Update README.md

README.md

@@ -35,6 +35,8 @@ tags:
 - whisper
 - voice
 - large-v3
+- ja
+- jp
 ---
 # Japanese Anime Speech Dataset
 
@@ -54,31 +56,33 @@ A list of all audio files and transcriptions are [**here**](https://huggingface.
 # Changelog
 
 * V1 - This version contains **16,143** audio-text pairs from the visual novel `IxSHE Tell`. Some cleaning of the transcriptions has been done to get rid of unwanted characters at the start and end of lines, but I intend to do much more for the second version.
-
 * V2 - The version contains **23,422** audio-text pairs from three different visual novels. Cleaning has been done to remove most nsfw lines, especially noises that aren't words. The audio is now in mp3 format, rather than wav. This version contains **32.6** hours of audio.
-
 * V3 - The version contains **38,325** audio-text pairs from five different visual novels. Thorough cleaning has been done to remove almost all nsfw or low-quality audio files. Transcriptions have been formatted to contain much fewer dramatised duplicated characters (for example 「ああああーーー」), and transcriptions have been made much more consistent (for example, using the same type of quotation mark). This version contains **52.5 hours** of audio.
-
-* **V4** - The dataset now contains **47,844** audio-text pairs from six different visual novels. Thorough cleaning has been done to remove almost all nsfw or low-quality audio files. Transcriptions have been formatted to contain much fewer dramatised duplicated characters (for example 「ああああーーー」). This version contains **63.4 hours** of audio.
+* V4 - The dataset contains **47,844** audio-text pairs from six different visual novels. Thorough cleaning has been done to remove almost all nsfw or low-quality audio files. This version contains **63.4 hours** of audio.
+* **V5** - The dataset contains **73,004** audio-text pairs from eight different visual novels. Thorough cleaning has been done to remove almost all nsfw or low-quality audio files. This version contains **63.4 hours** of audio. 
 
 # Dataset information
 
-* **47,844** audio-text pairs
-* **63.4 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).
-* **4.8s** average audio length.
-* Transcriptions have been scraped directly from the game files of **visual novels**.
+* Lastest version: **V5 - March 22nd 2024**
+* **73,004** audio-text pairs
+* **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).
+* **5.4s** average audio length
+* Transcriptions have been scraped directly from the game files of **visual novels**
 
 # To do
 
-* [X] Create a dataset of over 10k items
-* [X] Create a dataset of over 20k items
-* [X] Compress the audio with minimal quality loss
-* [X] Create a dataset of over 30k items
-* [X] Create a dataset of over 40k items
-* [ ] Create a dataset of over 50k items
-* [ ] Create more workflows for scraping audio from visual novels that use an engine other than Artemis
-* [ ] Convert names in transcriptions to katakana?
-* [ ] Scrape audio and transcriptions from YouTube
+- [X] Create a dataset of over 10k items
+- [X] Create a dataset of over 20k items
+- [X] Compress the audio with minimal quality loss
+- [X] Create a dataset of over 30k items
+- [X] Create a dataset of over 40k items
+- [X] Create a dataset of over 50k items
+- [ ] Create more workflows for scraping audio from visual novels that use an engine other than Artemis
+- [ ] Convert names in transcriptions to katakana?
+- [ ] Scrape audio and transcriptions from YouTube
+
+# Bias and Limitations
+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.
 
 # Use & Credit