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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. | |
import inspect | |
from typing import Any, Callable, Dict, List, Optional, Union | |
import numpy as np | |
import torch | |
import torch.nn.functional as F | |
from transformers import ClapTextModelWithProjection, RobertaTokenizer, RobertaTokenizerFast, SpeechT5HifiGan | |
from ...models import AutoencoderKL, UNet2DConditionModel | |
from ...schedulers import KarrasDiffusionSchedulers | |
from ...utils import logging, replace_example_docstring | |
from ...utils.torch_utils import randn_tensor | |
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline | |
logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
EXAMPLE_DOC_STRING = """ | |
Examples: | |
```py | |
>>> from diffusers import AudioLDMPipeline | |
>>> import torch | |
>>> import scipy | |
>>> repo_id = "cvssp/audioldm-s-full-v2" | |
>>> pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16) | |
>>> pipe = pipe.to("cuda") | |
>>> prompt = "Techno music with a strong, upbeat tempo and high melodic riffs" | |
>>> audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0] | |
>>> # save the audio sample as a .wav file | |
>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio) | |
``` | |
""" | |
class AudioLDMPipeline(DiffusionPipeline): | |
r""" | |
Pipeline for text-to-audio generation using AudioLDM. | |
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods | |
implemented for all pipelines (downloading, saving, running on a particular device, etc.). | |
Args: | |
vae ([`AutoencoderKL`]): | |
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. | |
text_encoder ([`~transformers.ClapTextModelWithProjection`]): | |
Frozen text-encoder (`ClapTextModelWithProjection`, specifically the | |
[laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant. | |
tokenizer ([`PreTrainedTokenizer`]): | |
A [`~transformers.RobertaTokenizer`] to tokenize text. | |
unet ([`UNet2DConditionModel`]): | |
A `UNet2DConditionModel` to denoise the encoded audio latents. | |
scheduler ([`SchedulerMixin`]): | |
A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of | |
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. | |
vocoder ([`~transformers.SpeechT5HifiGan`]): | |
Vocoder of class `SpeechT5HifiGan`. | |
""" | |
model_cpu_offload_seq = "text_encoder->unet->vae" | |
def __init__( | |
self, | |
vae: AutoencoderKL, | |
text_encoder: ClapTextModelWithProjection, | |
tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast], | |
unet: UNet2DConditionModel, | |
scheduler: KarrasDiffusionSchedulers, | |
vocoder: SpeechT5HifiGan, | |
): | |
super().__init__() | |
self.register_modules( | |
vae=vae, | |
text_encoder=text_encoder, | |
tokenizer=tokenizer, | |
unet=unet, | |
scheduler=scheduler, | |
vocoder=vocoder, | |
) | |
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing | |
def enable_vae_slicing(self): | |
r""" | |
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to | |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. | |
""" | |
self.vae.enable_slicing() | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing | |
def disable_vae_slicing(self): | |
r""" | |
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to | |
computing decoding in one step. | |
""" | |
self.vae.disable_slicing() | |
def _encode_prompt( | |
self, | |
prompt, | |
device, | |
num_waveforms_per_prompt, | |
do_classifier_free_guidance, | |
negative_prompt=None, | |
prompt_embeds: Optional[torch.FloatTensor] = None, | |
negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
): | |
r""" | |
Encodes the prompt into text encoder hidden states. | |
Args: | |
prompt (`str` or `List[str]`, *optional*): | |
prompt to be encoded | |
device (`torch.device`): | |
torch device | |
num_waveforms_per_prompt (`int`): | |
number of waveforms that should be generated per prompt | |
do_classifier_free_guidance (`bool`): | |
whether to use classifier free guidance or not | |
negative_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts not to guide the audio generation. If not defined, one has to pass | |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is | |
less than `1`). | |
prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not | |
provided, text embeddings will be generated from `prompt` input argument. | |
negative_prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input | |
argument. | |
""" | |
if prompt is not None and isinstance(prompt, str): | |
batch_size = 1 | |
elif prompt is not None and isinstance(prompt, list): | |
batch_size = len(prompt) | |
else: | |
batch_size = prompt_embeds.shape[0] | |
if prompt_embeds is None: | |
text_inputs = self.tokenizer( | |
prompt, | |
padding="max_length", | |
max_length=self.tokenizer.model_max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
text_input_ids = text_inputs.input_ids | |
attention_mask = text_inputs.attention_mask | |
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids | |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( | |
text_input_ids, untruncated_ids | |
): | |
removed_text = self.tokenizer.batch_decode( | |
untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] | |
) | |
logger.warning( | |
"The following part of your input was truncated because CLAP can only handle sequences up to" | |
f" {self.tokenizer.model_max_length} tokens: {removed_text}" | |
) | |
prompt_embeds = self.text_encoder( | |
text_input_ids.to(device), | |
attention_mask=attention_mask.to(device), | |
) | |
prompt_embeds = prompt_embeds.text_embeds | |
# additional L_2 normalization over each hidden-state | |
prompt_embeds = F.normalize(prompt_embeds, dim=-1) | |
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) | |
( | |
bs_embed, | |
seq_len, | |
) = prompt_embeds.shape | |
# duplicate text embeddings for each generation per prompt, using mps friendly method | |
prompt_embeds = prompt_embeds.repeat(1, num_waveforms_per_prompt) | |
prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len) | |
# get unconditional embeddings for classifier free guidance | |
if do_classifier_free_guidance and negative_prompt_embeds is None: | |
uncond_tokens: List[str] | |
if negative_prompt is None: | |
uncond_tokens = [""] * batch_size | |
elif type(prompt) is not type(negative_prompt): | |
raise TypeError( | |
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" | |
f" {type(prompt)}." | |
) | |
elif isinstance(negative_prompt, str): | |
uncond_tokens = [negative_prompt] | |
elif batch_size != len(negative_prompt): | |
raise ValueError( | |
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" | |
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" | |
" the batch size of `prompt`." | |
) | |
else: | |
uncond_tokens = negative_prompt | |
max_length = prompt_embeds.shape[1] | |
uncond_input = self.tokenizer( | |
uncond_tokens, | |
padding="max_length", | |
max_length=max_length, | |
truncation=True, | |
return_tensors="pt", | |
) | |
uncond_input_ids = uncond_input.input_ids.to(device) | |
attention_mask = uncond_input.attention_mask.to(device) | |
negative_prompt_embeds = self.text_encoder( | |
uncond_input_ids, | |
attention_mask=attention_mask, | |
) | |
negative_prompt_embeds = negative_prompt_embeds.text_embeds | |
# additional L_2 normalization over each hidden-state | |
negative_prompt_embeds = F.normalize(negative_prompt_embeds, dim=-1) | |
if do_classifier_free_guidance: | |
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method | |
seq_len = negative_prompt_embeds.shape[1] | |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device) | |
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt) | |
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len) | |
# For classifier free guidance, we need to do two forward passes. | |
# Here we concatenate the unconditional and text embeddings into a single batch | |
# to avoid doing two forward passes | |
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) | |
return prompt_embeds | |
def decode_latents(self, latents): | |
latents = 1 / self.vae.config.scaling_factor * latents | |
mel_spectrogram = self.vae.decode(latents).sample | |
return mel_spectrogram | |
def mel_spectrogram_to_waveform(self, mel_spectrogram): | |
if mel_spectrogram.dim() == 4: | |
mel_spectrogram = mel_spectrogram.squeeze(1) | |
waveform = self.vocoder(mel_spectrogram) | |
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 | |
waveform = waveform.cpu().float() | |
return waveform | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs | |
def prepare_extra_step_kwargs(self, generator, eta): | |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature | |
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. | |
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 | |
# and should be between [0, 1] | |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
extra_step_kwargs = {} | |
if accepts_eta: | |
extra_step_kwargs["eta"] = eta | |
# check if the scheduler accepts generator | |
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
if accepts_generator: | |
extra_step_kwargs["generator"] = generator | |
return extra_step_kwargs | |
def check_inputs( | |
self, | |
prompt, | |
audio_length_in_s, | |
vocoder_upsample_factor, | |
callback_steps, | |
negative_prompt=None, | |
prompt_embeds=None, | |
negative_prompt_embeds=None, | |
): | |
min_audio_length_in_s = vocoder_upsample_factor * self.vae_scale_factor | |
if audio_length_in_s < min_audio_length_in_s: | |
raise ValueError( | |
f"`audio_length_in_s` has to be a positive value greater than or equal to {min_audio_length_in_s}, but " | |
f"is {audio_length_in_s}." | |
) | |
if self.vocoder.config.model_in_dim % self.vae_scale_factor != 0: | |
raise ValueError( | |
f"The number of frequency bins in the vocoder's log-mel spectrogram has to be divisible by the " | |
f"VAE scale factor, but got {self.vocoder.config.model_in_dim} bins and a scale factor of " | |
f"{self.vae_scale_factor}." | |
) | |
if (callback_steps is None) or ( | |
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) | |
): | |
raise ValueError( | |
f"`callback_steps` has to be a positive integer but is {callback_steps} of type" | |
f" {type(callback_steps)}." | |
) | |
if prompt is not None and prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" | |
" only forward one of the two." | |
) | |
elif prompt is None and prompt_embeds is None: | |
raise ValueError( | |
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." | |
) | |
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): | |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | |
if negative_prompt is not None and negative_prompt_embeds is not None: | |
raise ValueError( | |
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" | |
f" {negative_prompt_embeds}. Please make sure to only forward one of the two." | |
) | |
if prompt_embeds is not None and negative_prompt_embeds is not None: | |
if prompt_embeds.shape != negative_prompt_embeds.shape: | |
raise ValueError( | |
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" | |
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" | |
f" {negative_prompt_embeds.shape}." | |
) | |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents with width->self.vocoder.config.model_in_dim | |
def prepare_latents(self, batch_size, num_channels_latents, height, dtype, device, generator, latents=None): | |
shape = ( | |
batch_size, | |
num_channels_latents, | |
height // self.vae_scale_factor, | |
self.vocoder.config.model_in_dim // self.vae_scale_factor, | |
) | |
if isinstance(generator, list) and len(generator) != batch_size: | |
raise ValueError( | |
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" | |
f" size of {batch_size}. Make sure the batch size matches the length of the generators." | |
) | |
if latents is None: | |
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) | |
else: | |
latents = latents.to(device) | |
# scale the initial noise by the standard deviation required by the scheduler | |
latents = latents * self.scheduler.init_noise_sigma | |
return latents | |
def __call__( | |
self, | |
prompt: Union[str, List[str]] = None, | |
audio_length_in_s: Optional[float] = None, | |
num_inference_steps: int = 10, | |
guidance_scale: float = 2.5, | |
negative_prompt: Optional[Union[str, List[str]]] = None, | |
num_waveforms_per_prompt: Optional[int] = 1, | |
eta: float = 0.0, | |
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, | |
latents: Optional[torch.FloatTensor] = None, | |
prompt_embeds: Optional[torch.FloatTensor] = None, | |
negative_prompt_embeds: Optional[torch.FloatTensor] = None, | |
return_dict: bool = True, | |
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, | |
callback_steps: Optional[int] = 1, | |
cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
output_type: Optional[str] = "np", | |
): | |
r""" | |
The call function to the pipeline for generation. | |
Args: | |
prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts to guide audio generation. If not defined, you need to pass `prompt_embeds`. | |
audio_length_in_s (`int`, *optional*, defaults to 5.12): | |
The length of the generated audio sample in seconds. | |
num_inference_steps (`int`, *optional*, defaults to 10): | |
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the | |
expense of slower inference. | |
guidance_scale (`float`, *optional*, defaults to 2.5): | |
A higher guidance scale value encourages the model to generate audio that is closely linked to the text | |
`prompt` at the expense of lower sound quality. Guidance scale is enabled when `guidance_scale > 1`. | |
negative_prompt (`str` or `List[str]`, *optional*): | |
The prompt or prompts to guide what to not include in audio generation. If not defined, you need to | |
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). | |
num_waveforms_per_prompt (`int`, *optional*, defaults to 1): | |
The number of waveforms to generate per prompt. | |
eta (`float`, *optional*, defaults to 0.0): | |
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies | |
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. | |
generator (`torch.Generator` or `List[torch.Generator]`, *optional*): | |
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make | |
generation deterministic. | |
latents (`torch.FloatTensor`, *optional*): | |
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image | |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents | |
tensor is generated by sampling using the supplied random `generator`. | |
prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not | |
provided, text embeddings are generated from the `prompt` input argument. | |
negative_prompt_embeds (`torch.FloatTensor`, *optional*): | |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If | |
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. | |
return_dict (`bool`, *optional*, defaults to `True`): | |
Whether or not to return a [`~pipelines.AudioPipelineOutput`] instead of a plain tuple. | |
callback (`Callable`, *optional*): | |
A function that calls every `callback_steps` steps during inference. The function is called with the | |
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. | |
callback_steps (`int`, *optional*, defaults to 1): | |
The frequency at which the `callback` function is called. If not specified, the callback is called at | |
every step. | |
cross_attention_kwargs (`dict`, *optional*): | |
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in | |
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | |
output_type (`str`, *optional*, defaults to `"np"`): | |
The output format of the generated image. Choose between `"np"` to return a NumPy `np.ndarray` or | |
`"pt"` to return a PyTorch `torch.Tensor` object. | |
Examples: | |
Returns: | |
[`~pipelines.AudioPipelineOutput`] or `tuple`: | |
If `return_dict` is `True`, [`~pipelines.AudioPipelineOutput`] is returned, otherwise a `tuple` is | |
returned where the first element is a list with the generated audio. | |
""" | |
# 0. Convert audio input length from seconds to spectrogram height | |
vocoder_upsample_factor = np.prod(self.vocoder.config.upsample_rates) / self.vocoder.config.sampling_rate | |
if audio_length_in_s is None: | |
audio_length_in_s = self.unet.config.sample_size * self.vae_scale_factor * vocoder_upsample_factor | |
height = int(audio_length_in_s / vocoder_upsample_factor) | |
original_waveform_length = int(audio_length_in_s * self.vocoder.config.sampling_rate) | |
if height % self.vae_scale_factor != 0: | |
height = int(np.ceil(height / self.vae_scale_factor)) * self.vae_scale_factor | |
logger.info( | |
f"Audio length in seconds {audio_length_in_s} is increased to {height * vocoder_upsample_factor} " | |
f"so that it can be handled by the model. It will be cut to {audio_length_in_s} after the " | |
f"denoising process." | |
) | |
# 1. Check inputs. Raise error if not correct | |
self.check_inputs( | |
prompt, | |
audio_length_in_s, | |
vocoder_upsample_factor, | |
callback_steps, | |
negative_prompt, | |
prompt_embeds, | |
negative_prompt_embeds, | |
) | |
# 2. Define call parameters | |
if prompt is not None and isinstance(prompt, str): | |
batch_size = 1 | |
elif prompt is not None and isinstance(prompt, list): | |
batch_size = len(prompt) | |
else: | |
batch_size = prompt_embeds.shape[0] | |
device = self._execution_device | |
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) | |
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` | |
# corresponds to doing no classifier free guidance. | |
do_classifier_free_guidance = guidance_scale > 1.0 | |
# 3. Encode input prompt | |
prompt_embeds = self._encode_prompt( | |
prompt, | |
device, | |
num_waveforms_per_prompt, | |
do_classifier_free_guidance, | |
negative_prompt, | |
prompt_embeds=prompt_embeds, | |
negative_prompt_embeds=negative_prompt_embeds, | |
) | |
# 4. Prepare timesteps | |
self.scheduler.set_timesteps(num_inference_steps, device=device) | |
timesteps = self.scheduler.timesteps | |
# 5. Prepare latent variables | |
num_channels_latents = self.unet.config.in_channels | |
latents = self.prepare_latents( | |
batch_size * num_waveforms_per_prompt, | |
num_channels_latents, | |
height, | |
prompt_embeds.dtype, | |
device, | |
generator, | |
latents, | |
) | |
# 6. Prepare extra step kwargs | |
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
# 7. Denoising loop | |
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order | |
with self.progress_bar(total=num_inference_steps) as progress_bar: | |
for i, t in enumerate(timesteps): | |
# expand the latents if we are doing classifier free guidance | |
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents | |
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
# predict the noise residual | |
noise_pred = self.unet( | |
latent_model_input, | |
t, | |
encoder_hidden_states=None, | |
class_labels=prompt_embeds, | |
cross_attention_kwargs=cross_attention_kwargs, | |
).sample | |
# perform guidance | |
if do_classifier_free_guidance: | |
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | |
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | |
# compute the previous noisy sample x_t -> x_t-1 | |
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample | |
# call the callback, if provided | |
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): | |
progress_bar.update() | |
if callback is not None and i % callback_steps == 0: | |
step_idx = i // getattr(self.scheduler, "order", 1) | |
callback(step_idx, t, latents) | |
# 8. Post-processing | |
mel_spectrogram = self.decode_latents(latents) | |
audio = self.mel_spectrogram_to_waveform(mel_spectrogram) | |
audio = audio[:, :original_waveform_length] | |
if output_type == "np": | |
audio = audio.numpy() | |
if not return_dict: | |
return (audio,) | |
return AudioPipelineOutput(audios=audio) | |