Ultra-high precision domestic speech recognition AI 'ReazonSpeech' was released free of charge, so I tried using the transcription function
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Tokyo-based technology company 'Raazon Holdings' has released ' ReazonSpeech ', one of the largest Japanese speech corpus in Japan with 19,000 hours, for free. At the same time, a transcription service that appeals to performance comparable to the ultra-high-performance speech recognition AI '
Free release of 'ReazonSpeech', a purely domestic Japanese speech recognition model that can be used commercially with ultra-high accuracy - Reason Human Interaction Lab
https://research.reason.jp/news/reasonspeech.html
ReasonSpeech - Reason Human Interaction Lab
https://research.reason.jp/projects/ReasonSpeech/
As a product group of 'ReazonSpeech', Raazon Holdings has a ' ReazonSpeech speech recognition model ' that can be used for transcription, a ' ReazonSpeech corpus creation tool ' that can automatically extract a speech corpus from recorded TV data, etc., a total of 19,000 hours. Three types of high-quality Japanese speech recognition model training corpus ' ReazonSpeech speech corpus ' have been released free of charge. Among them, 'ReazonSpeech speech recognition model' has achieved accuracy comparable to Whisper. Raizon Holdings also released a transcription service using the 'ReazonSpeech speech recognition model' at the same time, so I actually checked the transcription accuracy.
First, access the demo page of the transcription service and click 'Try speech recognition'.
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When a pop-up requesting permission to use the microphone appears on the upper left of the screen, tap 'Allow'.
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Then, 'Recording (5 seconds)' is displayed, so read the sentence you want to transcribe for 5 seconds.
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Speech recognition ends automatically in 5 seconds, and the transcription result is displayed on the right side of the screen. As a result of reading the beginning of 'I am a
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Since the demo page can only transcribe for a maximum of 5 seconds, I will try to transcribe a long sentence using a notebook published on Google's Python execution environment 'Google Colab'. First, click 'Open in Colab' while logged in to Google.
After the notebook opens, click Copy to Drive.
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When copying is completed, click the play button in the part marked 'ESPnet2 installation'.
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Wait for a few minutes and OK when a green check mark is displayed.
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Next, while logged in to Hugging Face, access the ReasonSpeech
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Next, access Hugging Face's
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When the token creation screen is displayed, enter a name of your choice and click 'Generate a token'.
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It is OK if you can create a token like this.
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Return to the Google Colab screen and click the play button on the part marked 'from huggingface_hub Import notebook_login'.
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When the following screen is displayed, enter the created token and click 'Login'.
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Then click the play button in the part marked 'Import torch'.
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Scroll down when you see a green check mark.
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Click the play button in the part marked 'Voice Recognition'.
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When the green check mark is displayed, click the play button of the part marked 'Recording'.
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Preparations are complete when a green check mark appears.
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Scroll to the bottom until you reach the cell marked 'seconds: 5'. By rewriting this 'seconds: 5' part, you can specify the number of seconds for transcription recognition.
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As a trial, I specified the number of seconds for recognition to be 30 seconds and clicked the play button.
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When asked for permission to use the microphone, click 'Allow'.
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The screen will display 'Please talk into the microphone for 30 seconds', so continue talking for 30 seconds.
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After speaking for 30 seconds, wait for a while, and the transcription result will be displayed at the bottom of the screen. However, despite reading the opening part of ``I am a cat'', ``I am looking at whether it is not yet possible to say that this is what I am, but it is not like this. I think he said that he didn't just say that he was doing it, but I think he said that he wasn't doing it, but I wonder what he meant after this. It was there, so there was nothing to worry about.'
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I thought that the reason why the transcription didn't work properly was that the number of seconds for recognition was lengthened . I can't get it, so I remember crying when I was about to be bullied. ' I got a relatively decent transcription result. At the time of writing the article, the demo version of ReasonSpeech does not seem to recognize sentence breaks well.
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in Review, Software, Web Application, Posted by log1o_hf