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| 1 | +<!--Copyright 2022 The HuggingFace Team. All rights reserved. |
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| 4 | +the License. You may obtain a copy of the License at |
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| 6 | +http://www.apache.org/licenses/LICENSE-2.0 |
| 7 | + |
| 8 | +Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on |
| 9 | +an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the |
| 10 | +specific language governing permissions and limitations under the License. |
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| 12 | + |
| 13 | +# Audio Diffusion |
| 14 | + |
| 15 | +## Overview |
| 16 | + |
| 17 | +[Audio Diffusion](https://github.com/teticio/audio-diffusion) by Robert Dargavel Smith. |
| 18 | + |
| 19 | +Audio Diffusion leverages the recent advances in image generation using diffusion models by converting audio samples to |
| 20 | +and from mel spectrogram images. |
| 21 | + |
| 22 | +The original codebase of this implementation can be found [here](https://github.com/teticio/audio-diffusion), including |
| 23 | +training scripts and example notebooks. |
| 24 | + |
| 25 | +## Available Pipelines: |
| 26 | + |
| 27 | +| Pipeline | Tasks | Colab |
| 28 | +|---|---|:---:| |
| 29 | +| [pipeline_audio_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py) | *Unconditional Audio Generation* | [](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/audio_diffusion_pipeline.ipynb) | |
| 30 | + |
| 31 | + |
| 32 | +## Examples: |
| 33 | + |
| 34 | +### Audio Diffusion |
| 35 | + |
| 36 | +```python |
| 37 | +import torch |
| 38 | +from IPython.display import Audio |
| 39 | +from diffusers import DiffusionPipeline |
| 40 | + |
| 41 | +device = "cuda" if torch.cuda.is_available() else "cpu" |
| 42 | +pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device) |
| 43 | + |
| 44 | +output = pipe() |
| 45 | +display(output.images[0]) |
| 46 | +display(Audio(output.audios[0], rate=mel.get_sample_rate())) |
| 47 | +``` |
| 48 | + |
| 49 | +### Latent Audio Diffusion |
| 50 | + |
| 51 | +```python |
| 52 | +import torch |
| 53 | +from IPython.display import Audio |
| 54 | +from diffusers import DiffusionPipeline |
| 55 | + |
| 56 | +device = "cuda" if torch.cuda.is_available() else "cpu" |
| 57 | +pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device) |
| 58 | + |
| 59 | +output = pipe() |
| 60 | +display(output.images[0]) |
| 61 | +display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) |
| 62 | +``` |
| 63 | + |
| 64 | +### Audio Diffusion with DDIM (faster) |
| 65 | + |
| 66 | +```python |
| 67 | +import torch |
| 68 | +from IPython.display import Audio |
| 69 | +from diffusers import DiffusionPipeline |
| 70 | + |
| 71 | +device = "cuda" if torch.cuda.is_available() else "cpu" |
| 72 | +pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256").to(device) |
| 73 | + |
| 74 | +output = pipe() |
| 75 | +display(output.images[0]) |
| 76 | +display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) |
| 77 | +``` |
| 78 | + |
| 79 | +### Variations, in-painting, out-painting etc. |
| 80 | + |
| 81 | +```python |
| 82 | +output = pipe( |
| 83 | + raw_audio=output.audios[0, 0], |
| 84 | + start_step=int(pipe.get_default_steps() / 2), |
| 85 | + mask_start_secs=1, |
| 86 | + mask_end_secs=1, |
| 87 | +) |
| 88 | +display(output.images[0]) |
| 89 | +display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) |
| 90 | +``` |
| 91 | + |
| 92 | +## AudioDiffusionPipeline |
| 93 | +[[autodoc]] AudioDiffusionPipeline |
| 94 | + - __call__ |
| 95 | + - encode |
| 96 | + - slerp |
| 97 | + |
| 98 | + |
| 99 | +## Mel |
| 100 | +[[autodoc]] Mel |
| 101 | + - audio_slice_to_image |
| 102 | + - image_to_audio |
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