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pipeline_audio_diffusion.py
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pipeline_audio_diffusion.py
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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNet2DConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class AudioDiffusionPipeline(DiffusionPipeline):
"""
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
Parameters:
vqae ([`AutoencoderKL`]): Variational AutoEncoder for Latent Audio Diffusion or None
unet ([`UNet2DConditionModel`]): UNET model
mel ([`Mel`]): transform audio <-> spectrogram
scheduler ([`DDIMScheduler` or `DDPMScheduler`]): de-noising scheduler
"""
_optional_components = ["vqvae"]
def __init__(
self,
vqvae: AutoencoderKL,
unet: UNet2DConditionModel,
mel: Mel,
scheduler: Union[DDIMScheduler, DDPMScheduler],
):
super().__init__()
self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)
def get_default_steps(self) -> int:
"""Returns default number of steps recommended for inference
Returns:
`int`: number of steps
"""
return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000
@torch.no_grad()
def __call__(
self,
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,
encoding: torch.Tensor = None,
return_dict=True,
) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
"""Generate random mel spectrogram from audio input and convert to audio.
Args:
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
encoding (`torch.Tensor`): for UNet2DConditionModel shape (batch_size, seq_length, cross_attention_dim)
return_dict (`bool`): if True return AudioPipelineOutput, ImagePipelineOutput else Tuple
Returns:
`List[PIL Image]`: mel spectrograms (`float`, `List[np.ndarray]`): sample rate and raw audios
"""
steps = steps or self.get_default_steps()
self.scheduler.set_timesteps(steps)
step_generator = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size) == int:
self.unet.config.sample_size = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
noise = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
),
generator=generator,
device=self.device,
)
images = noise
mask = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(audio_file, raw_audio)
input_image = self.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 = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device)
if self.vqvae is not None:
input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample(
generator=generator
)[0]
input_images = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1])
pixels_per_second = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.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(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
if isinstance(self.unet, UNet2DConditionModel):
model_output = self.unet(images, t, encoding)["sample"]
else:
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 self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
images = 1 / self.vqvae.config.scaling_factor * 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(
(Image.fromarray(_[:, :, 0]) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(_, mode="RGB").convert("L") for _ in images)
)
audios = [self.mel.image_to_audio(_) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images))
@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.config.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)