Inventory of AI video to text tools, 6 free subtitle generators available in 2026

Content creators in 2026 will be almost inseparable from AI video-to-text tools. Whether it is organizing a two-hour meeting recording into meeting minutes, quickly adding accurate Chinese subtitles to a short video, or even converting an entire podcast episode into a searchable verbatim transcript, these tasks that in the past could only be completed by manual keyboarding can now be done by AI in a few minutes. There are many tools on the market that can convert video to text, including open source and free ones, free online services from major manufacturers, and paid products for professional users. This article sorts out 6 tools that are commonly used in 2026 and can be started for free, clearly explaining their respective advantages, shortcomings and suitable groups, and helping you choose the most convenient one according to the scenario.

1 Why more and more people can’t live without AI video-to-text conversion

The first typical scenario is content creation. It is almost a necessity for short video bloggers to convert spoken videos into subtitles. In the past, they either spent dozens of dollars outsourcing it to a subtitle group, or they typed it line by line themselves. Now open any mainstream tool, drag in the video and you will get a subtitle file with a timeline in a few minutes, and you can directly suppress it with a little proofreading. For those who make long videos and podcasts, converting the complete audio into verbatim drafts can also be used to generate introductions, extract golden sentences, and create SEO titles.

A second typical scenario involves knowledge workers sorting out meetings and interviews. For a two-hour online meeting, the AI ​​tool can provide a verbatim transcript differentiated by speaker in a few minutes, and then add a layer of summary function to directly generate meeting minutes. When reporters and researchers conduct interviews, they are becoming more and more accustomed to using tools to transcribe first, and then taking notes and quotes on the transcribed text. The efficiency is an order of magnitude higher than repeatedly rewinding and listening to the recording.

The third typical scenario is learning and data archiving. A large number of overseas courses, technology sharing, and industry interviews exist in the form of videos. After being converted into text, it is easy to search for keywords, and it is also convenient to use AI summary tools to further refine the key points. Precipitating the videos you have watched throughout the year into a searchable text database is becoming more and more common in knowledge management circles.

2 evaluation dimensions, look at tools from these 4 perspectives

When choosing a video-to-text tool, most people are most concerned about accuracy. In Chinese scenarios, accents, technical terms, multi-person conversations, and background noise will all affect the quality of transcription. Generally speaking, major manufacturers have more sufficient training data for Chinese recognition, and the results are usually better than those of foreign general models. In the English scenario, the open source model represented by Whisper is recognized as one of the better choices in the industry.

The second dimension is language support. If you only produce Chinese content, it is enough to choose a tool with Chinese optimization. If it involves multiple languages ​​such as English, Japanese, Korean, etc., as well as dialect recognition requirements, please refer to the language list supported by the specific product. Open source models such as Whisper have relatively comprehensive multilingual support, which is its advantage.

The third dimension is price and restrictions. Free tools generally have a time limit or monthly quota, and you will be charged after exceeding it. Some tools charge based on audio and video duration, and some charge based on monthly subscription fees. Please refer to the official page for specific prices. For people who use it once in a while, the free quota is often enough; for professional users who need to process a large amount of materials every day, they need to calculate the cost-effectiveness.

The fourth dimension is processing speed and convenience. Online tools are convenient but have upload time and file size limitations. Local deployment is fast but requires a certain technical threshold. It also depends on whether it supports exporting common subtitle formats such as SRT and VTT, whether it can distinguish speakers, and whether it provides timeline alignment. These details determine whether the tool is easy to use.

3 OpenAI Whisper, the playability of the open source king

Whisper is OpenAI's open source speech recognition model. It can be said to be a landmark project in the field of open source speech recognition in the past few years. It supports hundreds of languages. The Chinese recognition effect is recognized as a good level in the open source model in the industry, and its robustness to noise and different accents is also good. The biggest advantage is that it is completely free and can be run locally. There is no need to upload audio to any third-party server. It is especially suitable for privacy-sensitive scenarios.

In terms of usage, Whisper has two paths. Those with strong technical skills can download the model weights and run them on their own computers. There is an optimized implementation of whisper.cpp on Mac, and even an ordinary notebook can run smaller models. Users who don't want to bother can use OpenAI's official API and pay according to the audio duration. Almost all third-party desktop tools (such as applications such as MacWhisper, Buzz, and Aiko) are based on a more friendly interface packaged by Whisper.

The shortcoming of Whisper is mainly that it does not distinguish between speakers. If there are multiple people talking to an audio piece, the converted text will only have continuous sentences, and post-processing tools will be needed to separate the speakers. In addition, its processing of Chinese punctuation marks is not perfect, and sometimes it is necessary to manually add commas and paragraphs.

Who is it suitable for: Individual users who value privacy and cost, developers and creators with certain technical abilities, and people who need multi-language support.

4 Tongyi Listening Comprehension, the first choice for Chinese scenes written by Ali

Tongyi Tingwu is a speech-to-text service launched by Alibaba. Its Chinese recognition performance ranks first among domestic services. Backed by Tongyi Qianwen's language model capabilities, its experience in Chinese punctuation, segmentation, and speaker distinction is relatively refined, and the resulting manuscript is more readable and requires less changes.

Tongyi Tingwu’s trump card is a complete set of supporting functions in addition to transcription. After uploading a video, it not only gives you a verbatim transcript, but also automatically generates abstracts, keywords, and to-do items, and directly outputs the meeting content in a structured manner. For content such as meetings, interviews, and training, this assembly line can save a lot of time in organizing. It also supports direct integration with office suites such as DingTalk, making it easier to implement in enterprise scenarios.

In terms of price, there is a free quota. You basically don’t have to pay for a small amount of daily use. After the free quota is exceeded, you will be billed based on the length of time. Please see the official page for details. For individual users, the free quota is usually enough to handle a few interviews or conference recordings on a daily basis.

The disadvantage is that for fields with rich professional terms (such as medicine, law, semiconductors), the recognition accuracy will decrease and later proofreading is required. For English audio, the effect is not as good as English-based tools of the same size.

Who it’s suitable for: Creators of meetings, interviews, and podcasts in Chinese, as well as knowledge workers who need to convert speech directly into structured notes.

5 Feishu Miaoji, in-depth integration of meeting scenes

Feishu Miaoji is a meeting recording function integrated into Feishu office suite owned by ByteDance. Its core advantage is that it deeply embeds the conversion of audio and video into text into the meeting process. After the Feishu meeting is over, Miaoji will automatically generate a complete verbatim draft with a timeline and speaker tags, which all participants can directly consult.

Its Chinese recognition effect is stable, and its accuracy is among the best among domestic services. The most distinctive feature is the AI ​​intelligent summary function, which automatically extracts discussion points, decision-making items, and to-do assignments from a meeting. The generated meeting minutes are basically usable in most cases and only require slight modifications.

In addition to meeting scenes, Miaoji also supports separately uploading audio and video files for transcription. Free users have a limited quota, after which they need to subscribe to the enterprise version. For teams that are already using Feishu Office, Miaoji is an out-of-the-box capability with almost no additional learning costs.

The disadvantage is that the experience will be compromised after leaving the Feishu ecosystem. If the team does not use Feishu, it is not cost-effective to switch to the office suite for Miaoji alone. In addition, its processing flow for pure recording (not Feishu meetings) is not as smooth as native integration.

Who is it suitable for: Teams that are already using Feishu Office, as well as medium-sized organizations that have higher requirements for the quality of meeting minutes.

6 Cutting and CapCut, subtitle functions that creators can easily use

CapCut (the overseas version is called CapCut) is a video editing tool produced by the same company as Douyin and TikTok. Its built-in automatic subtitle function allows countless short video creators to bid farewell to the era of manual subtitles. Open a new clipping project, drag the video into it, select automatic subtitles, and wait for tens of seconds to get the complete subtitles. The style can be applied to the template with one click.

For creators of short videos and spoken-word videos, the greatest value of the subtitle function lies in the seamless connection of workflow. Converting subtitles, editing, dubbing, and adding special effects are all done in the same software, so there is no need to switch between multiple tools. The Chinese recognition effect has a good reputation in the circle of creators, and the accuracy of daily spoken broadcasts is high. Manual proofreading is required in professional terminology and dialect scenarios.

The subtitle function of the clip is free, which is very friendly to individual creators. It also supports exporting subtitles as SRT files. If you do not complete the final editing in the editing, you can take the subtitles to other tools and continue to use them.

The disadvantage is that it is designed for video editing scenes after all. If you only want a verbatim draft without editing the video, the process will appear redundant. In addition, long audio (such as a meeting of more than two hours) is not as efficient as a dedicated meeting recording tool.

Who it's suitable for: Short video bloggers, podcast editors, content creators, and anyone who already uses cutouts for editing.

7 Notta, a convenient choice for cross-platform online services

Notta is an online speech-to-text service that focuses on cross-platform and multi-language capabilities. It is available on the web, iOS, Android, and desktop applications, supporting multiple languages ​​such as Chinese, English, Japanese, and Korean. Its free version provides a certain amount of transcription time, which is enough for daily use, while the paid version unlocks longer time and more functions.

Notta is characterized by making the tool process relatively lightweight. You can upload files or record directly by opening a web page. After the transcription is completed, you can edit, mark, and generate summaries directly on the web page. It is specially optimized for meeting scenarios and supports synchronous transcription of meeting platforms such as Zoom and Google Meet, which is very practical in remote meetings of multinational teams.

For mixed Chinese and English content, Notta's processing is considered stable, and there will be no obvious language switching errors. The export format supports common types such as TXT, SRT, and PDF, and it is very convenient to transfer it to other tools for further processing.

The disadvantage is that the overall effect of Chinese recognition is slightly inferior to domestic services such as Tongyi Listening and Feishu Miaoji, which are deeply involved in the Chinese scene. More manual proofreading is needed in terms of professional terms and dialects. The free quota is also tighter than that of some domestic services.

Who it's suitable for: Bilingual users who often deal with mixed Chinese and English content, and people who frequently participate in cross-border online meetings.

8 Otter.ai, a veteran player in the English scene

Otter.ai is one of the established products in the field of English speech-to-text, and has high recognition in the European and American markets. Its English recognition accuracy is generally considered to be at a good level in the industry, and its support for meeting scenes, interviews, and podcasts is relatively mature.

Otter's functional strengths lie in real-time transcription and team collaboration. It can be connected to mainstream conference platforms for real-time subtitles, and the generated transcribed documents support multi-person collaborative editing, adding comments, and highlighting key paragraphs. For English-speaking teams, Otter has become a standard tool for many companies.

It also provides a free version, which has a certain monthly quota of transcription time, and the paid version further increases the time limit and advanced features. For people who occasionally need to process one or two English audios, the free version is completely sufficient.

The disadvantage is that Chinese support is very limited and is basically not suitable for Chinese-based users. The interface is only in English, so there is a certain threshold for users who have trouble reading English.

Who it’s suitable for: Users who produce English content, need to attend English conferences, or need to handle a large number of English podcasts and interviews.

9 Best Combination Recommendations for Chinese Videos

If your content is mainly in Chinese and you have high requirements for ease of use, the most direct choice is Tongyi Listening or Feishu Miaoji. Both are at a relatively good level in the industry in terms of Chinese recognition accuracy, and both come with additional functions such as summarization, segmentation, and keyword extraction. The entire process from recording to usable manuscripts is relatively smooth. If the team is already using Feishu for office work, Feishu Miaoji is almost a no-brainer; if it is an individual or non-Feishu team, Tongyi Listening is more worthy of recommendation due to its free quota and complete functionality.

If your content is a short video or oral broadcast, and you are already using clip editing, just use the subtitle function that comes with the clip. Its closed-loop workflow advantage is difficult to replace with other tools. For parts that require more precise control (such as verbatim transcripts of long interviews), general listening comprehension can be added.

If you are very privacy-conscious and don't want your audio to be uploaded to any third-party server, Whisper local deployment is almost the only solution. The threshold for local Whisper-based applications such as MacWhisper and Buzz is already very low, and even ordinary users can get started. The trade-off is that deployment and model selection require a little learning time.

A good combination strategy is: use Tongyi Listening or Feishu Miaoji for daily meetings and interviews, use clipping and subtitles for short video creation, and switch to Whisper local when sensitive content or multi-lingual requirements are involved. The three-piece set can basically cover all the needs in the Chinese scene.

10 Best Combination Recommendations for English Videos

In the English scene, Whisper is almost unavoidable. Its English recognition effect is recognized as a good level in the open source model in the industry, with high accuracy and support for various deployment methods. If you are willing to pay for the OpenAI official API, you can use it almost out of the box, eliminating the complexity of local deployment. If you have privacy needs, running Whisper locally is also a mature solution.

For conference scenarios, Otter.ai is still one of the most mainstream choices in the English circle. Its real-time subtitles, team collaboration, and integration with platforms such as Zoom are relatively mature and suitable for daily use by English-based companies.

For mixed Chinese and English content, Notta is an option worth considering. Its stability in bilingual scenarios is better than pure English tools. If you add a large language model such as ChatGPT or Claude for post-processing to further polish, segment and refine the key points of the transcribed manuscript, the output quality of the entire process will be improved by another level.

In short, the core combination of the English scene is Whisper plus Otter plus GPT model for post-processing, which can cover almost the entire process from transliteration to generating final content.

FAQ

How accurate is the AI ​​video-to-text tool?

There is no one-size-fits-all answer to this question. In the Chinese scenario, services from major manufacturers such as Tongyi Listening and Feishu Miaoji perform better under the conditions of standard Mandarin, quiet environment, and clear recording. Content with a slight accent or noisy background will have a certain decline. Professional terminology, industry slang, and names of people and places are common weaknesses of all tools and require manual proofreading. In the English scenario, Whisper is recognized by the industry as having better results in the open source model. Generally speaking, the accuracy of mainstream tools has reached a level where the cost of manual proofreading is acceptable, but publishing-level verbatim manuscripts still require manual inspection.

Can long videos, such as two-hour meeting recordings, be uploaded directly?

Most mainstream tools support long audio and video uploads, but the specific upper limit depends on the product and your account type. The free version generally has a maximum duration for a single file, and if it exceeds the limit, you need to slice or upgrade your subscription. There is no upper limit on how long you can run Whisper locally, it is only affected by computer performance. To process about two hours of recordings, online tools generally take a few minutes to more than ten minutes to produce results, while local runs range from a few minutes to an hour depending on the model size and device performance.

Can these tools differentiate between multiple speakers?

Some tools support speaker separation, such as Feishu Miaoji and Tongyi Tingwu, which can automatically label different speakers in multi-person conference scenarios. The original version of Whisper does not have speaker separation function and needs to be implemented by adding a third-party diarization tool. Otter.ai's speaker recognition in English scenarios is also relatively mature. If your core requirement is verbatim transcripts of interviews or multi-person meetings, it is recommended to give priority to tools with speaker separation functions, rather than using pure Whisper to assemble them.

Are there any privacy risks when uploading audio and video to these tools?

Any content uploaded to a third-party server has certain privacy risks. The compliance and data protection of Dachang services are usually better, but it still cannot be completely ruled out. For sensitive meetings, unpublished research materials, and interviews involving personal privacy, it is recommended to use locally deployed open source solutions such as Whisper. If you have to use online services, give priority to products with clear privacy policies, the ability to choose not to be used as training data, and promptly delete uploaded files after use.

Can the transferred text be used directly or does it need to be proofread?

Proofreading is required in most cases, but the workload of proofreading varies. For daily meeting verbatim drafts, personal notes, and short video subtitles, the transcription quality is generally sufficient, and you only need to quickly go through it to correct obvious errors. Externally released content, publications, and legal-related citations must be proofread verbatim. The strength of AI tools is to liberate people from mechanical typing work, rather than completely replacing the proofreading process. Developing the habit of quickly rereading the text after transcribing it is more reliable in the long run than pursuing 100% automation.