SpeechText.AI can use one of several machine learning models to transcribe audio files based on the original type of the audio. It provides multiple pre-built models, and you can improve the quality of speech recognition for various types of audio. If you specify the type of the original audio, this will allow the service to process your audio files using a machine learning model trained from data similar to your file.
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Based on our record, Every Noice at Once seems to be a lot more popular than SpeechText.ai. While we know about 422 links to Every Noice at Once, we've tracked only 2 mentions of SpeechText.ai. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
I see this in https://everynoise.com/#updates > 2024-01-05 status update: With my layoff from Spotify on 2023-12-04, I lost the internal data-access required for ongoing updates to many parts of this site. Most of this, as a result, is now a static snapshot of what, for now, will be the final state from the site's 10-year history and evolution, hosted on my own server. Some pieces may get disabled and reenabled... - Source: Hacker News / 2 months ago
Anyone aware of a similar feature for foobar2000? I have an extensive library mostly tagged from Discogs, including release IDs. In theory, this should be sufficient to cluster music by genres, pull similar releases from Discogs "similar" feature and correlate data from https://everynoise.com. Obviously, in case of album mixed genres things will mix up, but I'm not sure there's a model that can correlate existing... - Source: Hacker News / 3 months ago
The article mentions Glenn McDonald's musical genre page (https://everynoise.com/, no longer refreshing with new Spotify data) as an example of a flexible graph-like exploration format, without being burdened by explicit connections. The author also has a thorough description of pros and cons of the general concept. - Source: Hacker News / 6 months ago
This is from Glenn McDonald's blog, founder of "Every Noise at Once". He was laid off from Spotify (discussed here briefly [0]) --- https://everynoise.com/ is now in "archival copy" mode [1][2]. Super sad to read / see this. [0] https://news.ycombinator.com/item?id=38650917 [2] https://twitter.com/EveryNoise/status/1736086849339244935. - Source: Hacker News / 7 months ago
Data exported using: https://benjaminbenben.com/lastfm-to-csv/ Album art compiled using: https://www.neverendingchartrendering.org/ Genre data compiled using: http://organizeyourmusic.playlistmachinery.com/# https://everynoise.com/ https://www.tunemymusic.com/transfer Gender, year and country of origin information manually compiled using Last.fm and wikipedia. Data analysis done in excel and image created in GIMP. Source: 7 months ago
Healthcare is one of the biggest industries in the world. Doctors, nurses, and other health care practitioners are using technology to enhance their performance. Voice recognition technologies and medical speech recognition software have become essential for healthcare providers. This sector includes many companies developing highly efficient voice recognition tools. Source: over 2 years ago
I also tried speechtext.ai which honestly did a very good job and I'm happy with it, but pricing wise it's too much. Source: almost 3 years ago
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