10 shown

datasets

Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

01teticio /audio-diffusion-1024Over 20,000 256x256 mel spectrograms of 5 second samples of music from my Spotify liked playlist. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models. x_res = 1024 y_res = 1024 sample_rate = 44100 n_fft = 2048 hop_length = 512 imageimage-to-image10K<n<100K0 likes299 downloads4y agoHugging Face 02teticio /audio-diffusion-512Over 20,000 512x512 mel spectrograms of 5 second samples of music from my Spotify liked playlist. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models. x_res = 512 y_res = 512 sample_rate = 22050 n_fft = 2048 hop_length = 512 imageimage-to-image10K<n<100K2 likes141 downloads3y agoHugging Face 03crlandsc /tiny-audio-diffusion-drums Drum Samples Drum Samples used to train in tiny-audio-diffusion Checkpoints: Hihats: https://huggingface.co/crlandsc/tiny-audio-diffusion-hihats Kicks: https://huggingface.co/crlandsc/tiny-audio-diffusion-kicks Snares: https://huggingface.co/crlandsc/tiny-audio-diffusion-snares Percussion: https://huggingface.co/crlandsc/tiny-audio-diffusion-percussion audio1K<n<10K3 likes98 downloads2y agoHugging Face 04teticio /audio-diffusion-instrumental-hiphop-256256x256 mel spectrograms of 5 second samples of instrumental Hip Hop. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models. x_res = 256 y_res = 256 sample_rate = 22050 n_fft = 2048 hop_length = 512 imageimage-to-image10K<n<100K7 likes84 downloads4y agoHugging Face 05teticio /audio-diffusion-breaks-25630,000 256x256 mel spectrograms of 5 second samples that have been used in music, sourced from WhoSampled and YouTube. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models. x_res = 256 y_res = 256 sample_rate = 22050 n_fft = 2048 hop_length = 512 imageimage-to-image10K<n<100K2 likes77 downloads4y agoHugging Face 06teticio /audio-diffusion-256Over 20,000 256x256 mel spectrograms of 5 second samples of music from my Spotify liked playlist. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models. x_res = 256 y_res = 256 sample_rate = 22050 n_fft = 2048 hop_length = 512 imageimage-to-image10K<n<100K6 likes39 downloads4y agoHugging Face 07ChrisOpenSource /audio-diffusion-breaks-25630,000 256x256 mel spectrograms of 5 second samples that have been used in music, sourced from WhoSampled and YouTube. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models. x_res = 256 y_res = 256 sample_rate = 22050 n_fft = 2048 hop_length = 512 imageimage-to-image10K<n<100K0 likes23 downloads2mo agoHugging Face 08dirtyuncleleo /tiny-audio-diffusion-drums Drum Samples Drum Samples used to train in tiny-audio-diffusion Checkpoints: Hihats: https://huggingface.co/crlandsc/tiny-audio-diffusion-hihats Kicks: https://huggingface.co/crlandsc/tiny-audio-diffusion-kicks Snares: https://huggingface.co/crlandsc/tiny-audio-diffusion-snares Percussion: https://huggingface.co/crlandsc/tiny-audio-diffusion-percussion audio1K<n<10K0 likes20 downloads2mo agoHugging Face 09Diffusion-ASR /worst100-testclean-clips Worst-100 test-clean clips — audio, transcripts, and the vocabulary finding The 100 LibriSpeech test-clean clips where the block-4 production model (4.60% WER) made the most word errors — with audio embedded so the failures can be listened to, plus the model's transcript next to the reference for each clip. The finding this dataset produced 47% of the word errors in these clips are on words that never appeared in the 30-hour training vocabulary at all (20,066… See the full description on the dataset page: https://huggingface.co/datasets/Diffusion-ASR/worst100-testclean-clips.audion<1K0 likes17 downloads1mo agoHugging Face 10jaeikkim /AIDAS-Omni-Modal-Diffusion-assetsaudion<1K0 likes12 downloads7mo agoHugging Face

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