AI voice has entered the "Tesla moment", and a single workflow is "devouring" the global market worth tens of billions.

The voice assistant in the kitchen accurately responds to commands, virtual idols seamlessly switch between seven languages during live broadcasts, and short dramas going global can generate multilingual voiceovers with just one click... These AI voice scenarios, once highly anticipated by the industry, have long been limited by technological bottlenecks and become "semi-finished products in the laboratory."

In March 2025, OpenAI officially launched a new generation of audio models: gpt - 4o - transcribe (speech - to - text), gpt - 4o - mini - transcribe (speech - to - text), and gpt - 4o - mini - tts (text - to - speech). Developers can access the API to obtain the required AI capabilities and achieve more efficient voice content production.

Among them, the capabilities of gpt - 4o - mini - tts are quite interesting: AI can preset different voice styles according to the developers' needs, and by changing the styles, the fun and realism brought by the Agent will be greatly enhanced.

As a leading enterprise in the industry, OpenAI's voice models have shown countless developers new opportunities. Perhaps the only drawback is that only the API interfaces for relevant model functions are open. For most users, they can only use AI to complete some simple content creation.

The next focus of industry competition will shift from the "parameter race" to the "industrial implementation ability" - whoever can first meet the real production needs with industrial capabilities will have the opportunity to take the lead in the new round of industry competition for the "strongest AI voice."

In this transformation, the breakthrough path of "All Voice Lab" launched by Quwan Technology is highly representative (currently in invitation - only testing). It has firmly grasped the technological steering wheel through the batch - processing and standardization capabilities demonstrated by the MaskGCT model.

Breaking Technological Barriers: The Underlying Logic of AI Voice - Driven Full - Process Intelligent Transformation

Before All Voice Lab made the industry re - recognize AI voice, there were already some AI products with similar functions in the market. However, from a practical perspective, many traditional AI voices are still like "handicraft workshops," while All Voice Lab aims to build a "Foxconn."

This product integrates multiple capabilities such as text - to - speech, video translation, and multilingual synthesis, and simultaneously supports refined functions such as seamless subtitle erasure. It can provide a one - stop, full - process intelligent voice solution.

Relying on the capabilities of the MaskGCT model jointly developed by The Chinese University of Hong Kong, Shenzhen and Quwan Technology, the voice generation effect is more emotional, comparable to that of a real person, and finely controllable.

It is reported that MaskGCT has reached the SOTA (state - of - the - art level) on multiple TTS benchmark datasets, surpassing the current most advanced similar models, and even exceeding the human level in some indicators. It has further broken through in terms of voice similarity, quality, and stability, especially leading absolutely in voice similarity.

It is worth mentioning that to make AI voice more industrialized and applicable to more scenarios requiring a large amount of repetitive work, All Voice Lab has achieved the full - process automation of video translation for the first time - subtitle erasure, translation, dubbing, post - production, and delivery of the final video. It can batch - process 40G of video at one time, with a daily processing volume exceeding 1000 minutes, and the efficiency is more than 10 times higher than that of traditional dubbing. Behind these figures, it not only leaves ElevenLabs, which supports single - uploads of 45 - minute videos, far behind but also represents a dimensionality - reduction blow of industrial capabilities over laboratory prototypes.

We used a speech video of a 36Kr CEO for a video translation test and could feel that the generated voice highly restored the intonation and emotion of the original voice. The cross - language synthesis effects in English and Japanese were clearly pronounced, natural, and smooth, approaching the quality of real - person recordings.

Taking the short - drama application scenario as an example, its core pain point is "high frequency and low price": Overseas users have a strong demand for instant content, but the cost of traditional dubbing is as high as 200 - 300 yuan per minute, and the cycle is as long as 30 days.

"This is not only a technological iteration but also a reconstruction of production relations," revealed the technical director of a domestic short - drama platform. After connecting to All Voice Lab, the dubbing cycle was compressed from 30 days to 3 days, and the number of overseas users increased by 300%. Behind the soaring efficiency is the extreme simplification of the Agent workflow, which requires no manual intervention throughout the process. This ability has quickly attracted leading short - drama platforms and promoted a 300% increase in their overseas users.

The maturity of industrialization means that AI voice technology has a lower threshold and cost. More content creators will have the opportunity to enter the "fast lane" of the AIGC era, liberate production efficiency, and release more creative inspiration.

Scenario Expansion: Evolving Step by Step from "Small" to "Global Content Infrastructure"

A seemingly minor technological breakthrough can often tear open a crack in a huge market.

The core logic of the product implementation path chosen by All Voice Lab is to use industrial capabilities to meet the large - scale needs of cross - language communication and become the "invisible operating system" of the global content industry chain. Starting from the vertical scenario of content going global, it gradually penetrates into diversified fields such as news, culture and tourism, enterprise services, and public services, and finally reconstructs the cooperation paradigm of the global content industry chain.

When industrial translation capabilities meet large - scale needs, any content form that requires cross - language communication - whether it is the zero - latency distribution of news videos or the real - time dialect conversion of museum guides - will become a new growth point.

In the news field, some media's international - version videos can generate English, Japanese, and Korean versions with one click through All Voice Lab and be distributed to TikTok and YouTube simultaneously, reducing the labor cost to zero; in the cultural and tourism scenario, Cantonese explanations can be converted into English in real - time to suit international tourists in museums; in the audiobook market, the system automatically assigns voices to characters, and the production cycle of a one - hour audiobook is shortened from 3 days to 20 minutes.

This logic of "small entry point, big opportunity" is similar to Tesla using the Model S to open up the electric - vehicle market: First, use extreme efficiency to conquer a high - demand scenario, and then use standardized capabilities to horizontally capture a multi - billion - dollar market. According to the "2024 Global Digital Content Industry Report," the scale of multilingual translation needs in the media and pan - entertainment fields alone has exceeded 65 billion US dollars, and All Voice Lab is becoming the core infrastructure in this track.

Judging from the existing products on the market, even for functions such as multilingual synthesis that seem to be homogeneous, All Voice Lab also performs excellently, especially the Chinese effect is surprisingly good in terms of pauses, rhythms, and pitch accuracy.

(You can listen to the audio on the WeChat platform: https://mp.weixin.qq.com/s/D8mmTazK3--zb3vcKrS_cQ)

In addition, there is greater potential in ecological positioning.

When AI voice becomes "invisible" enough, it will no longer be limited to a single function but will become a "super - application base" across terminals and scenarios - just as WeChat integrates social networking, payment, and mini - programs, the technology of All Voice Lab can be embedded in terminals such as mobile phones, AR glasses, and in - car audio to support diversified services such as intelligent voice interaction and navigation.

This ability coincides with the logic of the "super - application" that was widely discussed in the AI industry in 2024: Through standardized interfaces and an open ecosystem, industrial voice capabilities are transformed into "digital water and electricity" that can be called on demand, becoming the invisible operating system of the global content industry chain.

"The best AI voice in the future is one that makes people unaware of the existence of AI." This assertion from an Amazon Web Services executive is being verified by All Voice Lab. When the technological parameter race fades away, the real winner will be the ability to solve real needs on a large scale - and the super - application is the ultimate form of this ability.

Just as Tesla revolutionized the automotive industry with the assembly line, All Voice Lab is evolving AI voice from a "laboratory specimen" to "global content infrastructure." And the "strongest AI voice" may not be an application but a new energy driving the development of the AI era.