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