1105 lines
54 KiB
Python
1105 lines
54 KiB
Python
# Copyright 2025 CVSSP, ByteDance and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from typing import Any, Callable, Dict, List, Optional, Union
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import numpy as np
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import torch
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from transformers import (
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ClapFeatureExtractor,
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ClapModel,
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GPT2LMHeadModel,
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RobertaTokenizer,
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RobertaTokenizerFast,
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SpeechT5HifiGan,
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T5EncoderModel,
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T5Tokenizer,
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T5TokenizerFast,
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VitsModel,
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VitsTokenizer,
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)
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from ...models import AutoencoderKL
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from ...schedulers import KarrasDiffusionSchedulers
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from ...utils import (
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is_accelerate_available,
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is_accelerate_version,
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is_librosa_available,
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logging,
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replace_example_docstring,
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)
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from ...utils.import_utils import is_transformers_version
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from ...utils.torch_utils import empty_device_cache, randn_tensor
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from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
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from .modeling_audioldm2 import AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel
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if is_librosa_available():
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import librosa
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from ...utils import is_torch_xla_available
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> import scipy
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>>> import torch
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>>> from diffusers import AudioLDM2Pipeline
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>>> repo_id = "cvssp/audioldm2"
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>>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
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>>> pipe = pipe.to("cuda")
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>>> # define the prompts
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>>> prompt = "The sound of a hammer hitting a wooden surface."
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>>> negative_prompt = "Low quality."
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>>> # set the seed for generator
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>>> generator = torch.Generator("cuda").manual_seed(0)
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>>> # run the generation
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>>> audio = pipe(
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... prompt,
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... negative_prompt=negative_prompt,
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... num_inference_steps=200,
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... audio_length_in_s=10.0,
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... num_waveforms_per_prompt=3,
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... generator=generator,
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... ).audios
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>>> # save the best audio sample (index 0) as a .wav file
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>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio[0])
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```
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```
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#Using AudioLDM2 for Text To Speech
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>>> import scipy
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>>> import torch
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>>> from diffusers import AudioLDM2Pipeline
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>>> repo_id = "anhnct/audioldm2_gigaspeech"
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>>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
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>>> pipe = pipe.to("cuda")
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>>> # define the prompts
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>>> prompt = "A female reporter is speaking"
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>>> transcript = "wish you have a good day"
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>>> # set the seed for generator
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>>> generator = torch.Generator("cuda").manual_seed(0)
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>>> # run the generation
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>>> audio = pipe(
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... prompt,
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... transcription=transcript,
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... num_inference_steps=200,
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... audio_length_in_s=10.0,
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... num_waveforms_per_prompt=2,
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... generator=generator,
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... max_new_tokens=512, #Must set max_new_tokens equa to 512 for TTS
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... ).audios
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>>> # save the best audio sample (index 0) as a .wav file
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>>> scipy.io.wavfile.write("tts.wav", rate=16000, data=audio[0])
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```
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"""
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def prepare_inputs_for_generation(
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inputs_embeds,
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attention_mask=None,
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past_key_values=None,
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**kwargs,
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):
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if past_key_values is not None:
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# only last token for inputs_embeds if past is defined in kwargs
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inputs_embeds = inputs_embeds[:, -1:]
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return {
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"inputs_embeds": inputs_embeds,
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"attention_mask": attention_mask,
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"past_key_values": past_key_values,
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"use_cache": kwargs.get("use_cache"),
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}
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class AudioLDM2Pipeline(DiffusionPipeline):
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r"""
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Pipeline for text-to-audio generation using AudioLDM2.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
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text_encoder ([`~transformers.ClapModel`]):
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First frozen text-encoder. AudioLDM2 uses the joint audio-text embedding model
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[CLAP](https://huggingface.co/docs/transformers/model_doc/clap#transformers.CLAPTextModelWithProjection),
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specifically the [laion/clap-htsat-unfused](https://huggingface.co/laion/clap-htsat-unfused) variant. The
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text branch is used to encode the text prompt to a prompt embedding. The full audio-text model is used to
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rank generated waveforms against the text prompt by computing similarity scores.
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text_encoder_2 ([`~transformers.T5EncoderModel`, `~transformers.VitsModel`]):
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Second frozen text-encoder. AudioLDM2 uses the encoder of
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[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
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[google/flan-t5-large](https://huggingface.co/google/flan-t5-large) variant. Second frozen text-encoder use
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for TTS. AudioLDM2 uses the encoder of
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[Vits](https://huggingface.co/docs/transformers/model_doc/vits#transformers.VitsModel).
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projection_model ([`AudioLDM2ProjectionModel`]):
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A trained model used to linearly project the hidden-states from the first and second text encoder models
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and insert learned SOS and EOS token embeddings. The projected hidden-states from the two text encoders are
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concatenated to give the input to the language model. A Learned Position Embedding for the Vits
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hidden-states
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language_model ([`~transformers.GPT2Model`]):
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An auto-regressive language model used to generate a sequence of hidden-states conditioned on the projected
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outputs from the two text encoders.
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tokenizer ([`~transformers.RobertaTokenizer`]):
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Tokenizer to tokenize text for the first frozen text-encoder.
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tokenizer_2 ([`~transformers.T5Tokenizer`, `~transformers.VitsTokenizer`]):
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Tokenizer to tokenize text for the second frozen text-encoder.
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feature_extractor ([`~transformers.ClapFeatureExtractor`]):
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Feature extractor to pre-process generated audio waveforms to log-mel spectrograms for automatic scoring.
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unet ([`UNet2DConditionModel`]):
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A `UNet2DConditionModel` to denoise the encoded audio latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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vocoder ([`~transformers.SpeechT5HifiGan`]):
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Vocoder of class `SpeechT5HifiGan` to convert the mel-spectrogram latents to the final audio waveform.
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"""
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: ClapModel,
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text_encoder_2: Union[T5EncoderModel, VitsModel],
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projection_model: AudioLDM2ProjectionModel,
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language_model: GPT2LMHeadModel,
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tokenizer: Union[RobertaTokenizer, RobertaTokenizerFast],
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tokenizer_2: Union[T5Tokenizer, T5TokenizerFast, VitsTokenizer],
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feature_extractor: ClapFeatureExtractor,
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unet: AudioLDM2UNet2DConditionModel,
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scheduler: KarrasDiffusionSchedulers,
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vocoder: SpeechT5HifiGan,
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):
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super().__init__()
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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text_encoder_2=text_encoder_2,
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projection_model=projection_model,
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language_model=language_model,
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tokenizer=tokenizer,
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tokenizer_2=tokenizer_2,
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feature_extractor=feature_extractor,
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unet=unet,
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scheduler=scheduler,
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vocoder=vocoder,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
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# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.enable_vae_slicing
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def enable_vae_slicing(self):
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r"""
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Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
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compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
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"""
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self.vae.enable_slicing()
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# Copied from diffusers.pipelines.pipeline_utils.StableDiffusionMixin.disable_vae_slicing
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def disable_vae_slicing(self):
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r"""
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Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
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computing decoding in one step.
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"""
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self.vae.disable_slicing()
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def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
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r"""
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Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
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to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
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method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
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`enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
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"""
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if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
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from accelerate import cpu_offload_with_hook
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else:
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raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
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torch_device = torch.device(device)
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device_index = torch_device.index
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if gpu_id is not None and device_index is not None:
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raise ValueError(
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f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}"
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f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}"
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)
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device_type = torch_device.type
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device_str = device_type
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if gpu_id or torch_device.index:
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device_str = f"{device_str}:{gpu_id or torch_device.index}"
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device = torch.device(device_str)
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if self.device.type != "cpu":
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self.to("cpu", silence_dtype_warnings=True)
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empty_device_cache(device.type)
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model_sequence = [
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self.text_encoder.text_model,
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self.text_encoder.text_projection,
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self.text_encoder_2,
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self.projection_model,
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self.language_model,
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self.unet,
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self.vae,
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self.vocoder,
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self.text_encoder,
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]
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hook = None
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for cpu_offloaded_model in model_sequence:
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_, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
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# We'll offload the last model manually.
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self.final_offload_hook = hook
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def generate_language_model(
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self,
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inputs_embeds: torch.Tensor = None,
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max_new_tokens: int = 8,
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**model_kwargs,
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):
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"""
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Generates a sequence of hidden-states from the language model, conditioned on the embedding inputs.
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Parameters:
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inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
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The sequence used as a prompt for the generation.
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max_new_tokens (`int`):
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Number of new tokens to generate.
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model_kwargs (`Dict[str, Any]`, *optional*):
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Ad hoc parametrization of additional model-specific kwargs that will be forwarded to the `forward`
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function of the model.
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Return:
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`inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
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The sequence of generated hidden-states.
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"""
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cache_position_kwargs = {}
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if is_transformers_version("<", "4.52.0.dev0"):
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cache_position_kwargs["input_ids"] = inputs_embeds
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cache_position_kwargs["model_kwargs"] = model_kwargs
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else:
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cache_position_kwargs["seq_length"] = inputs_embeds.shape[0]
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cache_position_kwargs["device"] = (
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self.language_model.device if getattr(self, "language_model", None) is not None else self.device
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)
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cache_position_kwargs["model_kwargs"] = model_kwargs
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max_new_tokens = max_new_tokens if max_new_tokens is not None else self.language_model.config.max_new_tokens
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model_kwargs = self.language_model._get_initial_cache_position(**cache_position_kwargs)
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for _ in range(max_new_tokens):
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# prepare model inputs
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model_inputs = prepare_inputs_for_generation(inputs_embeds, **model_kwargs)
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# forward pass to get next hidden states
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output = self.language_model(**model_inputs, output_hidden_states=True, return_dict=True)
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next_hidden_states = output.hidden_states[-1]
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# Update the model input
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inputs_embeds = torch.cat([inputs_embeds, next_hidden_states[:, -1:, :]], dim=1)
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# Update generated hidden states, model inputs, and length for next step
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model_kwargs = self.language_model._update_model_kwargs_for_generation(output, model_kwargs)
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return inputs_embeds[:, -max_new_tokens:, :]
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def encode_prompt(
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self,
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prompt,
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device,
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num_waveforms_per_prompt,
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do_classifier_free_guidance,
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transcription=None,
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negative_prompt=None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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generated_prompt_embeds: Optional[torch.Tensor] = None,
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negative_generated_prompt_embeds: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.LongTensor] = None,
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negative_attention_mask: Optional[torch.LongTensor] = None,
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max_new_tokens: Optional[int] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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transcription (`str` or `List[str]`):
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transcription of text to speech
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device (`torch.device`):
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torch device
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num_waveforms_per_prompt (`int`):
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number of waveforms that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the audio generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-computed text embeddings from the Flan T5 model. Can be used to easily tweak text inputs, *e.g.*
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prompt weighting. If not provided, text embeddings will be computed from `prompt` input argument.
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-computed negative text embeddings from the Flan T5 model. Can be used to easily tweak text inputs,
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*e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from
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`negative_prompt` input argument.
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generated_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings from the GPT2 language model. Can be used to easily tweak text inputs,
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*e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input
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argument.
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negative_generated_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings from the GPT2 language model. Can be used to easily tweak text
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inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from
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`negative_prompt` input argument.
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attention_mask (`torch.LongTensor`, *optional*):
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Pre-computed attention mask to be applied to the `prompt_embeds`. If not provided, attention mask will
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be computed from `prompt` input argument.
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negative_attention_mask (`torch.LongTensor`, *optional*):
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Pre-computed attention mask to be applied to the `negative_prompt_embeds`. If not provided, attention
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mask will be computed from `negative_prompt` input argument.
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max_new_tokens (`int`, *optional*, defaults to None):
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The number of new tokens to generate with the GPT2 language model.
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Returns:
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prompt_embeds (`torch.Tensor`):
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Text embeddings from the Flan T5 model.
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attention_mask (`torch.LongTensor`):
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Attention mask to be applied to the `prompt_embeds`.
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generated_prompt_embeds (`torch.Tensor`):
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Text embeddings generated from the GPT2 language model.
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Example:
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```python
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>>> import scipy
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>>> import torch
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>>> from diffusers import AudioLDM2Pipeline
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>>> repo_id = "cvssp/audioldm2"
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>>> pipe = AudioLDM2Pipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
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>>> pipe = pipe.to("cuda")
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>>> # Get text embedding vectors
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>>> prompt_embeds, attention_mask, generated_prompt_embeds = pipe.encode_prompt(
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... prompt="Techno music with a strong, upbeat tempo and high melodic riffs",
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... device="cuda",
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... do_classifier_free_guidance=True,
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... )
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>>> # Pass text embeddings to pipeline for text-conditional audio generation
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>>> audio = pipe(
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... prompt_embeds=prompt_embeds,
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... attention_mask=attention_mask,
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... generated_prompt_embeds=generated_prompt_embeds,
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... num_inference_steps=200,
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... audio_length_in_s=10.0,
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... ).audios[0]
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>>> # save generated audio sample
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>>> scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)
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```"""
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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# Define tokenizers and text encoders
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tokenizers = [self.tokenizer, self.tokenizer_2]
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is_vits_text_encoder = isinstance(self.text_encoder_2, VitsModel)
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if is_vits_text_encoder:
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text_encoders = [self.text_encoder, self.text_encoder_2.text_encoder]
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else:
|
|
text_encoders = [self.text_encoder, self.text_encoder_2]
|
|
|
|
if prompt_embeds is None:
|
|
prompt_embeds_list = []
|
|
attention_mask_list = []
|
|
|
|
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
|
|
use_prompt = isinstance(
|
|
tokenizer, (RobertaTokenizer, RobertaTokenizerFast, T5Tokenizer, T5TokenizerFast)
|
|
)
|
|
text_inputs = tokenizer(
|
|
prompt if use_prompt else transcription,
|
|
padding="max_length"
|
|
if isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast, VitsTokenizer))
|
|
else True,
|
|
max_length=tokenizer.model_max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
text_input_ids = text_inputs.input_ids
|
|
attention_mask = text_inputs.attention_mask
|
|
untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
|
|
logger.warning(
|
|
f"The following part of your input was truncated because {text_encoder.config.model_type} can "
|
|
f"only handle sequences up to {tokenizer.model_max_length} tokens: {removed_text}"
|
|
)
|
|
|
|
text_input_ids = text_input_ids.to(device)
|
|
attention_mask = attention_mask.to(device)
|
|
|
|
if text_encoder.config.model_type == "clap":
|
|
prompt_embeds = text_encoder.get_text_features(
|
|
text_input_ids,
|
|
attention_mask=attention_mask,
|
|
)
|
|
# append the seq-len dim: (bs, hidden_size) -> (bs, seq_len, hidden_size)
|
|
prompt_embeds = prompt_embeds[:, None, :]
|
|
# make sure that we attend to this single hidden-state
|
|
attention_mask = attention_mask.new_ones((batch_size, 1))
|
|
elif is_vits_text_encoder:
|
|
# Add end_token_id and attention mask in the end of sequence phonemes
|
|
for text_input_id, text_attention_mask in zip(text_input_ids, attention_mask):
|
|
for idx, phoneme_id in enumerate(text_input_id):
|
|
if phoneme_id == 0:
|
|
text_input_id[idx] = 182
|
|
text_attention_mask[idx] = 1
|
|
break
|
|
prompt_embeds = text_encoder(
|
|
text_input_ids, attention_mask=attention_mask, padding_mask=attention_mask.unsqueeze(-1)
|
|
)
|
|
prompt_embeds = prompt_embeds[0]
|
|
else:
|
|
prompt_embeds = text_encoder(
|
|
text_input_ids,
|
|
attention_mask=attention_mask,
|
|
)
|
|
prompt_embeds = prompt_embeds[0]
|
|
|
|
prompt_embeds_list.append(prompt_embeds)
|
|
attention_mask_list.append(attention_mask)
|
|
|
|
projection_output = self.projection_model(
|
|
hidden_states=prompt_embeds_list[0],
|
|
hidden_states_1=prompt_embeds_list[1],
|
|
attention_mask=attention_mask_list[0],
|
|
attention_mask_1=attention_mask_list[1],
|
|
)
|
|
projected_prompt_embeds = projection_output.hidden_states
|
|
projected_attention_mask = projection_output.attention_mask
|
|
|
|
generated_prompt_embeds = self.generate_language_model(
|
|
projected_prompt_embeds,
|
|
attention_mask=projected_attention_mask,
|
|
max_new_tokens=max_new_tokens,
|
|
)
|
|
|
|
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
|
attention_mask = (
|
|
attention_mask.to(device=device)
|
|
if attention_mask is not None
|
|
else torch.ones(prompt_embeds.shape[:2], dtype=torch.long, device=device)
|
|
)
|
|
generated_prompt_embeds = generated_prompt_embeds.to(dtype=self.language_model.dtype, device=device)
|
|
|
|
bs_embed, seq_len, hidden_size = 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, 1)
|
|
prompt_embeds = prompt_embeds.view(bs_embed * num_waveforms_per_prompt, seq_len, hidden_size)
|
|
|
|
# duplicate attention mask for each generation per prompt
|
|
attention_mask = attention_mask.repeat(1, num_waveforms_per_prompt)
|
|
attention_mask = attention_mask.view(bs_embed * num_waveforms_per_prompt, seq_len)
|
|
|
|
bs_embed, seq_len, hidden_size = generated_prompt_embeds.shape
|
|
# duplicate generated embeddings for each generation per prompt, using mps friendly method
|
|
generated_prompt_embeds = generated_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1)
|
|
generated_prompt_embeds = generated_prompt_embeds.view(
|
|
bs_embed * num_waveforms_per_prompt, seq_len, hidden_size
|
|
)
|
|
|
|
# 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
|
|
|
|
negative_prompt_embeds_list = []
|
|
negative_attention_mask_list = []
|
|
max_length = prompt_embeds.shape[1]
|
|
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
|
|
uncond_input = tokenizer(
|
|
uncond_tokens,
|
|
padding="max_length",
|
|
max_length=tokenizer.model_max_length
|
|
if isinstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast, VitsTokenizer))
|
|
else max_length,
|
|
truncation=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
uncond_input_ids = uncond_input.input_ids.to(device)
|
|
negative_attention_mask = uncond_input.attention_mask.to(device)
|
|
|
|
if text_encoder.config.model_type == "clap":
|
|
negative_prompt_embeds = text_encoder.get_text_features(
|
|
uncond_input_ids,
|
|
attention_mask=negative_attention_mask,
|
|
)
|
|
# append the seq-len dim: (bs, hidden_size) -> (bs, seq_len, hidden_size)
|
|
negative_prompt_embeds = negative_prompt_embeds[:, None, :]
|
|
# make sure that we attend to this single hidden-state
|
|
negative_attention_mask = negative_attention_mask.new_ones((batch_size, 1))
|
|
elif is_vits_text_encoder:
|
|
negative_prompt_embeds = torch.zeros(
|
|
batch_size,
|
|
tokenizer.model_max_length,
|
|
text_encoder.config.hidden_size,
|
|
).to(dtype=self.text_encoder_2.dtype, device=device)
|
|
negative_attention_mask = torch.zeros(batch_size, tokenizer.model_max_length).to(
|
|
dtype=self.text_encoder_2.dtype, device=device
|
|
)
|
|
else:
|
|
negative_prompt_embeds = text_encoder(
|
|
uncond_input_ids,
|
|
attention_mask=negative_attention_mask,
|
|
)
|
|
negative_prompt_embeds = negative_prompt_embeds[0]
|
|
|
|
negative_prompt_embeds_list.append(negative_prompt_embeds)
|
|
negative_attention_mask_list.append(negative_attention_mask)
|
|
|
|
projection_output = self.projection_model(
|
|
hidden_states=negative_prompt_embeds_list[0],
|
|
hidden_states_1=negative_prompt_embeds_list[1],
|
|
attention_mask=negative_attention_mask_list[0],
|
|
attention_mask_1=negative_attention_mask_list[1],
|
|
)
|
|
negative_projected_prompt_embeds = projection_output.hidden_states
|
|
negative_projected_attention_mask = projection_output.attention_mask
|
|
|
|
negative_generated_prompt_embeds = self.generate_language_model(
|
|
negative_projected_prompt_embeds,
|
|
attention_mask=negative_projected_attention_mask,
|
|
max_new_tokens=max_new_tokens,
|
|
)
|
|
|
|
if do_classifier_free_guidance:
|
|
seq_len = negative_prompt_embeds.shape[1]
|
|
|
|
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
|
|
negative_attention_mask = (
|
|
negative_attention_mask.to(device=device)
|
|
if negative_attention_mask is not None
|
|
else torch.ones(negative_prompt_embeds.shape[:2], dtype=torch.long, device=device)
|
|
)
|
|
negative_generated_prompt_embeds = negative_generated_prompt_embeds.to(
|
|
dtype=self.language_model.dtype, device=device
|
|
)
|
|
|
|
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
|
|
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1)
|
|
negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_waveforms_per_prompt, seq_len, -1)
|
|
|
|
# duplicate unconditional attention mask for each generation per prompt
|
|
negative_attention_mask = negative_attention_mask.repeat(1, num_waveforms_per_prompt)
|
|
negative_attention_mask = negative_attention_mask.view(batch_size * num_waveforms_per_prompt, seq_len)
|
|
|
|
# duplicate unconditional generated embeddings for each generation per prompt
|
|
seq_len = negative_generated_prompt_embeds.shape[1]
|
|
negative_generated_prompt_embeds = negative_generated_prompt_embeds.repeat(1, num_waveforms_per_prompt, 1)
|
|
negative_generated_prompt_embeds = negative_generated_prompt_embeds.view(
|
|
batch_size * num_waveforms_per_prompt, seq_len, -1
|
|
)
|
|
|
|
# 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])
|
|
attention_mask = torch.cat([negative_attention_mask, attention_mask])
|
|
generated_prompt_embeds = torch.cat([negative_generated_prompt_embeds, generated_prompt_embeds])
|
|
|
|
return prompt_embeds, attention_mask, generated_prompt_embeds
|
|
|
|
# Copied from diffusers.pipelines.audioldm.pipeline_audioldm.AudioLDMPipeline.mel_spectrogram_to_waveform
|
|
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
|
|
|
|
def score_waveforms(self, text, audio, num_waveforms_per_prompt, device, dtype):
|
|
if not is_librosa_available():
|
|
logger.info(
|
|
"Automatic scoring of the generated audio waveforms against the input prompt text requires the "
|
|
"`librosa` package to resample the generated waveforms. Returning the audios in the order they were "
|
|
"generated. To enable automatic scoring, install `librosa` with: `pip install librosa`."
|
|
)
|
|
return audio
|
|
inputs = self.tokenizer(text, return_tensors="pt", padding=True)
|
|
resampled_audio = librosa.resample(
|
|
audio.numpy(), orig_sr=self.vocoder.config.sampling_rate, target_sr=self.feature_extractor.sampling_rate
|
|
)
|
|
inputs["input_features"] = self.feature_extractor(
|
|
list(resampled_audio), return_tensors="pt", sampling_rate=self.feature_extractor.sampling_rate
|
|
).input_features.type(dtype)
|
|
inputs = inputs.to(device)
|
|
|
|
# compute the audio-text similarity score using the CLAP model
|
|
logits_per_text = self.text_encoder(**inputs).logits_per_text
|
|
# sort by the highest matching generations per prompt
|
|
indices = torch.argsort(logits_per_text, dim=1, descending=True)[:, :num_waveforms_per_prompt]
|
|
audio = torch.index_select(audio, 0, indices.reshape(-1).cpu())
|
|
return audio
|
|
|
|
# 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://huggingface.co/papers/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,
|
|
transcription=None,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
generated_prompt_embeds=None,
|
|
negative_generated_prompt_embeds=None,
|
|
attention_mask=None,
|
|
negative_attention_mask=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 or generated_prompt_embeds is None):
|
|
raise ValueError(
|
|
"Provide either `prompt`, or `prompt_embeds` and `generated_prompt_embeds`. Cannot leave "
|
|
"`prompt` undefined without specifying both `prompt_embeds` and `generated_prompt_embeds`."
|
|
)
|
|
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."
|
|
)
|
|
elif negative_prompt_embeds is not None and negative_generated_prompt_embeds is None:
|
|
raise ValueError(
|
|
"Cannot forward `negative_prompt_embeds` without `negative_generated_prompt_embeds`. Ensure that"
|
|
"both arguments are specified"
|
|
)
|
|
|
|
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}."
|
|
)
|
|
if attention_mask is not None and attention_mask.shape != prompt_embeds.shape[:2]:
|
|
raise ValueError(
|
|
"`attention_mask should have the same batch size and sequence length as `prompt_embeds`, but got:"
|
|
f"`attention_mask: {attention_mask.shape} != `prompt_embeds` {prompt_embeds.shape}"
|
|
)
|
|
|
|
if transcription is None:
|
|
if self.text_encoder_2.config.model_type == "vits":
|
|
raise ValueError("Cannot forward without transcription. Please make sure to have transcription")
|
|
elif transcription is not None and (
|
|
not isinstance(transcription, str) and not isinstance(transcription, list)
|
|
):
|
|
raise ValueError(f"`transcription` has to be of type `str` or `list` but is {type(transcription)}")
|
|
|
|
if generated_prompt_embeds is not None and negative_generated_prompt_embeds is not None:
|
|
if generated_prompt_embeds.shape != negative_generated_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`generated_prompt_embeds` and `negative_generated_prompt_embeds` must have the same shape when "
|
|
f"passed directly, but got: `generated_prompt_embeds` {generated_prompt_embeds.shape} != "
|
|
f"`negative_generated_prompt_embeds` {negative_generated_prompt_embeds.shape}."
|
|
)
|
|
if (
|
|
negative_attention_mask is not None
|
|
and negative_attention_mask.shape != negative_prompt_embeds.shape[:2]
|
|
):
|
|
raise ValueError(
|
|
"`attention_mask should have the same batch size and sequence length as `prompt_embeds`, but got:"
|
|
f"`attention_mask: {negative_attention_mask.shape} != `prompt_embeds` {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,
|
|
int(height) // self.vae_scale_factor,
|
|
int(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
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
transcription: Union[str, List[str]] = None,
|
|
audio_length_in_s: Optional[float] = None,
|
|
num_inference_steps: int = 200,
|
|
guidance_scale: float = 3.5,
|
|
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.Tensor] = None,
|
|
prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_prompt_embeds: Optional[torch.Tensor] = None,
|
|
generated_prompt_embeds: Optional[torch.Tensor] = None,
|
|
negative_generated_prompt_embeds: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.LongTensor] = None,
|
|
negative_attention_mask: Optional[torch.LongTensor] = None,
|
|
max_new_tokens: Optional[int] = None,
|
|
return_dict: bool = True,
|
|
callback: Optional[Callable[[int, int, torch.Tensor], 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`.
|
|
transcription (`str` or `List[str]`, *optional*):\
|
|
The transcript for text to speech.
|
|
audio_length_in_s (`int`, *optional*, defaults to 10.24):
|
|
The length of the generated audio sample in seconds.
|
|
num_inference_steps (`int`, *optional*, defaults to 200):
|
|
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 3.5):
|
|
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. If `num_waveforms_per_prompt > 1`, then automatic
|
|
scoring is performed between the generated outputs and the text prompt. This scoring ranks the
|
|
generated waveforms based on their cosine similarity with the text input in the joint text-audio
|
|
embedding space.
|
|
eta (`float`, *optional*, defaults to 0.0):
|
|
Corresponds to parameter eta (η) from the [DDIM](https://huggingface.co/papers/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.Tensor`, *optional*):
|
|
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for spectrogram
|
|
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.Tensor`, *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.Tensor`, *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.
|
|
generated_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated text embeddings from the GPT2 language model. 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_generated_prompt_embeds (`torch.Tensor`, *optional*):
|
|
Pre-generated negative text embeddings from the GPT2 language model. Can be used to easily tweak text
|
|
inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be computed from
|
|
`negative_prompt` input argument.
|
|
attention_mask (`torch.LongTensor`, *optional*):
|
|
Pre-computed attention mask to be applied to the `prompt_embeds`. If not provided, attention mask will
|
|
be computed from `prompt` input argument.
|
|
negative_attention_mask (`torch.LongTensor`, *optional*):
|
|
Pre-computed attention mask to be applied to the `negative_prompt_embeds`. If not provided, attention
|
|
mask will be computed from `negative_prompt` input argument.
|
|
max_new_tokens (`int`, *optional*, defaults to None):
|
|
Number of new tokens to generate with the GPT2 language model. If not provided, number of tokens will
|
|
be taken from the config of the model.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] 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.Tensor)`.
|
|
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 audio. Choose between `"np"` to return a NumPy `np.ndarray` or
|
|
`"pt"` to return a PyTorch `torch.Tensor` object. Set to `"latent"` to return the latent diffusion
|
|
model (LDM) output.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] 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,
|
|
transcription,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
generated_prompt_embeds,
|
|
negative_generated_prompt_embeds,
|
|
attention_mask,
|
|
negative_attention_mask,
|
|
)
|
|
|
|
# 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://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
do_classifier_free_guidance = guidance_scale > 1.0
|
|
|
|
# 3. Encode input prompt
|
|
prompt_embeds, attention_mask, generated_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_waveforms_per_prompt,
|
|
do_classifier_free_guidance,
|
|
transcription,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
generated_prompt_embeds=generated_prompt_embeds,
|
|
negative_generated_prompt_embeds=negative_generated_prompt_embeds,
|
|
attention_mask=attention_mask,
|
|
negative_attention_mask=negative_attention_mask,
|
|
max_new_tokens=max_new_tokens,
|
|
)
|
|
|
|
# 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=generated_prompt_embeds,
|
|
encoder_hidden_states_1=prompt_embeds,
|
|
encoder_attention_mask_1=attention_mask,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
# 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)
|
|
|
|
if XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
self.maybe_free_model_hooks()
|
|
|
|
# 8. Post-processing
|
|
if not output_type == "latent":
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
mel_spectrogram = self.vae.decode(latents).sample
|
|
else:
|
|
return AudioPipelineOutput(audios=latents)
|
|
|
|
audio = self.mel_spectrogram_to_waveform(mel_spectrogram)
|
|
|
|
audio = audio[:, :original_waveform_length]
|
|
|
|
# 9. Automatic scoring
|
|
if num_waveforms_per_prompt > 1 and prompt is not None:
|
|
audio = self.score_waveforms(
|
|
text=prompt,
|
|
audio=audio,
|
|
num_waveforms_per_prompt=num_waveforms_per_prompt,
|
|
device=device,
|
|
dtype=prompt_embeds.dtype,
|
|
)
|
|
|
|
if output_type == "np":
|
|
audio = audio.numpy()
|
|
|
|
if not return_dict:
|
|
return (audio,)
|
|
|
|
return AudioPipelineOutput(audios=audio)
|