![]() ![]() “Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English and that accuracy on speech recognition and translation is near the state-of-the-art level,” OpenAI’s researchers explained on GitHub. And while OpenAI admits Whisper’s accuracy doesn’t always measure up to other models, the “robust” nature of its training puts it ahead in other And though the “robust” training enables Whisper to discern and transcribe speech through background noise and accent variations, it also creates new problems. While impressive, OpenAI’s research paper suggests that the ASR is really only that successful in about 10 languages, a limitation likely stemming from how two-thirds of the training data is in English. The Whisper speech to text model is multilingual and can even transcribe K-Pop: /PNY3Gs2kjP You can see an example in the Korean song translated and transcribed below. The AI can understand and transcribe many languages and translate any of them into English. The idea is that a broad approach to data collection improves Whisper’s ability to understand more speech because of the different accents, environmental noise, and subjects discussed. Whisper trained its ASR model on 680,000 hours of “multilingual and multitask” data pulled from the web. Whisper’s AI can transcribe speech in multiple languages and translate them into English, though the GPT-3 developer claims Whisper’s training makes it better at distinguishing voices in loud environments and parsing heavy accents and technical language. # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS INįrom pydub.OpenAI has introduced a new automatic speech recognition (ASR) system called Whisper as an open-source software kit on GitHub. # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # all copies or substantial portions of the Software. # The above copyright notice and this permission notice shall be included in # furnished to do so, subject to the following conditions: # copies of the Software, and to permit persons to whom the Software is # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell ![]() # in the Software without restriction, including without limitation the rights # of this software and associated documentation files (the "Software"), to deal # Permission is hereby granted, free of charge, to any person obtaining a copy In example, for a 10 min audio, transcript done with an estimated time of 06:05. I added a start/end check, so you can get an idea of how much time you need to spend processing an episode. Print("\r\nExporting >", out_file, " - ", i, "/", count)Īnd that’s it! Depending on your machine, this may take some time. Print("Audio split into " + str(count) + " audio chunks \n") Sound_file = om_mp3(podcast_audio_file)Īudio_chunks = split_on_silence(sound_file, min_silence_len=1000, silence_thresh=-40 ) ![]()
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