwide."

The process doesn't flow without challenges, though. Preserving the authenticity of background sounds and ambiance, and accurately handling overlapping speech in complex audio environments are problems the team faces during their routine.

Maintaining high audio quality during stem separation is also critical, especially when dealing with long-form content or varying audio quality from diverse source materials.

"Our users expect the dubbed content to feel as authentic as the original. Poor vocal isolation leads to artifacts, echo effects, or loss of environmental context. We offer two processing options: 'Studio' for clean voice and 'Realistic' that preserves the recording environment, both require excellent stem separation to work properly."

That's why VoiceCheap has chosen LALAL.AI.

LALAL.AI Met All 3 VoiceCheap's Criteria: Quality, Speed, Stability

Kevin discovered LALAL.AI through a Google search while looking for professional-grade stem separation solutions.

"I tested your web application first and was impressed by the quality: no artifacts, clean separation, and fast processing. Your API documentation was clear, which made the decision to integrate it straightforward."

"LALAL.AI is fully integrated via API into our backend infrastructure. It's a core component of our audio processing pipeline."

VoiceCheap mentions that the integration was surprisingly smooth: it took only about a week to fully implement and test.

"The API documentation was clear, and the service has been remarkably stable. The only surprise was how much better the quality was compared to our previous solutions."

When looking for a stem separation solution, the team was trying to solve three critical issues: quality, speed, and stability. The tool should be able to make sure there was no artifacts left after the stem separation, processing has to be fast as it used to be too slow for commercial use, and it shouldn't crash or fail on long videos as this problem directly impacted client satisfaction and limited VoiceCheap's ability to scale. Luckily, LALAL.AI met the expectations.

"Before LALAL.AI, I used the open-source Spleeter model from Deezer. While it worked for basic cases, it had significant limitations. Processing time was extremely long, the quality was inconsistent with frequent artifacts, it struggled with complex audio environments and there was no API stability for production use."

"None of the tools I tested met all our requirements for quality, speed, and stability. LALAL.AI was the first solution that satisfied all criteria."

LALAL.AI plays its part right at the very beginning of the translation pipeline, immediately after extracting audio from the video. VoiceCheap team is sure it's the crucial first step that enables everything else.

"We exclusively use LALAL.AI for stem separation, namely separating vocals from instrumental or background audio. This allows us to preserve original background sounds (traffic, nature, ambient noise), isolate clean vocals for transcription, and maintain environmental authenticity in the final dubbed version."

"Speed improved by 2x and quality by 3x. Processing time went from being prohibitively long to just seconds or a few minutes for longer content. Since implementing their latest model, quality complaints have dropped to nearly zero."

When working with several content creators, they immediately noticed the improvement, VoiceCheap says. One YouTuber specifically mentioned that the absence of artifacts and the clean separation made their multilingual content feel genuinely authentic. The preserved background sounds made viewers forget they were watching dubbed content.

"LALAL.AI excels at handling complex audio scenarios. It cleanly separates even when there's background music or environmental noise, allowing us to preserve the original atmosphere while replacing vocals. This is especially important for vlogs, outdoor content, and videos with music.

"Besides, I'd love to see voice cloning capabilities in the API as this would allow us to streamline our pipeline by using LALAL.AI for both separation and voice cloning.

" Beyond localization, I would absolutely recommend LALAL.AI for podcast production (removing background noise), film post-production (dialogue isolation), educational content (cleaning lecture recordings), and music production (stem separation for remixes). Any industry dealing with audio content would benefit from this level of separation quality."

Stem Separation Lets Keep AI-Assisted Dubbing Authentic

Just like many AI-powered tools these days, AI-dubbing solutions often face criticism that content translated or dubbed with the assistance of artificial intelligence can't feel authentic. VoiceCheap isn't the exception, and Kevin has his own views on that:

"Our response is that authenticity comes from preserving the complete audio experience, not just the voice. By keeping original background sounds and environmental audio, the dubbed version maintains the 'feel' of the original. This is why high-quality stem separation is crucial as it allows us to preserve what makes content authentic while replacing only the voice."

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