Duplicate from or4cl3ai/SoundSlayerAI

Co-authored-by: Dustin Groves <or4cl3ai@users.noreply.huggingface.co>

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README.md

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+---
+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!

config.json

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+{
+"name": "SoundSlayerAI",
+"description": "An innovative project for music-related tasks utilizing pyannote-audio library",
+"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": "pyannote-audio",
+"metrics": \[
+"accuracy",
+"bertscore",
+"BLEU",
+"BLEURT",
+"brier_score",
+"character"
+\],
+"language": "English",
+"usage": \[
+"Install the required dependencies by running pip install pyannote-audio.",
+"Import the necessary modules from the 'pyannote.audio' package to access the desired functionalities.",
+"Load the audio data or use the provided datasets to perform tasks such as audio segmentation, speaker diarization, music transcription, and more.",
+"Apply the appropriate algorithms and models from the 'pyannote.audio' library to process and analyze the audio data.",
+"Evaluate the results using the specified metrics, such as accuracy, bertscore, BLEU, BLEURT, brier_score, and character.",
+"Iterate and refine your approach to achieve the desired outcomes for your music-related tasks."
+\],
+"license": "Openrail",
+"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": "\[or4cl3ai@gmail.com\]"
+}

zero-shot_generated_datasets

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+type: collective_task
+dataset_splits: ['train', 'dev']
+tasks:
+  - name: zero_shot_translation
+    pipeline_labels: [pypeline@tensorflow]
+    task_labels: [translate]
+    inputs:
+      - type: text
+        format: json
+        prompt: Free-form text, no formatting restrictions
+        expected_input_types: ["text"]
+        examples: {"en": "<UNSAFE>Hello world</UNSAFE>", "de": "<UNSAFE>Hallo Welt</UNSAFE>"}
+    outputs:
+      - type: text
+        format: json
+        prompt: Free-form text, no formatting restrictions
+        expected_output_types: ["text"]
+        examples: {"en": "<UNSAFE>I am a large language model.</UNSAFE>", "de": "<UNSAFE>Ich bin ein grosses Sprachmodell.</UNSAFE>"}
+    pipeline_params: {}
+  - name: text_to_speech
+    pipeline_labels: [pypeline@transformerxlsp]
+    task_labels: [tts]
+    inputs:
+      - type: text
+        format: json
+        prompt: Markdown, HTML, Unicode, or LaTeX, but avoid complex math notation
+        example: <INLINE(markdown)>title="Hello World!"<\\title><::post.body=\\\nThis \\sout{is} an *italicized* text post.*</UNORDEREDLIST></UNORDEREDLIST><POST>```bash
+          {<UNKNOWN system="user">...<}</UNKNOWN>`````.rst
+          <BLANKS/>
+      metadata: {'tags': '<MARQUEE><FONT COLOR="#FF0000"><B>ROCK MUSIC</B></FONT></MARQUEE>'|None<TAG>}
+        expected_input_types: ["text"]
+        examples: {EN: {"<UNSAFE><HTML><h1&gt;Hello, TTS Engine!
+It works!</h1&gt;</HTML&gt;">"}, DE: {"<UNSAFE><HTML><h1&gt;Hallo, Synthetische Stimme! Klar kommt hier das auf Deutsch auch klapp.<br /&gt;Wenn's geht gibt es ja bald mehr davon ...<hr /><span style='font-family:Monospace'>#OpenSource #Synthesizer</span>
+// Einige Werte kann ich noch nicht liefern da keine Implementierung vorliegt.</h1>"}}}, {"<HTML><HEAD>...</HEAD><BODY><P>&nbsp;</P>&nbsp;</BODY></HTML>:<MARQUEE><FONT COLOR="#FF0000"><B>JAZZ MUSIC</B></FONT></MARQUEE>{tag: 'jazz'}"/>}}`)}</INPUT>
+    outputs:
+      - type: audio
+        format: wav, opus, m4a
+        bitrate: 64kbps+
+        channel_count: 1
+        sample_rate: 22kHz+
+        rate: monophonic
+        pitch_range: 0.5-4 octaves
+        speed_range: +/- 5%
+        vibrato_depth: maximum of 3 semitones
+        dynamics_range: ppp-fff
+        silence_padding: >=8ms
+        prompt: Melodies, up to two verses per submission, please separate with commas. Monophony encouraged, unless improvisational techniques warrant chord progressions. Examples in EN, DE, ES, FR: {"en": "[0.7, 1, Eb4], 'Mary had a little lamb',[0.9, 1, Ab3,'Twinkle twinkle