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--- |
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license: openrail |
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datasets: |
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- Fhrozen/AudioSet2K22 |
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- Chr0my/Epidemic_sounds |
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- ChristophSchuhmann/lyrics-index |
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- Cropinky/rap_lyrics_english |
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- tsterbak/eurovision-lyrics-1956-2023 |
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- brunokreiner/genius-lyrics |
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- google/MusicCaps |
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- ccmusic-database/music_genre |
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- Hyeon2/riffusion-musiccaps-dataset |
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- SamAct/autotrain-data-musicprompt |
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- Chr0my/Epidemic_music |
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- juliensimon/autonlp-data-song-lyrics |
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- Datatang/North_American_English_Speech_Data_by_Mobile_Phone_and_PC |
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- Chr0my/freesound.org |
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- teticio/audio-diffusion-256 |
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- KELONMYOSA/dusha_emotion_audio |
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- Ar4ikov/iemocap_audio_text_splitted |
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- flexthink/ljspeech |
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- mozilla-foundation/common_voice_13_0 |
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- facebook/voxpopuli |
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- SocialGrep/one-million-reddit-jokes |
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- breadlicker45/human-midi-rlhf |
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- breadlicker45/midi-gpt-music-small |
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- projectlosangeles/Los-Angeles-MIDI-Dataset |
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- huggingartists/epic-rap-battles-of-history |
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- SocialGrep/one-million-reddit-confessions |
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- shahules786/prosocial-nsfw-reddit |
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- Thewillonline/reddit-sarcasm |
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- autoevaluate/autoeval-eval-futin__guess-vi-4200fb-2012366606 |
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- lmsys/chatbot_arena_conversations |
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- mozilla-foundation/common_voice_11_0 |
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- mozilla-foundation/common_voice_4_0 |
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- dell-research-harvard/AmericanStories |
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- zZWipeoutZz/insane_style |
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- mu-llama/MusicQA |
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- RaphaelOlivier/whisper_adversarial_examples |
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- huggingartists/metallica |
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- vldsavelyev/guitar_tab |
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- NLPCoreTeam/humaneval_ru |
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- seungheondoh/audioset-music |
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- gary109/onset-singing3_corpora_parliament_processed_MIR-ST500 |
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- LDD5522/Rock_Vocals |
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- huggingartists/rage-against-the-machine |
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- huggingartists/chester-bennington |
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- huggingartists/logic |
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- cmsolson75/artist_song_lyric_dataset |
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- BhavyaMuni/artist-lyrics |
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- vjain/emotional_intelligence |
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- mhenrichsen/context-aware-splits |
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metrics: |
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- accuracy |
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- bertscore |
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- bleu |
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- bleurt |
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- brier_score |
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- character |
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- chrf |
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language: |
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- en |
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- es |
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- it |
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- pt |
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- la |
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- fr |
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- ru |
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- zh |
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- ja |
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- el |
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library_name: transformers |
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tags: |
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- music |
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pipeline_tag: text-to-speech |
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--- |
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# SoundSlayerAI |
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SoundSlayerAI is an innovative project that focuses on music-related tasks This project aims to provide various functionalities for audio analysis and processing, making it easier to work with music datasets. |
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## Datasets |
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SoundSlayerAI makes use of the following datasets: |
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- Fhrozen/AudioSet2K22 |
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- Chr0my/Epidemic_sounds |
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- ChristophSchuhmann/lyrics-index |
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- Cropinky/rap_lyrics_english |
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- tsterbak/eurovision-lyrics-1956-2023 |
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- brunokreiner/genius-lyrics |
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- google/MusicCaps |
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- ccmusic-database/music_genre |
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- Hyeon2/riffusion-musiccaps-dataset |
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- SamAct/autotrain-data-musicprompt |
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- Chr0my/Epidemic_music |
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- juliensimon/autonlp-data-song-lyrics |
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- Datatang/North_American_English_Speech_Data_by_Mobile_Phone_and_PC |
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- Chr0my/freesound.org |
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- teticio/audio-diffusion-256 |
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- KELONMYOSA/dusha_emotion_audio |
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- Ar4ikov/iemocap_audio_text_splitted |
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- flexthink/ljspeech |
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- mozilla-foundation/common_voice_13_0 |
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- facebook/voxpopuli |
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- SocialGrep/one-million-reddit-jokes |
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- breadlicker45/human-midi-rlhf |
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- breadlicker45/midi-gpt-music-small |
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- projectlosangeles/Los-Angeles-MIDI-Dataset |
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- huggingartists/epic-rap-battles-of-history |
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- SocialGrep/one-million-reddit-confessions |
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- shahules786/prosocial-nsfw-reddit |
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- Thewillonline/reddit-sarcasm |
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- autoevaluate/autoeval-eval-futin__guess-vi-4200fb-2012366606 |
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- lmsys/chatbot_arena_conversations |
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- mozilla-foundation/common_voice_11_0 |
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- mozilla-foundation/common_voice_4_0 |
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## Library |
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The core library used in this project is "pyannote-audio." This library provides a wide range of functionalities for audio analysis and processing, making it an excellent choice for working with music datasets. The "pyannote-audio" library offers a comprehensive set of tools and algorithms for tasks such as audio segmentation, speaker diarization, music transcription, and more. |
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## Metrics |
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To evaluate the performance of SoundSlayerAI, several metrics are employed, including: |
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- Accuracy |
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- Bertscore |
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- BLEU |
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- BLEURT |
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- Brier Score |
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- Character |
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These metrics help assess the effectiveness and accuracy of the implemented algorithms and models. |
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## Language |
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The SoundSlayerAI project primarily focuses on the English language. The datasets and models used in this project are optimized for English audio and text analysis tasks. |
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## Usage |
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To use SoundSlayerAI, follow these steps: |
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1. Install the required dependencies by running `pip install pyannote-audio`. |
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2. Import the necessary modules from the "pyannote.audio" package to access the desired functionalities. |
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3. Load the audio data or use the provided datasets to perform tasks such as audio segmentation, speaker diarization, music transcription, and more. |
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4. Apply the appropriate algorithms and models from the "pyannote.audio" library to process and analyze the audio data. |
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5. Evaluate the results using the specified metrics, such as accuracy, bertscore, BLEU, BLEURT, brier_score, and character. |
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6. Iterate and refine your approach to achieve the desired outcomes for your music-related tasks. |
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## License |
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SoundSlayerAI is released under the Openrail license. Please refer to the LICENSE file for more details. |
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## Contributions |
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Contributions to SoundSlayerAI are welcome! If you have any ideas, bug fixes, or enhancements, feel free to submit a pull request or open an issue on the GitHub repository. |
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## Contact |
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For any inquiries or questions regarding SoundSlayerAI, please reach out to the project maintainer at [insert email address]. |
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Thank you for your interest in SoundSlayerAI! |
