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@@ -57,7 +57,7 @@ A list of all audio files and transcriptions can be found [**here**](https://hug
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  * **73,004** audio-text pairs
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  * **110 hours** of audio (OpenAI suggests a minimum of [5 hours](https://huggingface.co/blog/fine-tune-whisper) for productive [Whisper](https://huggingface.co/openai/whisper-large-v2) fine-tuning).
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  * **5.4s** average audio length
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- * Transcriptions have been scraped directly from the game files of **visual novels**
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  * Lastest version: **V5 - March 22nd 2024**
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  # Changelog
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  * **V5** - The dataset contains **73,004** audio-text pairs from eight different visual novels. Thorough cleaning has been done to remove most nsfw or low-quality audio files. This version contains **110 hours** of audio.
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  # Bias and Limitations
 
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  This dataset, while valuable for training anime-style Japanese speech recognition, has some inherent biases and limitations. The audio is primarily sourced from visual novels, leading to a gender bias towards female voices and a domain-specific vocabulary revolving around topics such as love, relationships, and fantasy. Additionally, the professionally produced nature of the audio results in clear and slow speech, which may not fully reflect real-world speaking patterns.
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  # Use & Credit
 
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  * **73,004** audio-text pairs
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  * **110 hours** of audio (OpenAI suggests a minimum of [5 hours](https://huggingface.co/blog/fine-tune-whisper) for productive [Whisper](https://huggingface.co/openai/whisper-large-v2) fine-tuning).
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  * **5.4s** average audio length
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+ * Audio source: **visual novels**
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  * Lastest version: **V5 - March 22nd 2024**
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  # Changelog
 
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  * **V5** - The dataset contains **73,004** audio-text pairs from eight different visual novels. Thorough cleaning has been done to remove most nsfw or low-quality audio files. This version contains **110 hours** of audio.
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  # Bias and Limitations
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+
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  This dataset, while valuable for training anime-style Japanese speech recognition, has some inherent biases and limitations. The audio is primarily sourced from visual novels, leading to a gender bias towards female voices and a domain-specific vocabulary revolving around topics such as love, relationships, and fantasy. Additionally, the professionally produced nature of the audio results in clear and slow speech, which may not fully reflect real-world speaking patterns.
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  # Use & Credit