audio_diffusion_fork

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,13 +1,163 @@
 ---
-title: Audio-diffusion Style Transfer
-emoji: 🏃
-colorFrom: indigo
-colorTo: pink
+title: Audio Diffusion
+emoji: 🎵
+colorFrom: pink
+colorTo: blue
 sdk: gradio
-sdk_version: 3.9
+sdk_version: 3.1.4
 app_file: app.py
 pinned: false
 license: gpl-3.0
 ---
+# audio-diffusion [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/gradio_app.ipynb)
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+### Apply diffusion models to synthesize music instead of images using the new Hugging Face [diffusers](https://github.com/huggingface/diffusers) package.
+
+---
+
+**UPDATES**:
+
+**22/10/2022**. Added DDIM encoder and ability to interpolate between audios in latent "noise" space. Mel spectrograms no longer have to be square (thanks to Tristan for this one), so you can set the vertical (frequency) and horizontal (time) resolutions independently.
+
+**15/10/2022**. Added latent audio diffusion (see below). Also added the possibility to train a DDIM ([De-noising Diffusion Implicit Models](https://arxiv.org/pdf/2010.02502.pdf)). These have the benefit that samples can be generated with much fewer steps (~50) than used in training.
+
+**4/10/2022**. It is now possible to mask parts of the input audio during generation which means you can stitch several samples together (think "out-painting").
+
+**27/9/2022**. You can now generate an audio based on a previous one. You can use this to generate variations of the same audio or even to "remix" a track (via a sort of "style transfer"). You can find examples of how to do this in the [`test_model.ipynb`](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/test_model.ipynb) notebook.
+
+---
+
+![mel spectrogram](mel.png)
+
+---
+
+## DDPM ([De-noising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239))
+
+Audio can be represented as images by transforming to a [mel spectrogram](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum), such as the one shown above. The class `Mel` in `mel.py` can convert a slice of audio into a mel spectrogram of `x_res` x `y_res` and vice versa. The higher the resolution, the less audio information will be lost. You can see how this works in the [`test_mel.ipynb`](https://github.com/teticio/audio-diffusion/blob/main/notebooks/test_mel.ipynb) notebook.
+
+A DDPM is trained on a set of mel spectrograms that have been generated from a directory of audio files. It is then used to synthesize similar mel spectrograms, which are then converted back into audio.
+
+You can play around with some pre-trained models on [Google Colab](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/test_model.ipynb) or [Hugging Face spaces](https://huggingface.co/spaces/teticio/audio-diffusion). Check out some automatically generated loops [here](https://soundcloud.com/teticio2/sets/audio-diffusion-loops).
+
+
+| Model | Dataset | Description |
+|-------|---------|-------------|
+| [teticio/audio-diffusion-256](https://huggingface.co/teticio/audio-diffusion-256) | [teticio/audio-diffusion-256](https://huggingface.co/datasets/teticio/audio-diffusion-256) | My "liked" Spotify playlist |
+| [teticio/audio-diffusion-breaks-256](https://huggingface.co/teticio/audio-diffusion-breaks-256) | [teticio/audio-diffusion-breaks-256](https://huggingface.co/datasets/teticio/audio-diffusion-breaks-256) | Samples that have been used in music, sourced from [WhoSampled](https://whosampled.com) and [YouTube](https://youtube.com) |
+| [teticio/audio-diffusion-instrumental-hiphop-256](https://huggingface.co/teticio/audio-diffusion-instrumental-hiphop-256) | [teticio/audio-diffusion-instrumental-hiphop-256](https://huggingface.co/datasets/teticio/audio-diffusion-instrumental-hiphop-256) | Instrumental Hip Hop music |
+
+---
+
+## Generate Mel spectrogram dataset from directory of audio files
+#### Install
+```bash
+pip install .
+```
+
+#### Training can be run with Mel spectrograms of resolution 64x64 on a single commercial grade GPU (e.g. RTX 2080 Ti). The `hop_length` should be set to 1024 for better results.
+```bash
+python scripts/audio_to_images.py \
+  --resolution 64,64 \
+  --hop_length 1024 \
+  --input_dir path-to-audio-files \
+  --output_dir path-to-output-data
+```
+
+#### Generate dataset of 256x256 Mel spectrograms and push to hub (you will need to be authenticated with `huggingface-cli login`).
+```bash
+python scripts/audio_to_images.py \
+  --resolution 256 \
+  --input_dir path-to-audio-files \
+  --output_dir data/audio-diffusion-256 \
+  --push_to_hub teticio/audio-diffusion-256
+```
+  
+## Train model
+#### Run training on local machine.
+```bash
+accelerate launch --config_file config/accelerate_local.yaml \
+  scripts/train_unconditional.py \
+  --dataset_name data/audio-diffusion-64 \
+  --hop_length 1024 \
+  --output_dir models/ddpm-ema-audio-64 \
+  --train_batch_size 16 \
+  --num_epochs 100 \
+  --gradient_accumulation_steps 1 \
+  --learning_rate 1e-4 \
+  --lr_warmup_steps 500 \
+  --mixed_precision no
+```
+
+#### Run training on local machine with `batch_size` of 2 and `gradient_accumulation_steps` 8 to compensate, so that 256x256 resolution model fits on commercial grade GPU and push to hub.
+```bash
+accelerate launch --config_file config/accelerate_local.yaml \
+  scripts/train_unconditional.py \
+  --dataset_name teticio/audio-diffusion-256 \
+  --output_dir models/audio-diffusion-256 \
+  --num_epochs 100 \
+  --train_batch_size 2 \
+  --eval_batch_size 2 \
+  --gradient_accumulation_steps 8 \
+  --learning_rate 1e-4 \
+  --lr_warmup_steps 500 \
+  --mixed_precision no \
+  --push_to_hub True \
+  --hub_model_id audio-diffusion-256 \
+  --hub_token $(cat $HOME/.huggingface/token)
+```
+
+#### Run training on SageMaker.
+```bash
+accelerate launch --config_file config/accelerate_sagemaker.yaml \
+  scripts/train_unconditional.py \
+  --dataset_name teticio/audio-diffusion-256 \
+  --output_dir models/ddpm-ema-audio-256 \
+  --train_batch_size 16 \
+  --num_epochs 100 \
+  --gradient_accumulation_steps 1 \
+  --learning_rate 1e-4 \
+  --lr_warmup_steps 500 \
+  --mixed_precision no
+```
+
+## DDIM ([De-noising Diffusion Implicit Models](https://arxiv.org/pdf/2010.02502.pdf))
+#### A DDIM can be trained by adding the parameter
+```bash
+  --scheduler ddim
+```
+
+Inference can the be run with far fewer steps than the number used for training (e.g., ~50), allowing for much faster generation. Without retraining, the parameter `eta` can be used to replicate a DDPM if it is set to 1 or a DDIM if it is set to 0, with all values in between being valid. When `eta` is 0 (the default value), the de-noising procedure is deterministic, which means that it can be run in reverse as a kind of encoder that recovers the original noise used in generation. A function `encode` has been added to `AudioDiffusionPipeline` for this purpose. It is then possible to interpolate between audios in the latent "noise" space using the function `slerp` (Spherical Linear intERPolation).
+
+## Latent Audio Diffusion
+Rather than de-noising images directly, it is interesting to work in the "latent space" after first encoding images using an autoencoder. This has a number of advantages. Firstly, the information in the images is compressed into a latent space of a much lower dimension, so it is much faster to train de-noising diffusion models and run inference with them. Secondly, similar images tend to be clustered together and interpolating between two images in latent space can produce meaningful combinations.
+
+At the time of writing, the Hugging Face `diffusers` library is geared towards inference and lacking in training functionality (rather like its cousin `transformers` in the early days of development). In order to train a VAE (Variational AutoEncoder), I use the [stable-diffusion](https://github.com/CompVis/stable-diffusion) repo from CompVis and convert the checkpoints to `diffusers` format. Note that it uses a perceptual loss function for images; it would be nice to try a perceptual *audio* loss function.
+
+#### Train latent diffusion model using pre-trained VAE.
+```bash
+accelerate launch ...
+  ...
+  --vae teticio/latent-audio-diffusion-256
+```
+
+#### Install dependencies to train with Stable Diffusion.
+```
+pip install omegaconf pytorch_lightning
+pip install -e git+https://github.com/CompVis/stable-diffusion.git@main#egg=latent-diffusion
+pip install -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
+```
+
+#### Train an autoencoder.
+```bash
+python scripts/train_vae.py \
+  --dataset_name teticio/audio-diffusion-256 \
+  --batch_size 2 \
+  --gradient_accumulation_steps 12
+```
+
+#### Train latent diffusion model.
+```bash
+accelerate launch ...
+  ...
+  --vae models/autoencoder-kl
+```

app.py

@@ -0,0 +1,46 @@
+import argparse
+
+import gradio as gr
+
+from audiodiffusion import AudioDiffusion
+
+
+def generate_spectrogram_audio_and_loop(model_id):
+    audio_diffusion = AudioDiffusion(model_id=model_id)
+    image, (sample_rate,
+            audio) = audio_diffusion.generate_spectrogram_and_audio()
+    loop = AudioDiffusion.loop_it(audio, sample_rate)
+    if loop is None:
+        loop = audio
+    return image, (sample_rate, audio), (sample_rate, loop)
+
+
+demo = gr.Interface(fn=generate_spectrogram_audio_and_loop,
+                    title="Audio Diffusion",
+                    description="Generate audio using Huggingface diffusers.\
+        This takes about 20 minutes without a GPU, so why not make yourself a \
+            cup of tea in the meantime? (Or try the teticio/audio-diffusion-ddim-256 \
+                model which is faster.)",
+                    inputs=[
+                        gr.Dropdown(label="Model",
+                                    choices=[
+                                        "teticio/audio-diffusion-256",
+                                        "teticio/audio-diffusion-breaks-256",
+                                        "teticio/audio-diffusion-instrumental-hiphop-256",
+                                        "teticio/audio-diffusion-ddim-256"
+                                    ],
+                                    value="teticio/audio-diffusion-256")
+                    ],
+                    outputs=[
+                        gr.Image(label="Mel spectrogram", image_mode="L"),
+                        gr.Audio(label="Audio"),
+                        gr.Audio(label="Loop"),
+                    ],
+                    allow_flagging="never")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--port", type=int)
+    parser.add_argument("--server", type=int)
+    args = parser.parse_args()
+    demo.launch(server_name=args.server or "0.0.0.0", server_port=args.port)

audiodiffusion/__init__.py

@@ -0,0 +1,369 @@
+from math import acos, sin
+from typing import Iterable, Tuple, Union, List
+
+import torch
+import numpy as np
+from PIL import Image
+from tqdm.auto import tqdm
+from librosa.beat import beat_track
+from diffusers import (DiffusionPipeline, UNet2DConditionModel, DDIMScheduler,
+                       DDPMScheduler, AutoencoderKL)
+
+from .mel import Mel
+
+VERSION = "1.2.5"
+
+
+class AudioDiffusion:
+
+    def __init__(self,
+                 model_id: str = "teticio/audio-diffusion-256",
+                 sample_rate: int = 22050,
+                 n_fft: int = 2048,
+                 hop_length: int = 512,
+                 top_db: int = 80,
+                 cuda: bool = torch.cuda.is_available(),
+                 progress_bar: Iterable = tqdm):
+        """Class for generating audio using De-noising Diffusion Probabilistic Models.
+
+        Args:
+            model_id (String): name of model (local directory or Hugging Face Hub)
+            sample_rate (int): sample rate of audio
+            n_fft (int): number of Fast Fourier Transforms
+            hop_length (int): hop length (a higher number is recommended for lower than 256 y_res)
+            top_db (int): loudest in decibels
+            cuda (bool): use CUDA?
+            progress_bar (iterable): iterable callback for progress updates or None
+        """
+        self.model_id = model_id
+        pipeline = {
+            'LatentAudioDiffusionPipeline': LatentAudioDiffusionPipeline,
+            'AudioDiffusionPipeline': AudioDiffusionPipeline
+        }.get(
+            DiffusionPipeline.get_config_dict(self.model_id)['_class_name'],
+            AudioDiffusionPipeline)
+        self.pipe = pipeline.from_pretrained(self.model_id)
+        if cuda:
+            self.pipe.to("cuda")
+        self.progress_bar = progress_bar or (lambda _: _)
+
+        # For backwards compatibility
+        sample_size = (self.pipe.unet.sample_size,
+                       self.pipe.unet.sample_size) if type(
+                           self.pipe.unet.sample_size
+                       ) == int else self.pipe.unet.sample_size
+        self.mel = Mel(x_res=sample_size[1],
+                       y_res=sample_size[0],
+                       sample_rate=sample_rate,
+                       n_fft=n_fft,
+                       hop_length=hop_length,
+                       top_db=top_db)
+
+    def generate_spectrogram_and_audio(
+        self,
+        steps: int = None,
+        generator: torch.Generator = None,
+        step_generator: torch.Generator = None,
+        eta: float = 0,
+        noise: torch.Tensor = None
+    ) -> Tuple[Image.Image, Tuple[int, np.ndarray]]:
+        """Generate random mel spectrogram and convert to audio.
+
+        Args:
+            steps (int): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
+            generator (torch.Generator): random number generator or None
+            step_generator (torch.Generator): random number generator used to de-noise or None
+            eta (float): parameter between 0 and 1 used with DDIM scheduler
+            noise (torch.Tensor): noisy image or None
+
+        Returns:
+            PIL Image: mel spectrogram
+            (float, np.ndarray): sample rate and raw audio
+        """
+        images, (sample_rate,
+                 audios) = self.pipe(mel=self.mel,
+                                     batch_size=1,
+                                     steps=steps,
+                                     generator=generator,
+                                     step_generator=step_generator,
+                                     eta=eta,
+                                     noise=noise)
+        return images[0], (sample_rate, audios[0])
+
+    def generate_spectrogram_and_audio_from_audio(
+        self,
+        audio_file: str = None,
+        raw_audio: np.ndarray = None,
+        slice: int = 0,
+        start_step: int = 0,
+        steps: int = None,
+        generator: torch.Generator = None,
+        mask_start_secs: float = 0,
+        mask_end_secs: float = 0,
+        step_generator: torch.Generator = None,
+        eta: float = 0,
+        noise: torch.Tensor = None
+    ) -> Tuple[Image.Image, Tuple[int, np.ndarray]]:
+        """Generate random mel spectrogram from audio input and convert to audio.
+
+        Args:
+            audio_file (str): must be a file on disk due to Librosa limitation or
+            raw_audio (np.ndarray): audio as numpy array
+            slice (int): slice number of audio to convert
+            start_step (int): step to start from
+            steps (int): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
+            generator (torch.Generator): random number generator or None
+            mask_start_secs (float): number of seconds of audio to mask (not generate) at start
+            mask_end_secs (float): number of seconds of audio to mask (not generate) at end
+            step_generator (torch.Generator): random number generator used to de-noise or None
+            eta (float): parameter between 0 and 1 used with DDIM scheduler
+            noise (torch.Tensor): noisy image or None
+
+        Returns:
+            PIL Image: mel spectrogram
+            (float, np.ndarray): sample rate and raw audio
+        """
+
+        images, (sample_rate,
+                 audios) = self.pipe(mel=self.mel,
+                                     batch_size=1,
+                                     audio_file=audio_file,
+                                     raw_audio=raw_audio,
+                                     slice=slice,
+                                     start_step=start_step,
+                                     steps=steps,
+                                     generator=generator,
+                                     mask_start_secs=mask_start_secs,
+                                     mask_end_secs=mask_end_secs,
+                                     step_generator=step_generator,
+                                     eta=eta,
+                                     noise=noise)
+        return images[0], (sample_rate, audios[0])
+
+    @staticmethod
+    def loop_it(audio: np.ndarray,
+                sample_rate: int,
+                loops: int = 12) -> np.ndarray:
+        """Loop audio
+
+        Args:
+            audio (np.ndarray): audio as numpy array
+            sample_rate (int): sample rate of audio
+            loops (int): number of times to loop
+
+        Returns:
+            (float, np.ndarray): sample rate and raw audio or None
+        """
+        _, beats = beat_track(y=audio, sr=sample_rate, units='samples')
+        for beats_in_bar in [16, 12, 8, 4]:
+            if len(beats) > beats_in_bar:
+                return np.tile(audio[beats[0]:beats[beats_in_bar]], loops)
+        return None
+
+
+class AudioDiffusionPipeline(DiffusionPipeline):
+
+    def __init__(self, unet: UNet2DConditionModel,
+                 scheduler: Union[DDIMScheduler, DDPMScheduler]):
+        super().__init__()
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        mel: Mel,
+        batch_size: int = 1,
+        audio_file: str = None,
+        raw_audio: np.ndarray = None,
+        slice: int = 0,
+        start_step: int = 0,
+        steps: int = None,
+        generator: torch.Generator = None,
+        mask_start_secs: float = 0,
+        mask_end_secs: float = 0,
+        step_generator: torch.Generator = None,
+        eta: float = 0,
+        noise: torch.Tensor = None
+    ) -> Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]]:
+        """Generate random mel spectrogram from audio input and convert to audio.
+
+        Args:
+            mel (Mel): instance of Mel class to perform image <-> audio
+            batch_size (int): number of samples to generate
+            audio_file (str): must be a file on disk due to Librosa limitation or
+            raw_audio (np.ndarray): audio as numpy array
+            slice (int): slice number of audio to convert
+            start_step (int): step to start from
+            steps (int): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
+            generator (torch.Generator): random number generator or None
+            mask_start_secs (float): number of seconds of audio to mask (not generate) at start
+            mask_end_secs (float): number of seconds of audio to mask (not generate) at end
+            step_generator (torch.Generator): random number generator used to de-noise or None
+            eta (float): parameter between 0 and 1 used with DDIM scheduler
+            noise (torch.Tensor): noise tensor of shape (batch_size, 1, height, width) or None
+
+        Returns:
+            List[PIL Image]: mel spectrograms
+            (float, List[np.ndarray]): sample rate and raw audios
+        """
+
+        steps = steps or 50 if isinstance(self.scheduler,
+                                          DDIMScheduler) else 1000
+        self.scheduler.set_timesteps(steps)
+        step_generator = step_generator or generator
+        # For backwards compatibility
+        if type(self.unet.sample_size) == int:
+            self.unet.sample_size = (self.unet.sample_size,
+                                     self.unet.sample_size)
+        if noise is None:
+            noise = torch.randn(
+                (batch_size, self.unet.in_channels, self.unet.sample_size[0],
+                 self.unet.sample_size[1]),
+                generator=generator)
+        images = noise
+        mask = None
+
+        if audio_file is not None or raw_audio is not None:
+            mel.load_audio(audio_file, raw_audio)
+            input_image = mel.audio_slice_to_image(slice)
+            input_image = np.frombuffer(input_image.tobytes(),
+                                        dtype="uint8").reshape(
+                                            (input_image.height,
+                                             input_image.width))
+            input_image = ((input_image / 255) * 2 - 1)
+            input_images = np.tile(input_image, (batch_size, 1, 1, 1))
+
+            if hasattr(self, 'vqvae'):
+                input_images = self.vqvae.encode(
+                    input_images).latent_dist.sample(generator=generator)
+                input_images = 0.18215 * input_images
+
+            if start_step > 0:
+                images[0, 0] = self.scheduler.add_noise(
+                    torch.tensor(input_images[:, np.newaxis, np.newaxis, :]),
+                    noise, torch.tensor(steps - start_step))
+
+            pixels_per_second = (self.unet.sample_size[1] *
+                                 mel.get_sample_rate() / mel.x_res /
+                                 mel.hop_length)
+            mask_start = int(mask_start_secs * pixels_per_second)
+            mask_end = int(mask_end_secs * pixels_per_second)
+            mask = self.scheduler.add_noise(
+                torch.tensor(input_images[:, np.newaxis, :]), noise,
+                torch.tensor(self.scheduler.timesteps[start_step:]))
+
+        images = images.to(self.device)
+        for step, t in enumerate(
+                self.progress_bar(self.scheduler.timesteps[start_step:])):
+            model_output = self.unet(images, t)['sample']
+
+            if isinstance(self.scheduler, DDIMScheduler):
+                images = self.scheduler.step(
+                    model_output=model_output,
+                    timestep=t,
+                    sample=images,
+                    eta=eta,
+                    generator=step_generator)['prev_sample']
+            else:
+                images = self.scheduler.step(
+                    model_output=model_output,
+                    timestep=t,
+                    sample=images,
+                    generator=step_generator)['prev_sample']
+
+            if mask is not None:
+                if mask_start > 0:
+                    images[:, :, :, :mask_start] = mask[
+                        step, :, :, :, :mask_start]
+                if mask_end > 0:
+                    images[:, :, :, -mask_end:] = mask[step, :, :, :,
+                                                       -mask_end:]
+
+        if hasattr(self, 'vqvae'):
+            # 0.18215 was scaling factor used in training to ensure unit variance
+            images = 1 / 0.18215 * images
+            images = self.vqvae.decode(images)['sample']
+
+        images = (images / 2 + 0.5).clamp(0, 1)
+        images = images.cpu().permute(0, 2, 3, 1).numpy()
+        images = (images * 255).round().astype("uint8")
+        images = list(
+            map(lambda _: Image.fromarray(_[:, :, 0]), images) if images.
+            shape[3] == 1 else map(
+                lambda _: Image.fromarray(_, mode='RGB').convert('L'), images))
+
+        audios = list(map(lambda _: mel.image_to_audio(_), images))
+        return images, (mel.get_sample_rate(), audios)
+
+    @torch.no_grad()
+    def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
+        """Reverse step process: recover noisy image from generated image.
+
+        Args:
+            images (List[PIL Image]): list of images to encode
+            steps (int): number of encoding steps to perform (defaults to 50)
+
+        Returns:
+            np.ndarray: noise tensor of shape (batch_size, 1, height, width)
+        """
+
+        # Only works with DDIM as this method is deterministic
+        assert isinstance(self.scheduler, DDIMScheduler)
+        self.scheduler.set_timesteps(steps)
+        sample = np.array([
+            np.frombuffer(image.tobytes(), dtype="uint8").reshape(
+                (1, image.height, image.width)) for image in images
+        ])
+        sample = ((sample / 255) * 2 - 1)
+        sample = torch.Tensor(sample).to(self.device)
+
+        for t in self.progress_bar(torch.flip(self.scheduler.timesteps,
+                                              (0, ))):
+            prev_timestep = (t - self.scheduler.num_train_timesteps //
+                             self.scheduler.num_inference_steps)
+            alpha_prod_t = self.scheduler.alphas_cumprod[t]
+            alpha_prod_t_prev = (self.scheduler.alphas_cumprod[prev_timestep]
+                                 if prev_timestep >= 0 else
+                                 self.scheduler.final_alpha_cumprod)
+            beta_prod_t = 1 - alpha_prod_t
+            model_output = self.unet(sample, t)['sample']
+            pred_sample_direction = (1 -
+                                     alpha_prod_t_prev)**(0.5) * model_output
+            sample = (sample -
+                      pred_sample_direction) * alpha_prod_t_prev**(-0.5)
+            sample = sample * alpha_prod_t**(0.5) + beta_prod_t**(
+                0.5) * model_output
+
+        return sample
+
+    @staticmethod
+    def slerp(x0: torch.Tensor, x1: torch.Tensor,
+              alpha: float) -> torch.Tensor:
+        """Spherical Linear intERPolation
+
+        Args:
+            x0 (torch.Tensor): first tensor to interpolate between
+            x1 (torch.Tensor): seconds tensor to interpolate between
+            alpha (float): interpolation between 0 and 1
+
+        Returns:
+            torch.Tensor: interpolated tensor
+        """
+
+        theta = acos(
+            torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) /
+            torch.norm(x1))
+        return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(
+            alpha * theta) * x1 / sin(theta)
+
+
+class LatentAudioDiffusionPipeline(AudioDiffusionPipeline):
+
+    def __init__(self, unet: UNet2DConditionModel,
+                 scheduler: Union[DDIMScheduler,
+                                  DDPMScheduler], vqvae: AutoencoderKL):
+        super().__init__(unet=unet, scheduler=scheduler)
+        self.register_modules(vqvae=vqvae)
+
+    def __call__(self, *args, **kwargs):
+        return super().__call__(*args, **kwargs)

audiodiffusion/mel.py

@@ -0,0 +1,127 @@
+import warnings
+
+warnings.filterwarnings('ignore')
+
+import librosa
+import numpy as np
+from PIL import Image
+
+
+class Mel:
+
+    def __init__(
+        self,
+        x_res: int = 256,
+        y_res: int = 256,
+        sample_rate: int = 22050,
+        n_fft: int = 2048,
+        hop_length: int = 512,
+        top_db: int = 80,
+    ):
+        """Class to convert audio to mel spectrograms and vice versa.
+
+        Args:
+            x_res (int): x resolution of spectrogram (time)
+            y_res (int): y resolution of spectrogram (frequency bins)
+            sample_rate (int): sample rate of audio
+            n_fft (int): number of Fast Fourier Transforms
+            hop_length (int): hop length (a higher number is recommended for lower than 256 y_res)
+            top_db (int): loudest in decibels
+        """
+        self.x_res = x_res
+        self.y_res = y_res
+        self.sr = sample_rate
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.n_mels = self.y_res
+        self.slice_size = self.x_res * self.hop_length - 1
+        self.fmax = self.sr / 2
+        self.top_db = top_db
+        self.audio = None
+
+    def load_audio(self, audio_file: str = None, raw_audio: np.ndarray = None):
+        """Load audio.
+
+        Args:
+            audio_file (str): must be a file on disk due to Librosa limitation or
+            raw_audio (np.ndarray): audio as numpy array
+        """
+        if audio_file is not None:
+            self.audio, _ = librosa.load(audio_file, mono=True, sr=self.sr)
+        else:
+            self.audio = raw_audio
+
+        # Pad with silence if necessary.
+        if len(self.audio) < self.x_res * self.hop_length:
+            self.audio = np.concatenate([
+                self.audio,
+                np.zeros((self.x_res * self.hop_length - len(self.audio), ))
+            ])
+
+    def get_number_of_slices(self) -> int:
+        """Get number of slices in audio.
+
+        Returns:
+            int: number of spectograms audio can be sliced into
+        """
+        return len(self.audio) // self.slice_size
+
+    def get_audio_slice(self, slice: int = 0) -> np.ndarray:
+        """Get slice of audio.
+
+        Args:
+            slice (int): slice number of audio (out of get_number_of_slices())
+
+        Returns:
+            np.ndarray: audio as numpy array
+        """
+        return self.audio[self.slice_size * slice:self.slice_size *
+                          (slice + 1)]
+
+    def get_sample_rate(self) -> int:
+        """Get sample rate:
+
+        Returns:
+            int: sample rate of audio
+        """
+        return self.sr
+
+    def audio_slice_to_image(self, slice: int) -> Image.Image:
+        """Convert slice of audio to spectrogram.
+
+        Args:
+            slice (int): slice number of audio to convert (out of get_number_of_slices())
+
+        Returns:
+            PIL Image: grayscale image of x_res x y_res
+        """
+        S = librosa.feature.melspectrogram(
+            y=self.get_audio_slice(slice),
+            sr=self.sr,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            n_mels=self.n_mels,
+            fmax=self.fmax,
+        )
+        log_S = librosa.power_to_db(S, ref=np.max, top_db=self.top_db)
+        bytedata = (((log_S + self.top_db) * 255 / self.top_db).clip(0, 255) +
+                    0.5).astype(np.uint8)
+        image = Image.fromarray(bytedata)
+        return image
+
+    def image_to_audio(self, image: Image.Image) -> np.ndarray:
+        """Converts spectrogram to audio.
+
+        Args:
+            image (PIL Image): x_res x y_res grayscale image
+
+        Returns:
+            audio (np.ndarray): raw audio
+        """
+        bytedata = np.frombuffer(image.tobytes(), dtype="uint8").reshape(
+            (image.height, image.width))
+        log_S = bytedata.astype("float") * self.top_db / 255 - self.top_db
+        S = librosa.db_to_power(log_S)
+        audio = librosa.feature.inverse.mel_to_audio(
+            S, sr=self.sr, n_fft=self.n_fft, hop_length=self.hop_length)
+        return audio

audiodiffusion/utils.py

@@ -0,0 +1,342 @@
+# adpated from https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py
+
+import torch
+from diffusers import AutoencoderKL
+
+
+def shave_segments(path, n_shave_prefix_segments=1):
+    """
+    Removes segments. Positive values shave the first segments, negative shave the last segments.
+    """
+    if n_shave_prefix_segments >= 0:
+        return ".".join(path.split(".")[n_shave_prefix_segments:])
+    else:
+        return ".".join(path.split(".")[:n_shave_prefix_segments])
+
+
+def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
+        new_item = shave_segments(
+            new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("norm.weight", "group_norm.weight")
+        new_item = new_item.replace("norm.bias", "group_norm.bias")
+
+        new_item = new_item.replace("q.weight", "query.weight")
+        new_item = new_item.replace("q.bias", "query.bias")
+
+        new_item = new_item.replace("k.weight", "key.weight")
+        new_item = new_item.replace("k.bias", "key.bias")
+
+        new_item = new_item.replace("v.weight", "value.weight")
+        new_item = new_item.replace("v.bias", "value.bias")
+
+        new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
+        new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
+
+        new_item = shave_segments(
+            new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def assign_to_checkpoint(paths,
+                         checkpoint,
+                         old_checkpoint,
+                         attention_paths_to_split=None,
+                         additional_replacements=None,
+                         config=None):
+    """
+    This does the final conversion step: take locally converted weights and apply a global renaming
+    to them. It splits attention layers, and takes into account additional replacements
+    that may arise.
+
+    Assigns the weights to the new checkpoint.
+    """
+    assert isinstance(
+        paths, list
+    ), "Paths should be a list of dicts containing 'old' and 'new' keys."
+
+    # Splits the attention layers into three variables.
+    if attention_paths_to_split is not None:
+        for path, path_map in attention_paths_to_split.items():
+            old_tensor = old_checkpoint[path]
+            channels = old_tensor.shape[0] // 3
+
+            target_shape = (-1,
+                            channels) if len(old_tensor.shape) == 3 else (-1)
+
+            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
+
+            old_tensor = old_tensor.reshape((num_heads, 3 * channels //
+                                             num_heads) + old_tensor.shape[1:])
+            query, key, value = old_tensor.split(channels // num_heads, dim=1)
+
+            checkpoint[path_map["query"]] = query.reshape(target_shape)
+            checkpoint[path_map["key"]] = key.reshape(target_shape)
+            checkpoint[path_map["value"]] = value.reshape(target_shape)
+
+    for path in paths:
+        new_path = path["new"]
+
+        # These have already been assigned
+        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
+            continue
+
+        # Global renaming happens here
+        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
+        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
+        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
+
+        if additional_replacements is not None:
+            for replacement in additional_replacements:
+                new_path = new_path.replace(replacement["old"],
+                                            replacement["new"])
+
+        # proj_attn.weight has to be converted from conv 1D to linear
+        if "proj_attn.weight" in new_path:
+            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
+        else:
+            checkpoint[new_path] = old_checkpoint[path["old"]]
+
+
+def conv_attn_to_linear(checkpoint):
+    keys = list(checkpoint.keys())
+    attn_keys = ["query.weight", "key.weight", "value.weight"]
+    for key in keys:
+        if ".".join(key.split(".")[-2:]) in attn_keys:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0, 0]
+        elif "proj_attn.weight" in key:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0]
+
+
+def create_vae_diffusers_config(original_config):
+    """
+    Creates a config for the diffusers based on the config of the LDM model.
+    """
+    vae_params = original_config.model.params.ddconfig
+    _ = original_config.model.params.embed_dim
+
+    block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
+    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
+    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
+
+    config = dict(
+        sample_size=vae_params.resolution,
+        in_channels=vae_params.in_channels,
+        out_channels=vae_params.out_ch,
+        down_block_types=tuple(down_block_types),
+        up_block_types=tuple(up_block_types),
+        block_out_channels=tuple(block_out_channels),
+        latent_channels=vae_params.z_channels,
+        layers_per_block=vae_params.num_res_blocks,
+    )
+    return config
+
+
+def convert_ldm_vae_checkpoint(checkpoint, config):
+    # extract state dict for VAE
+    vae_state_dict = checkpoint
+
+    new_checkpoint = {}
+
+    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict[
+        "encoder.conv_in.weight"]
+    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict[
+        "encoder.conv_in.bias"]
+    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict[
+        "encoder.conv_out.weight"]
+    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict[
+        "encoder.conv_out.bias"]
+    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict[
+        "encoder.norm_out.weight"]
+    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict[
+        "encoder.norm_out.bias"]
+
+    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict[
+        "decoder.conv_in.weight"]
+    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict[
+        "decoder.conv_in.bias"]
+    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict[
+        "decoder.conv_out.weight"]
+    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict[
+        "decoder.conv_out.bias"]
+    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict[
+        "decoder.norm_out.weight"]
+    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict[
+        "decoder.norm_out.bias"]
+
+    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
+    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
+    new_checkpoint["post_quant_conv.weight"] = vae_state_dict[
+        "post_quant_conv.weight"]
+    new_checkpoint["post_quant_conv.bias"] = vae_state_dict[
+        "post_quant_conv.bias"]
+
+    # Retrieves the keys for the encoder down blocks only
+    num_down_blocks = len({
+        ".".join(layer.split(".")[:3])
+        for layer in vae_state_dict if "encoder.down" in layer
+    })
+    down_blocks = {
+        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key]
+        for layer_id in range(num_down_blocks)
+    }
+
+    # Retrieves the keys for the decoder up blocks only
+    num_up_blocks = len({
+        ".".join(layer.split(".")[:3])
+        for layer in vae_state_dict if "decoder.up" in layer
+    })
+    up_blocks = {
+        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key]
+        for layer_id in range(num_up_blocks)
+    }
+
+    for i in range(num_down_blocks):
+        resnets = [
+            key for key in down_blocks[i]
+            if f"down.{i}" in key and f"down.{i}.downsample" not in key
+        ]
+
+        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
+            new_checkpoint[
+                f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
+                    f"encoder.down.{i}.downsample.conv.weight")
+            new_checkpoint[
+                f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
+                    f"encoder.down.{i}.downsample.conv.bias")
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {
+            "old": f"down.{i}.block",
+            "new": f"down_blocks.{i}.resnets"
+        }
+        assign_to_checkpoint(paths,
+                             new_checkpoint,
+                             vae_state_dict,
+                             additional_replacements=[meta_path],
+                             config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [
+            key for key in mid_resnets if f"encoder.mid.block_{i}" in key
+        ]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {
+            "old": f"mid.block_{i}",
+            "new": f"mid_block.resnets.{i - 1}"
+        }
+        assign_to_checkpoint(paths,
+                             new_checkpoint,
+                             vae_state_dict,
+                             additional_replacements=[meta_path],
+                             config=config)
+
+    mid_attentions = [
+        key for key in vae_state_dict if "encoder.mid.attn" in key
+    ]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths,
+                         new_checkpoint,
+                         vae_state_dict,
+                         additional_replacements=[meta_path],
+                         config=config)
+    conv_attn_to_linear(new_checkpoint)
+
+    for i in range(num_up_blocks):
+        block_id = num_up_blocks - 1 - i
+        resnets = [
+            key for key in up_blocks[block_id]
+            if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
+        ]
+
+        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
+            new_checkpoint[
+                f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
+                    f"decoder.up.{block_id}.upsample.conv.weight"]
+            new_checkpoint[
+                f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
+                    f"decoder.up.{block_id}.upsample.conv.bias"]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {
+            "old": f"up.{block_id}.block",
+            "new": f"up_blocks.{i}.resnets"
+        }
+        assign_to_checkpoint(paths,
+                             new_checkpoint,
+                             vae_state_dict,
+                             additional_replacements=[meta_path],
+                             config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [
+            key for key in mid_resnets if f"decoder.mid.block_{i}" in key
+        ]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {
+            "old": f"mid.block_{i}",
+            "new": f"mid_block.resnets.{i - 1}"
+        }
+        assign_to_checkpoint(paths,
+                             new_checkpoint,
+                             vae_state_dict,
+                             additional_replacements=[meta_path],
+                             config=config)
+
+    mid_attentions = [
+        key for key in vae_state_dict if "decoder.mid.attn" in key
+    ]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths,
+                         new_checkpoint,
+                         vae_state_dict,
+                         additional_replacements=[meta_path],
+                         config=config)
+    conv_attn_to_linear(new_checkpoint)
+    return new_checkpoint
+
+def convert_ldm_to_hf_vae(ldm_checkpoint, ldm_config, hf_checkpoint):
+    checkpoint = torch.load(ldm_checkpoint)["state_dict"]
+
+    # Convert the VAE model.
+    vae_config = create_vae_diffusers_config(ldm_config)
+    converted_vae_checkpoint = convert_ldm_vae_checkpoint(
+        checkpoint, vae_config)
+
+    vae = AutoencoderKL(**vae_config)
+    vae.load_state_dict(converted_vae_checkpoint)
+    vae.save_pretrained(hf_checkpoint)

config/accelerate_deepspeed.yaml

@@ -0,0 +1,18 @@
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  offload_optimizer_device: cpu
+  offload_param_device: cpu
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+mixed_precision: 'no'
+num_machines: 1
+num_processes: 1
+use_cpu: false

config/accelerate_local.yaml

@@ -0,0 +1,13 @@
+compute_environment: LOCAL_MACHINE
+deepspeed_config: {}
+distributed_type: 'NO'
+downcast_bf16: 'no'
+fsdp_config: {}
+machine_rank: 0
+main_process_ip: null
+main_process_port: null
+main_training_function: main
+mixed_precision: 'no'
+num_machines: 1
+num_processes: 1
+use_cpu: false

config/accelerate_sagemaker.yaml

@@ -0,0 +1,16 @@
+base_job_name: accelerate-sagemaker-1
+compute_environment: AMAZON_SAGEMAKER
+distributed_type: 'NO'
+ec2_instance_type: ml.p3.8xlarge
+iam_role_name: accelerate_sagemaker_execution_role
+image_uri: null
+mixed_precision: 'No'
+num_machines: 1
+profile: default
+py_version: py38
+pytorch_version: 1.10.2
+region: eu-west-2
+sagemaker_inputs_file: null
+sagemaker_metrics_file: null
+transformers_version: 4.17.0
+use_cpu: false

config/ldm_autoencoder_kl.yaml

@@ -0,0 +1,34 @@
+
+# based on https://github.com/CompVis/stable-diffusion/blob/main/configs/autoencoder/autoencoder_kl_32x32x4.yaml
+
+model:
+  base_learning_rate: 4.5e-6
+  target: ldm.models.autoencoder.AutoencoderKL
+  params:
+    monitor: "val/rec_loss"
+    embed_dim: 1  # = in_channels
+    lossconfig:
+      target: ldm.modules.losses.LPIPSWithDiscriminator
+      params:
+        disc_start: 50001
+        kl_weight: 0.000001
+        disc_weight: 0.5
+        disc_in_channels: 1  # = out_ch
+
+    ddconfig:
+      double_z: True
+      z_channels: 1  # must = embed_dim due to HF limitation
+      resolution: 256
+      in_channels: 1
+      out_ch: 1
+      ch: 128
+      ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
+      num_res_blocks: 2
+      attn_resolutions: [ ]
+      dropout: 0.0
+
+lightning:
+  trainer:
+    benchmark: True
+    accelerator: gpu
+    devices: 1

notebooks/gradio_app.ipynb

@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a489aa44",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/gradio_app.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9502ffa7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # are we running on Google Colab?\n",
+    "    import google.colab\n",
+    "    !git clone -q https://github.com/teticio/audio-diffusion.git\n",
+    "    %cd audio-diffusion\n",
+    "    !pip install -q -r requirements.txt\n",
+    "except:\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8f8b6e43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.insert(0, os.path.dirname(os.path.abspath(\"\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d948967",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import app\n",
+    "app.demo.launch(share=True);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46f03607",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "huggingface",
+   "language": "python",
+   "name": "huggingface"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/test_mel.ipynb

@@ -0,0 +1,148 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a61d194",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21f27189",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.insert(0, os.path.dirname(os.path.abspath(\"\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "218fcdf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import Audio\n",
+    "from audiodiffusion.mel import Mel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e4f8ee5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mel = Mel()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2178c3f",
+   "metadata": {},
+   "source": [
+    "### Transform slice of audio to mel spectrogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61dbcd2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mel.load_audio('/home/teticio/Music/Music/A Tribe Called Quest/The Anthology/08 Can I Kick It_.mp3')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccadcc0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = mel.audio_slice_to_image(15)\n",
+    "image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cec79c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image.width, image.height"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe112fef",
+   "metadata": {},
+   "source": [
+    "### Transform mel spectrogram back to audio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b268a54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio = mel.image_to_audio(image)\n",
+    "Audio(data=audio, rate=mel.get_sample_rate())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0dffbc4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "huggingface",
+   "language": "python",
+   "name": "huggingface"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/test_model.ipynb

@@ -0,0 +1,541 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "62c5865f",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/test_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c7800a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # are we running on Google Colab?\n",
+    "    import google.colab\n",
+    "    !git clone -q https://github.com/teticio/audio-diffusion.git\n",
+    "    %cd audio-diffusion\n",
+    "    !pip install -q -r requirements.txt\n",
+    "except:\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b447e2c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "sys.path.insert(0, os.path.dirname(os.path.abspath(\"\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2fc0e7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import random\n",
+    "import numpy as np\n",
+    "from datasets import load_dataset\n",
+    "from IPython.display import Audio\n",
+    "from audiodiffusion.mel import Mel\n",
+    "from audiodiffusion import AudioDiffusion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b294a94a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mel = Mel(x_res=256, y_res=256)\n",
+    "generator = torch.Generator()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3feb265",
+   "metadata": {},
+   "source": [
+    "## DDPM (De-noising Diffusion Probabilistic Models)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fd945bb",
+   "metadata": {},
+   "source": [
+    "### Select model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97f24046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@markdown teticio/audio-diffusion-256                     - trained on my Spotify \"liked\" playlist\n",
+    "\n",
+    "#@markdown teticio/audio-diffusion-breaks-256              - trained on samples used in music\n",
+    "\n",
+    "#@markdown teticio/audio-diffusion-instrumental-hiphop-256 - trained on instrumental hiphop\n",
+    "\n",
+    "model_id = \"teticio/audio-diffusion-256\"  #@param [\"teticio/audio-diffusion-256\", \"teticio/audio-diffusion-breaks-256\", \"audio-diffusion-instrumenal-hiphop-256\", \"teticio/audio-diffusion-ddim-256\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3d45c36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio_diffusion = AudioDiffusion(model_id=model_id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "011fb5a1",
+   "metadata": {},
+   "source": [
+    "### Run model inference to generate mel spectrogram, audios and loops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b809fed5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for _ in range(10):\n",
+    "    seed = generator.seed()\n",
+    "    print(f'Seed = {seed}')\n",
+    "    generator.manual_seed(seed)\n",
+    "    image, (sample_rate,\n",
+    "            audio) = audio_diffusion.generate_spectrogram_and_audio(\n",
+    "                generator=generator)\n",
+    "    display(image)\n",
+    "    display(Audio(audio, rate=sample_rate))\n",
+    "    loop = AudioDiffusion.loop_it(audio, sample_rate)\n",
+    "    if loop is not None:\n",
+    "        display(Audio(loop, rate=sample_rate))\n",
+    "    else:\n",
+    "        print(\"Unable to determine loop points\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bb03e33",
+   "metadata": {},
+   "source": [
+    "### Generate variations of audios"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80e5b5fa",
+   "metadata": {},
+   "source": [
+    "Try playing around with `start_steps`. Values closer to zero will produce new samples, while values closer to 1,000 will produce samples more faithful to the original."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5074ec11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "seed = 16183389798189209330  #@param {type:\"integer\"}\n",
+    "generator.manual_seed(seed)\n",
+    "image, (sample_rate, audio) = audio_diffusion.generate_spectrogram_and_audio(\n",
+    "    generator=generator)\n",
+    "display(image)\n",
+    "display(Audio(audio, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0fefe28",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "start_steps = 500  #@param {type:\"slider\", min:0, max:1000, step:10}\n",
+    "track = AudioDiffusion.loop_it(audio, sample_rate, loops=1)\n",
+    "for variation in range(12):\n",
+    "    image2, (\n",
+    "        sample_rate,\n",
+    "        audio2) = audio_diffusion.generate_spectrogram_and_audio_from_audio(\n",
+    "            raw_audio=audio, start_step=start_steps)\n",
+    "    display(image2)\n",
+    "    display(Audio(audio2, rate=sample_rate))\n",
+    "    track = np.concatenate(\n",
+    "        [track, AudioDiffusion.loop_it(audio2, sample_rate, loops=1)])\n",
+    "display(Audio(track, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a876c1",
+   "metadata": {},
+   "source": [
+    "### Generate continuations (\"out-painting\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b95d5780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overlap_secs = 2  #@param {type:\"integer\"}\n",
+    "start_step = 0  #@param {type:\"slider\", min:0, max:1000, step:10}\n",
+    "overlap_samples = overlap_secs * sample_rate\n",
+    "track = audio\n",
+    "for variation in range(12):\n",
+    "    image2, (\n",
+    "        sample_rate,\n",
+    "        audio2) = audio_diffusion.generate_spectrogram_and_audio_from_audio(\n",
+    "            raw_audio=audio[-overlap_samples:],\n",
+    "            start_step=start_step,\n",
+    "            mask_start_secs=overlap_secs)\n",
+    "    display(image2)\n",
+    "    display(Audio(audio2, rate=sample_rate))\n",
+    "    track = np.concatenate([track, audio2[overlap_samples:]])\n",
+    "    audio = audio2\n",
+    "display(Audio(track, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6434d3f",
+   "metadata": {},
+   "source": [
+    "### Remix (style transfer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0da030b2",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can start from another audio altogether, resulting in a kind of style transfer. Maintaining the same seed during generation fixes the style, while masking helps stitch consecutive segments together more smoothly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc620a80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # are we running on Google Colab?\n",
+    "    from google.colab import files\n",
+    "    audio_file = list(files.upload().keys())[0]\n",
+    "except:\n",
+    "    audio_file = \"/home/teticio/Music/liked/El Michels Affair - Glaciers Of Ice.mp3\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a257e69",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "start_step = 500  #@param {type:\"slider\", min:0, max:1000, step:10}\n",
+    "overlap_secs = 2  #@param {type:\"integer\"}\n",
+    "mel.load_audio(audio_file)\n",
+    "overlap_samples = overlap_secs * mel.get_sample_rate()\n",
+    "slice_size = mel.x_res * mel.hop_length\n",
+    "stride = slice_size - overlap_samples\n",
+    "generator = torch.Generator()\n",
+    "seed = generator.seed()\n",
+    "print(f'Seed = {seed}')\n",
+    "track = np.array([])\n",
+    "not_first = 0\n",
+    "for sample in range(len(mel.audio) // stride):\n",
+    "    generator.manual_seed(seed)\n",
+    "    audio = np.array(mel.audio[sample * stride:sample * stride + slice_size])\n",
+    "    if not_first:\n",
+    "        # Normalize and re-insert generated audio\n",
+    "        audio[:overlap_samples] = audio2[-overlap_samples:] * np.max(\n",
+    "            audio[:overlap_samples]) / np.max(audio2[-overlap_samples:])\n",
+    "    _, (sample_rate,\n",
+    "        audio2) = audio_diffusion.generate_spectrogram_and_audio_from_audio(\n",
+    "            raw_audio=audio,\n",
+    "            start_step=start_step,\n",
+    "            generator=generator,\n",
+    "            mask_start_secs=overlap_secs * not_first)\n",
+    "    track = np.concatenate([track, audio2[overlap_samples * not_first:]])\n",
+    "    not_first = 1\n",
+    "    display(Audio(track, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "924ff9d5",
+   "metadata": {},
+   "source": [
+    "### Fill the gap (\"in-painting\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0200264c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "slice = 3  #@param {type:\"integer\"}\n",
+    "audio = mel.get_audio_slice(slice)\n",
+    "_, (sample_rate,\n",
+    "    audio2) = audio_diffusion.generate_spectrogram_and_audio_from_audio(\n",
+    "        raw_audio=mel.get_audio_slice(slice),\n",
+    "        mask_start_secs=1,\n",
+    "        mask_end_secs=1,\n",
+    "        step_generator=torch.Generator())\n",
+    "display(Audio(audio, rate=sample_rate))\n",
+    "display(Audio(audio2, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efc32dae",
+   "metadata": {},
+   "source": [
+    "## DDIM (De-noising Diffusion Implicit Models)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a021f78a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "audio_diffusion = AudioDiffusion(model_id='teticio/audio-diffusion-ddim-256')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "deb23339",
+   "metadata": {},
+   "source": [
+    "### Generation can be done in many fewer steps with DDIMs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c105a497",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for _ in range(10):\n",
+    "    seed = generator.seed()\n",
+    "    print(f'Seed = {seed}')\n",
+    "    generator.manual_seed(seed)\n",
+    "    image, (sample_rate,\n",
+    "            audio) = audio_diffusion.generate_spectrogram_and_audio(\n",
+    "                generator=generator)\n",
+    "    display(image)\n",
+    "    display(Audio(audio, rate=sample_rate))\n",
+    "    loop = AudioDiffusion.loop_it(audio, sample_rate)\n",
+    "    if loop is not None:\n",
+    "        display(Audio(loop, rate=sample_rate))\n",
+    "    else:\n",
+    "        print(\"Unable to determine loop points\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cab4692c",
+   "metadata": {},
+   "source": [
+    "The parameter eta controls the variance:\n",
+    "* 0 - DDIM (deterministic)\n",
+    "* 1 - DDPM (De-noising Diffusion Probabilistic Model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72bdd207",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image, (sample_rate, audio) = audio_diffusion.generate_spectrogram_and_audio(\n",
+    "    steps=1000, generator=generator, eta=1)\n",
+    "display(image)\n",
+    "display(Audio(audio, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8d5442c",
+   "metadata": {},
+   "source": [
+    "### DDIMs can be used as encoders..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "269ee816",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Doesn't have to be an audio from the train dataset, this is just for convenience\n",
+    "ds = load_dataset('teticio/audio-diffusion-256')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "278d1d80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image = ds['train'][264]['image']\n",
+    "display(Audio(mel.image_to_audio(image), rate=mel.get_sample_rate()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "912b54e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noise = audio_diffusion.pipe.encode([image])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7b31f97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reconstruct original audio from noise\n",
+    "_, (sample_rate, audio) = audio_diffusion.generate_spectrogram_and_audio(\n",
+    "    noise=noise, generator=generator)\n",
+    "display(Audio(audio, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "998c776b",
+   "metadata": {},
+   "source": [
+    "### ...or to interpolate between audios"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33f82367",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image2 = ds['train'][15978]['image']\n",
+    "display(Audio(mel.image_to_audio(image2), rate=mel.get_sample_rate()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f93fb6c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noise2 = audio_diffusion.pipe.encode([image2], steps=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4190563",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = 0.5  #@param {type:\"slider\", min:0, max:1, step:0.1}\n",
+    "_, (sample_rate, audio) = audio_diffusion.generate_spectrogram_and_audio(\n",
+    "    noise=audio_diffusion.pipe.slerp(noise, noise2, alpha),\n",
+    "    generator=generator)\n",
+    "display(Audio(mel.image_to_audio(image), rate=mel.get_sample_rate()))\n",
+    "display(Audio(mel.image_to_audio(image2), rate=mel.get_sample_rate()))\n",
+    "display(Audio(audio, rate=sample_rate))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b05539f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "provenance": []
+  },
+  "gpuClass": "standard",
+  "kernelspec": {
+   "display_name": "huggingface",
+   "language": "python",
+   "name": "huggingface"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/train_model.ipynb

@@ -0,0 +1,599 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "62c5865f",
+   "metadata": {
+    "id": "62c5865f"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/train_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c7800a6",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6c7800a6",
+    "outputId": "ed18f4a9-ccea-4d7c-c82b-1749f1041f6c"
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # are we running on Google Colab?\n",
+    "    import google.colab\n",
+    "    !git clone -q https://github.com/teticio/audio-diffusion.git\n",
+    "    %cd audio-diffusion\n",
+    "    !pip install -q -r requirements.txt .\n",
+    "except:\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c2fc0e7a",
+   "metadata": {
+    "id": "c2fc0e7a"
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Audio\n",
+    "from audiodiffusion import AudioDiffusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "MqlpL75_mDVv",
+   "metadata": {
+    "id": "MqlpL75_mDVv"
+   },
+   "source": [
+    "### Upload / specify audio files to train on\n",
+    "Provide some MP3 or WAV files that will be split into samples and converted to Mel spectrograms. For a resolution of 256, the samples will be about 5 seconds long."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "jg1zAHVsmCBG",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 73
+    },
+    "id": "jg1zAHVsmCBG",
+    "outputId": "414244c9-02b6-4ccf-cbfd-83f9022a0fc1"
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    # are we running on Google Colab?\n",
+    "    from google.colab import files\n",
+    "    input_dir = '.'\n",
+    "    files.upload();\n",
+    "except:\n",
+    "    input_dir = \"/home/teticio/Music/liked\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10v0RCSUu75P",
+   "metadata": {
+    "id": "10v0RCSUu75P"
+   },
+   "source": [
+    "### Prepare dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "NJNeEU6ftaTM",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NJNeEU6ftaTM",
+    "outputId": "6c5bed15-c821-4def-eb90-3ab1a17b3c3d"
+   },
+   "outputs": [],
+   "source": [
+    "!python scripts/audio_to_images.py \\\n",
+    "  --resolution 256,256 \\\n",
+    "  --input_dir {input_dir} \\\n",
+    "  --output_dir data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5mGeXyJFvQCO",
+   "metadata": {
+    "id": "5mGeXyJFvQCO"
+   },
+   "source": [
+    "### Train model\n",
+    "The DDIM scheduler generates samples much faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "JGnlePbLvTOH",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JGnlePbLvTOH",
+    "outputId": "69b6f53e-25a3-4c59-e205-2eab42889cd8"
+   },
+   "outputs": [],
+   "source": [
+    "!python scripts/train_unconditional.py \\\n",
+    "  --dataset_name data \\\n",
+    "  --output_dir model \\\n",
+    "  --num_epochs 10 \\\n",
+    "  --train_batch_size 2 \\\n",
+    "  --eval_batch_size 2 \\\n",
+    "  --gradient_accumulation_steps 8 \\\n",
+    "  --save_images_epochs 100 \\\n",
+    "  --save_model_epochs 1 \\\n",
+    "  --scheduler ddim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nTMAYEtMxtt0",
+   "metadata": {
+    "id": "nTMAYEtMxtt0"
+   },
+   "source": [
+    "### Generate samples with model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b294a94a",
+   "metadata": {
+    "id": "b294a94a"
+   },
+   "outputs": [],
+   "source": [
+    "audio_diffusion = AudioDiffusion('model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "k2bKq3aqyAIM",
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+    "image, (sample_rate, audio) = audio_diffusion.generate_spectrogram_and_audio()\n",
+    "display(image)\n",
+    "display(Audio(audio, rate=sample_rate))"
+   ]
+  },
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requirements-lock.txt

@@ -0,0 +1,182 @@
+absl-py==1.2.0
+accelerate==0.12.0
+aiohttp==3.8.1
+aiosignal==1.2.0
+altair==4.2.0
+analytics-python==1.4.0
+anyio==3.6.1
+appdirs==1.4.4
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+asttokens==2.0.8
+async-timeout==4.0.2
+attrs==22.1.0
+audioread==3.0.0
+backcall==0.2.0
+backoff==1.10.0
+bcrypt==4.0.0
+beautifulsoup4==4.11.1
+bleach==5.0.1
+blinker==1.5
+cachetools==5.2.0
+certifi==2022.6.15
+cffi==1.15.1
+charset-normalizer==2.1.1
+click==8.1.3
+commonmark==0.9.1
+cryptography==37.0.4
+cycler==0.11.0
+datasets==2.4.0
+debugpy==1.6.3
+decorator==5.1.1
+defusedxml==0.7.1
+diffusers==0.2.4
+dill==0.3.5.1
+entrypoints==0.4
+executing==0.10.0
+fastapi==0.81.0
+fastjsonschema==2.16.1
+ffmpy==0.3.0
+filelock==3.8.0
+fonttools==4.37.1
+frozenlist==1.3.1
+fsspec==2022.7.1
+ftfy==6.1.1
+gitdb==4.0.9
+GitPython==3.1.27
+google-auth==2.11.0
+google-auth-oauthlib==0.4.6
+gradio==3.1.7
+grpcio==1.47.0
+h11==0.12.0
+httpcore==0.15.0
+httpx==0.23.0
+huggingface-hub==0.9.0
+idna==3.3
+importlib-metadata==4.12.0
+ipykernel==6.15.1
+ipython==8.4.0
+ipython-genutils==0.2.0
+ipywidgets==7.7.1
+jedi==0.18.1
+Jinja2==3.1.2
+joblib==1.1.0
+jsonschema==4.14.0
+jupyter-client==7.3.4
+jupyter-console==6.4.4
+jupyter-core==4.11.1
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==3.0.2
+kiwisolver==1.4.4
+librosa==0.9.2
+linkify-it-py==1.0.3
+llvmlite==0.39.0
+lxml==4.9.1
+Markdown==3.4.1
+markdown-it-py==2.1.0
+MarkupSafe==2.1.1
+matplotlib==3.5.3
+matplotlib-inline==0.1.6
+mdit-py-plugins==0.3.0
+mdurl==0.1.2
+mistune==2.0.4
+monotonic==1.6
+multidict==6.0.2
+multiprocess==0.70.13
+nbclient==0.6.7
+nbconvert==7.0.0
+nbformat==5.4.0
+nest-asyncio==1.5.5
+notebook==6.4.12
+numba==0.56.0
+numpy==1.22.4
+oauthlib==3.2.0
+orjson==3.8.0
+packaging==21.3
+pandas==1.4.3
+pandocfilters==1.5.0
+paramiko==2.11.0
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.2.0
+pooch==1.6.0
+prometheus-client==0.14.1
+prompt-toolkit==3.0.30
+protobuf==3.19.4
+psutil==5.9.1
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==9.0.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+pycparser==2.21
+pycryptodome==3.15.0
+pydantic==1.9.2
+pydeck==0.8.0b1
+pydub==0.25.1
+Pygments==2.13.0
+Pympler==1.0.1
+PyNaCl==1.5.0
+pyparsing==3.0.9
+pyrsistent==0.18.1
+python-dateutil==2.8.2
+python-multipart==0.0.5
+pytz==2022.2.1
+pytz-deprecation-shim==0.1.0.post0
+PyYAML==6.0
+pyzmq==23.2.1
+qtconsole==5.3.1
+QtPy==2.2.0
+regex==2022.8.17
+requests==2.28.1
+requests-oauthlib==1.3.1
+resampy==0.4.0
+responses==0.18.0
+rfc3986==1.5.0
+rich==12.5.1
+rsa==4.9
+scikit-learn==1.1.2
+scipy==1.9.0
+semver==2.13.0
+Send2Trash==1.8.0
+six==1.16.0
+smmap==5.0.0
+sniffio==1.2.0
+SoundFile==0.10.3.post1
+soupsieve==2.3.2.post1
+stack-data==0.4.0
+starlette==0.19.1
+streamlit==1.12.2
+tensorboard==2.10.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.1
+terminado==0.15.0
+threadpoolctl==3.1.0
+tinycss2==1.1.1
+tokenizers==0.12.1
+toml==0.10.2
+toolz==0.12.0
+torch==1.12.1
+torchvision==0.13.1
+tornado==6.2
+tqdm==4.64.0
+traitlets==5.3.0
+transformers==4.21.1
+typing_extensions==4.3.0
+tzdata==2022.2
+tzlocal==4.2
+uc-micro-py==1.0.1
+urllib3==1.26.12
+uvicorn==0.18.3
+validators==0.20.0
+watchdog==2.1.9
+wcwidth==0.2.5
+webencodings==0.5.1
+websockets==10.3
+Werkzeug==2.2.2
+widgetsnbextension==3.6.1
+xxhash==3.0.0
+yapf==0.32.0
+yarl==1.8.1
+zipp==3.8.1

requirements.txt

@@ -0,0 +1,10 @@
+torch
+numpy
+Pillow
+diffusers>=0.4.1
+librosa
+datasets
+gradio
+streamlit
+tensorboard
+accelerate

scripts/audio_to_images.py

@@ -0,0 +1,109 @@
+import os
+import re
+import io
+import logging
+import argparse
+
+import numpy as np
+import pandas as pd
+from tqdm.auto import tqdm
+from datasets import Dataset, DatasetDict, Features, Image, Value
+
+from audiodiffusion.mel import Mel
+
+logging.basicConfig(level=logging.WARN)
+logger = logging.getLogger('audio_to_images')
+
+
+def main(args):
+    mel = Mel(x_res=args.resolution[0],
+              y_res=args.resolution[1],
+              hop_length=args.hop_length,
+              sample_rate=args.sample_rate)   
+    os.makedirs(args.output_dir, exist_ok=True)
+    audio_files = [
+        os.path.join(root, file) for root, _, files in os.walk(args.input_dir)
+        for file in files if re.search("\.(mp3|wav|m4a)$", file, re.IGNORECASE)
+    ]
+    examples = []
+    try:
+        for audio_file in tqdm(audio_files):
+            try:
+                mel.load_audio(audio_file)
+            except KeyboardInterrupt:
+                raise
+            except:
+                continue
+            for slice in range(mel.get_number_of_slices()):
+                image = mel.audio_slice_to_image(slice)
+                assert (image.width == args.resolution[0] and image.height
+                        == args.resolution[1]), "Wrong resolution"
+                # skip completely silent slices
+                if all(np.frombuffer(image.tobytes(), dtype=np.uint8) == 255):
+                    logger.warn('File %s slice %d is completely silent',
+                                audio_file, slice)
+                    continue
+                with io.BytesIO() as output:
+                    image.save(output, format="PNG")
+                    bytes = output.getvalue()
+                examples.extend([{
+                    "image": {
+                        "bytes": bytes
+                    },
+                    "audio_file": audio_file,
+                    "slice": slice,
+                }])
+    except Exception as e:
+        print(e)
+    finally:
+        if len(examples) == 0:
+            logger.warn('No valid audio files were found.')
+            return
+        ds = Dataset.from_pandas(
+            pd.DataFrame(examples),
+            features=Features({
+                "image": Image(),
+                "audio_file": Value(dtype="string"),
+                "slice": Value(dtype="int16"),
+            }),
+        )
+        dsd = DatasetDict({"train": ds})
+        dsd.save_to_disk(os.path.join(args.output_dir))
+        if args.push_to_hub:
+            dsd.push_to_hub(args.push_to_hub)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description=
+        "Create dataset of Mel spectrograms from directory of audio files.")
+    parser.add_argument("--input_dir", type=str)
+    parser.add_argument("--output_dir", type=str, default="data")
+    parser.add_argument("--resolution",
+                        type=str,
+                        default="256",
+                        help="Either square resolution or width,height.")
+    parser.add_argument("--hop_length", type=int, default=512)
+    parser.add_argument("--push_to_hub", type=str, default=None)
+    parser.add_argument("--sample_rate", type=int, default=22050)
+    args = parser.parse_args()
+
+    if args.input_dir is None:
+        raise ValueError(
+            "You must specify an input directory for the audio files.")
+
+    # Handle the resolutions.
+    try:
+        args.resolution = (int(args.resolution), int(args.resolution))
+    except ValueError:
+        try:
+            args.resolution = tuple(int(x) for x in args.resolution.split(","))
+            if len(args.resolution) != 2:
+                raise ValueError
+        except ValueError:
+            raise ValueError(
+                "Resolution must be a tuple of two integers or a single integer."
+            )
+    assert isinstance(args.resolution, tuple)
+
+    main(args)

scripts/train_unconditional.py

@@ -0,0 +1,390 @@
+# based on https://github.com/huggingface/diffusers/blob/main/examples/train_unconditional.py
+
+import argparse
+import os
+
+import torch
+import torch.nn.functional as F
+
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from datasets import load_from_disk, load_dataset
+from diffusers import (DiffusionPipeline, DDPMScheduler, UNet2DModel,
+                       DDIMScheduler, AutoencoderKL)
+from diffusers.hub_utils import init_git_repo, push_to_hub
+from diffusers.optimization import get_scheduler
+from diffusers.training_utils import EMAModel
+from torchvision.transforms import (
+    Compose,
+    Normalize,
+    ToTensor,
+)
+import numpy as np
+from tqdm.auto import tqdm
+from librosa.util import normalize
+
+from audiodiffusion.mel import Mel
+from audiodiffusion import LatentAudioDiffusionPipeline, AudioDiffusionPipeline
+
+logger = get_logger(__name__)
+
+
+def main(args):
+    output_dir = os.environ.get("SM_MODEL_DIR", None) or args.output_dir
+    logging_dir = os.path.join(output_dir, args.logging_dir)
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with="tensorboard",
+        logging_dir=logging_dir,
+    )
+
+    if args.dataset_name is not None:
+        if os.path.exists(args.dataset_name):
+            dataset = load_from_disk(args.dataset_name,
+                                     args.dataset_config_name)["train"]
+        else:
+            dataset = load_dataset(
+                args.dataset_name,
+                args.dataset_config_name,
+                cache_dir=args.cache_dir,
+                use_auth_token=True if args.use_auth_token else None,
+                split="train",
+            )
+    else:
+        dataset = load_dataset(
+            "imagefolder",
+            data_dir=args.train_data_dir,
+            cache_dir=args.cache_dir,
+            split="train",
+        )
+    # Determine image resolution
+    resolution = dataset[0]['image'].height, dataset[0]['image'].width
+
+    augmentations = Compose([
+        ToTensor(),
+        Normalize([0.5], [0.5]),
+    ])
+
+    def transforms(examples):
+        if args.vae is not None and vqvae.config['in_channels'] == 3:
+            images = [
+                augmentations(image.convert('RGB'))
+                for image in examples["image"]
+            ]
+        else:
+            images = [augmentations(image) for image in examples["image"]]
+        return {"input": images}
+
+    dataset.set_transform(transforms)
+    train_dataloader = torch.utils.data.DataLoader(
+        dataset, batch_size=args.train_batch_size, shuffle=True)
+
+    vqvae = None
+    if args.vae is not None:
+        try:
+            vqvae = AutoencoderKL.from_pretrained(args.vae)
+        except EnvironmentError:
+            vqvae = LatentAudioDiffusionPipeline.from_pretrained(
+                args.vae).vqvae
+        # Determine latent resolution
+        with torch.no_grad():
+            latent_resolution = vqvae.encode(
+                torch.zeros((1, 1) +
+                            resolution)).latent_dist.sample().shape[2:]
+
+    if args.from_pretrained is not None:
+        pipeline = {
+            'LatentAudioDiffusionPipeline': LatentAudioDiffusionPipeline,
+            'AudioDiffusionPipeline': AudioDiffusionPipeline
+        }.get(
+            DiffusionPipeline.get_config_dict(
+                args.from_pretrained)['_class_name'], AudioDiffusionPipeline)
+        pipeline = pipeline.from_pretrained(args.from_pretrained)
+        model = pipeline.unet
+        if hasattr(pipeline, 'vqvae'):
+            vqvae = pipeline.vqvae
+    else:
+        model = UNet2DModel(
+            sample_size=resolution if vqvae is None else latent_resolution,
+            in_channels=1
+            if vqvae is None else vqvae.config['latent_channels'],
+            out_channels=1
+            if vqvae is None else vqvae.config['latent_channels'],
+            layers_per_block=2,
+            block_out_channels=(128, 128, 256, 256, 512, 512),
+            down_block_types=(
+                "DownBlock2D",
+                "DownBlock2D",
+                "DownBlock2D",
+                "DownBlock2D",
+                "AttnDownBlock2D",
+                "DownBlock2D",
+            ),
+            up_block_types=(
+                "UpBlock2D",
+                "AttnUpBlock2D",
+                "UpBlock2D",
+                "UpBlock2D",
+                "UpBlock2D",
+                "UpBlock2D",
+            ),
+        )
+
+    if args.scheduler == "ddpm":
+        noise_scheduler = DDPMScheduler(
+            num_train_timesteps=args.num_train_steps)
+    else:
+        noise_scheduler = DDIMScheduler(
+            num_train_timesteps=args.num_train_steps)
+
+    optimizer = torch.optim.AdamW(
+        model.parameters(),
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps,
+        num_training_steps=(len(train_dataloader) * args.num_epochs) //
+        args.gradient_accumulation_steps,
+    )
+
+    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        model, optimizer, train_dataloader, lr_scheduler)
+
+    ema_model = EMAModel(
+        getattr(model, "module", model),
+        inv_gamma=args.ema_inv_gamma,
+        power=args.ema_power,
+        max_value=args.ema_max_decay,
+    )
+
+    if args.push_to_hub:
+        repo = init_git_repo(args, at_init=True)
+
+    if accelerator.is_main_process:
+        run = os.path.split(__file__)[-1].split(".")[0]
+        accelerator.init_trackers(run)
+
+    mel = Mel(x_res=resolution[1],
+              y_res=resolution[0],
+              hop_length=args.hop_length)
+
+    global_step = 0
+    for epoch in range(args.num_epochs):
+        progress_bar = tqdm(total=len(train_dataloader),
+                            disable=not accelerator.is_local_main_process)
+        progress_bar.set_description(f"Epoch {epoch}")
+
+        if epoch < args.start_epoch:
+            for step in range(len(train_dataloader)):
+                optimizer.step()
+                lr_scheduler.step()
+                progress_bar.update(1)
+                global_step += 1
+            if epoch == args.start_epoch - 1 and args.use_ema:
+                ema_model.optimization_step = global_step
+            continue
+
+        model.train()
+        for step, batch in enumerate(train_dataloader):
+            clean_images = batch["input"]
+
+            if vqvae is not None:
+                vqvae.to(clean_images.device)
+                with torch.no_grad():
+                    clean_images = vqvae.encode(
+                        clean_images).latent_dist.sample()
+                # Scale latent images to ensure approximately unit variance
+                clean_images = clean_images * 0.18215
+
+            # Sample noise that we'll add to the images
+            noise = torch.randn(clean_images.shape).to(clean_images.device)
+            bsz = clean_images.shape[0]
+            # Sample a random timestep for each image
+            timesteps = torch.randint(
+                0,
+                noise_scheduler.num_train_timesteps,
+                (bsz, ),
+                device=clean_images.device,
+            ).long()
+
+            # Add noise to the clean images according to the noise magnitude at each timestep
+            # (this is the forward diffusion process)
+            noisy_images = noise_scheduler.add_noise(clean_images, noise,
+                                                     timesteps)
+
+            with accelerator.accumulate(model):
+                # Predict the noise residual
+                noise_pred = model(noisy_images, timesteps)["sample"]
+                loss = F.mse_loss(noise_pred, noise)
+                accelerator.backward(loss)
+
+                if accelerator.sync_gradients:
+                    accelerator.clip_grad_norm_(model.parameters(), 1.0)
+                optimizer.step()
+                lr_scheduler.step()
+                if args.use_ema:
+                    ema_model.step(model)
+                optimizer.zero_grad()
+
+            progress_bar.update(1)
+            global_step += 1
+
+            logs = {
+                "loss": loss.detach().item(),
+                "lr": lr_scheduler.get_last_lr()[0],
+                "step": global_step,
+            }
+            if args.use_ema:
+                logs["ema_decay"] = ema_model.decay
+            progress_bar.set_postfix(**logs)
+            accelerator.log(logs, step=global_step)
+        progress_bar.close()
+
+        accelerator.wait_for_everyone()
+
+        # Generate sample images for visual inspection
+        if accelerator.is_main_process:
+            if (
+                    epoch + 1
+            ) % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
+                if vqvae is not None:
+                    pipeline = LatentAudioDiffusionPipeline(
+                        unet=accelerator.unwrap_model(
+                            ema_model.averaged_model if args.use_ema else model
+                        ),
+                        vqvae=vqvae,
+                        scheduler=noise_scheduler)
+                else:
+                    pipeline = AudioDiffusionPipeline(
+                        unet=accelerator.unwrap_model(
+                            ema_model.averaged_model if args.use_ema else model
+                        ),
+                        scheduler=noise_scheduler,
+                    )
+
+                # save the model
+                if args.push_to_hub:
+                    try:
+                        push_to_hub(
+                            args,
+                            pipeline,
+                            repo,
+                            commit_message=f"Epoch {epoch}",
+                            blocking=False,
+                        )
+                    except NameError:  # current version of diffusers has a little bug
+                        pass
+                else:
+                    pipeline.save_pretrained(output_dir)
+
+            if (epoch + 1) % args.save_images_epochs == 0:
+                generator = torch.manual_seed(42)
+                # run pipeline in inference (sample random noise and denoise)
+                images, (sample_rate, audios) = pipeline(
+                    mel=mel,
+                    generator=generator,
+                    batch_size=args.eval_batch_size,
+                )
+
+                # denormalize the images and save to tensorboard
+                images = np.array([
+                    np.frombuffer(image.tobytes(), dtype="uint8").reshape(
+                        (len(image.getbands()), image.height, image.width))
+                    for image in images
+                ])
+                accelerator.trackers[0].writer.add_images(
+                    "test_samples", images, epoch)
+                for _, audio in enumerate(audios):
+                    accelerator.trackers[0].writer.add_audio(
+                        f"test_audio_{_}",
+                        normalize(audio),
+                        epoch,
+                        sample_rate=sample_rate,
+                    )
+        accelerator.wait_for_everyone()
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Simple example of a training script.")
+    parser.add_argument("--local_rank", type=int, default=-1)
+    parser.add_argument("--dataset_name", type=str, default=None)
+    parser.add_argument("--dataset_config_name", type=str, default=None)
+    parser.add_argument(
+        "--train_data_dir",
+        type=str,
+        default=None,
+        help="A folder containing the training data.",
+    )
+    parser.add_argument("--output_dir", type=str, default="ddpm-model-64")
+    parser.add_argument("--overwrite_output_dir", type=bool, default=False)
+    parser.add_argument("--cache_dir", type=str, default=None)
+    parser.add_argument("--train_batch_size", type=int, default=16)
+    parser.add_argument("--eval_batch_size", type=int, default=16)
+    parser.add_argument("--num_epochs", type=int, default=100)
+    parser.add_argument("--save_images_epochs", type=int, default=10)
+    parser.add_argument("--save_model_epochs", type=int, default=10)
+    parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
+    parser.add_argument("--learning_rate", type=float, default=1e-4)
+    parser.add_argument("--lr_scheduler", type=str, default="cosine")
+    parser.add_argument("--lr_warmup_steps", type=int, default=500)
+    parser.add_argument("--adam_beta1", type=float, default=0.95)
+    parser.add_argument("--adam_beta2", type=float, default=0.999)
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-6)
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08)
+    parser.add_argument("--use_ema", type=bool, default=True)
+    parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
+    parser.add_argument("--ema_power", type=float, default=3 / 4)
+    parser.add_argument("--ema_max_decay", type=float, default=0.9999)
+    parser.add_argument("--push_to_hub", type=bool, default=False)
+    parser.add_argument("--use_auth_token", type=bool, default=False)
+    parser.add_argument("--hub_token", type=str, default=None)
+    parser.add_argument("--hub_model_id", type=str, default=None)
+    parser.add_argument("--hub_private_repo", type=bool, default=False)
+    parser.add_argument("--logging_dir", type=str, default="logs")
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose"
+            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+            "and an Nvidia Ampere GPU."),
+    )
+    parser.add_argument("--hop_length", type=int, default=512)
+    parser.add_argument("--from_pretrained", type=str, default=None)
+    parser.add_argument("--start_epoch", type=int, default=0)
+    parser.add_argument("--num_train_steps", type=int, default=1000)
+    parser.add_argument("--scheduler",
+                        type=str,
+                        default="ddpm",
+                        help="ddpm or ddim")
+    parser.add_argument("--vae",
+                        type=str,
+                        default=None,
+                        help="pretrained VAE model for latent diffusion")
+
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    if args.dataset_name is None and args.train_data_dir is None:
+        raise ValueError(
+            "You must specify either a dataset name from the hub or a train data directory."
+        )
+    if args.dataset_name is not None and args.dataset_name == args.hub_model_id:
+        raise ValueError(
+            "The local dataset name must be different from the hub model id.")
+
+    main(args)

scripts/train_vae.py

@@ -0,0 +1,177 @@
+# based on https://github.com/CompVis/stable-diffusion/blob/main/main.py
+
+import os
+import argparse
+
+import torch
+import torchvision
+import numpy as np
+from PIL import Image
+import pytorch_lightning as pl
+from omegaconf import OmegaConf
+from librosa.util import normalize
+from ldm.util import instantiate_from_config
+from pytorch_lightning.trainer import Trainer
+from torch.utils.data import DataLoader, Dataset
+from datasets import load_from_disk, load_dataset
+from pytorch_lightning.callbacks import Callback, ModelCheckpoint
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+from audiodiffusion.mel import Mel
+from audiodiffusion.utils import convert_ldm_to_hf_vae
+
+
+class AudioDiffusion(Dataset):
+
+    def __init__(self, model_id, channels=3):
+        super().__init__()
+        self.channels = channels
+        if os.path.exists(model_id):
+            self.hf_dataset = load_from_disk(model_id)['train']
+        else:
+            self.hf_dataset = load_dataset(model_id)['train']
+
+    def __len__(self):
+        return len(self.hf_dataset)
+
+    def __getitem__(self, idx):
+        image = self.hf_dataset[idx]['image']
+        if self.channels == 3:
+            image = image.convert('RGB')
+        image = np.frombuffer(image.tobytes(), dtype="uint8").reshape(
+            (image.height, image.width, self.channels))
+        image = ((image / 255) * 2 - 1)
+        return {'image': image}
+
+
+class AudioDiffusionDataModule(pl.LightningDataModule):
+
+    def __init__(self, model_id, batch_size, channels):
+        super().__init__()
+        self.batch_size = batch_size
+        self.dataset = AudioDiffusion(model_id=model_id, channels=channels)
+        self.num_workers = 1
+
+    def train_dataloader(self):
+        return DataLoader(self.dataset,
+                          batch_size=self.batch_size,
+                          num_workers=self.num_workers)
+
+
+class ImageLogger(Callback):
+
+    def __init__(self, every=1000, hop_length=512):
+        super().__init__()
+        self.every = every
+        self.hop_length = hop_length
+
+    @rank_zero_only
+    def log_images_and_audios(self, pl_module, batch):
+        pl_module.eval()
+        with torch.no_grad():
+            images = pl_module.log_images(batch, split='train')
+        pl_module.train()
+
+        image_shape = next(iter(images.values())).shape
+        channels = image_shape[1]
+        mel = Mel(x_res=image_shape[2],
+                  y_res=image_shape[3],
+                  hop_length=self.hop_length)
+
+        for k in images:
+            images[k] = images[k].detach().cpu()
+            images[k] = torch.clamp(images[k], -1., 1.)
+            images[k] = (images[k] + 1.0) / 2.0  # -1,1 -> 0,1; c,h,w
+            grid = torchvision.utils.make_grid(images[k])
+
+            tag = f"train/{k}"
+            pl_module.logger.experiment.add_image(
+                tag, grid, global_step=pl_module.global_step)
+
+            images[k] = (images[k].numpy() *
+                         255).round().astype("uint8").transpose(0, 2, 3, 1)
+            for _, image in enumerate(images[k]):
+                audio = mel.image_to_audio(
+                    Image.fromarray(image, mode='RGB').convert('L')
+                    if channels == 3 else Image.fromarray(image[:, :, 0]))
+                pl_module.logger.experiment.add_audio(
+                    tag + f"/{_}",
+                    normalize(audio),
+                    global_step=pl_module.global_step,
+                    sample_rate=mel.get_sample_rate())
+
+    def on_train_batch_end(self, trainer, pl_module, outputs, batch,
+                           batch_idx):
+        if (batch_idx + 1) % self.every != 0:
+            return
+        self.log_images_and_audios(pl_module, batch)
+
+
+class HFModelCheckpoint(ModelCheckpoint):
+
+    def __init__(self, ldm_config, hf_checkpoint, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.ldm_config = ldm_config
+        self.hf_checkpoint = hf_checkpoint
+
+    def on_train_epoch_end(self, trainer, pl_module):
+        ldm_checkpoint = self._get_metric_interpolated_filepath_name(
+            {'epoch': trainer.current_epoch}, trainer)
+        super().on_train_epoch_end(trainer, pl_module)
+        convert_ldm_to_hf_vae(ldm_checkpoint, self.ldm_config,
+                              self.hf_checkpoint)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Train VAE using ldm.")
+    parser.add_argument("-d", "--dataset_name", type=str, default=None)
+    parser.add_argument("-b", "--batch_size", type=int, default=1)
+    parser.add_argument("-c",
+                        "--ldm_config_file",
+                        type=str,
+                        default="config/ldm_autoencoder_kl.yaml")
+    parser.add_argument("--ldm_checkpoint_dir",
+                        type=str,
+                        default="models/ldm-autoencoder-kl")
+    parser.add_argument("--hf_checkpoint_dir",
+                        type=str,
+                        default="models/autoencoder-kl")
+    parser.add_argument("-r",
+                        "--resume_from_checkpoint",
+                        type=str,
+                        default=None)
+    parser.add_argument("-g",
+                        "--gradient_accumulation_steps",
+                        type=int,
+                        default=1)
+    parser.add_argument("--hop_length", type=int, default=512)
+    parser.add_argument("--save_images_batches", type=int, default=1000)
+    parser.add_argument("--max_epochs", type=int, default=100)
+    args = parser.parse_args()
+
+    config = OmegaConf.load(args.ldm_config_file)
+    model = instantiate_from_config(config.model)
+    model.learning_rate = config.model.base_learning_rate
+    data = AudioDiffusionDataModule(
+        model_id=args.dataset_name,
+        batch_size=args.batch_size,
+        channels=config.model.params.ddconfig.in_channels)
+    lightning_config = config.pop("lightning", OmegaConf.create())
+    trainer_config = lightning_config.get("trainer", OmegaConf.create())
+    trainer_config.accumulate_grad_batches = args.gradient_accumulation_steps
+    trainer_opt = argparse.Namespace(**trainer_config)
+    trainer = Trainer.from_argparse_args(
+        trainer_opt,
+        max_epochs=args.max_epochs,
+        resume_from_checkpoint=args.resume_from_checkpoint,
+        callbacks=[
+            ImageLogger(every=args.save_images_batches,
+                        hop_length=args.hop_length),
+            HFModelCheckpoint(ldm_config=config,
+                              hf_checkpoint=args.hf_checkpoint_dir,
+                              dirpath=args.ldm_checkpoint_dir,
+                              filename='{epoch:06}',
+                              verbose=True,
+                              save_last=True)
+        ])
+    trainer.fit(model, data)

setup.cfg

@@ -0,0 +1,19 @@
+[metadata]
+name = audiodiffusion
+version = attr: audiodiffusion.VERSION
+description = Generate Mel spectrogram dataset from directory of audio files.
+long_description = file: README.md
+license = GPL3
+classifiers =
+    Programming Language :: Python :: 3
+
+[options]
+zip_safe = False
+packages = audiodiffusion
+install_requires =
+    torch
+    numpy
+    Pillow
+    diffusers>=0.4.1
+    librosa
+    datasets

setup.py

@@ -0,0 +1,6 @@
+#!/usr/bin/env python
+
+from setuptools import setup
+
+if __name__ == "__main__":
+    setup()

streamlit_app.py

@@ -0,0 +1,37 @@
+from io import BytesIO
+import streamlit as st
+import soundfile as sf
+from librosa.util import normalize
+from librosa.beat import beat_track
+
+from audiodiffusion import AudioDiffusion
+
+if __name__ == "__main__":
+    st.header("Audio Diffusion")
+    st.markdown("Generate audio using Huggingface diffusers.\
+        This takes about 20 minutes without a GPU, so why not make yourself a \
+            cup of tea in the meantime? (Or try the teticio/audio-diffusion-ddim-256 \
+                model which is faster.)")
+
+    model_id = st.selectbox("Model", [
+        "teticio/audio-diffusion-256", "teticio/audio-diffusion-breaks-256",
+        "teticio/audio-diffusion-instrumental-hiphop-256",
+        "teticio/audio-diffusion-ddim-256"
+    ])
+    audio_diffusion = AudioDiffusion(model_id=model_id)
+
+    if st.button("Generate"):
+        st.markdown("Generating...")
+        image, (sample_rate,
+                audio) = audio_diffusion.generate_spectrogram_and_audio()
+        st.image(image, caption="Mel spectrogram")
+        buffer = BytesIO()
+        sf.write(buffer, normalize(audio), sample_rate, format="WAV")
+        st.audio(buffer, format="audio/wav")
+
+        audio = AudioDiffusion.loop_it(audio, sample_rate)
+        if audio is not None:
+            st.markdown("Loop")
+            buffer = BytesIO()
+            sf.write(buffer, normalize(audio), sample_rate, format="WAV")
+            st.audio(buffer, format="audio/wav")