Sound Generation with AudioLDM2 and OpenVINO™

This Jupyter notebook can be launched after a local installation only.

Github

AudioLDM 2 is a latent text-to-audio diffusion model capable of generating realistic audio samples given any text input.

AudioLDM 2 was proposed in the paper AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining by Haohe Liu et al.

The model takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.

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In this tutorial we will try out the pipeline, convert the models backing it one by one and will run an interactive app with Gradio!

Table of contents:

Prerequisites

%pip install -q accelerate "diffusers>=0.21.0" transformers "torch>=2.1" "gradio>=4.19" "peft==0.6.2" --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -q "openvino>=2024.0.0"
Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.

Instantiating Generation Pipeline

To work with AudioLDM 2 by Centre for Vision, Speech and Signal Processing - University of Surrey, we will use Hugging Face Diffusers package. Diffusers package exposes the AudioLDM2Pipeline class, simplifying the model instantiation and weights loading. The code below demonstrates how to create a AudioLDM2Pipeline and generate a text-conditioned sound sample.

from collections import namedtuple
from functools import partial
import gc
from pathlib import Path

from diffusers import AudioLDM2Pipeline
from IPython.display import Audio
import numpy as np
import openvino as ov
import torch

MODEL_ID = "cvssp/audioldm2"
pipe = AudioLDM2Pipeline.from_pretrained(MODEL_ID)

prompt = "birds singing in the forest"
negative_prompt = "Low quality"
audio = pipe(
    prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=150,
    audio_length_in_s=3.0,
).audios[0]

sampling_rate = 16000
Audio(audio, rate=sampling_rate)
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Convert models to OpenVINO Intermediate representation (IR) format

Model conversion API enables direct conversion of PyTorch models backing the pipeline. We need to provide a model object, input data for model tracing to ov.convert_model function to obtain OpenVINO ov.Model object instance. Model can be saved on disk for next deployment using ov.save_model function.

The pipeline consists of seven important parts:

  • T5 and CLAP Text Encoders for creation condition to generate an sound from a text prompt.

  • Projection model to merge outputs from the two text encoders.

  • GPT-2 language model to generate a sequence of hidden-states conditioned on the projected outputs from the two text encoders.

  • Vocoder to convert the mel-spectrogram latents to the final audio waveform.

  • Unet for step-by-step denoising latent image representation.

  • Autoencoder (VAE) for decoding latent space to image.

models_base_folder = Path("models")


def cleanup_torchscript_cache():
    """
    Helper for removing cached model representation
    """
    torch._C._jit_clear_class_registry()
    torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
    torch.jit._state._clear_class_state()

CLAP Text Encoder Conversion

First frozen text-encoder. AudioLDM2 uses the joint audio-text embedding model CLAP, specifically the laion/clap-htsat-unfused variant. The text branch is used to encode the text prompt to a prompt embedding. The full audio-text model is used to rank generated waveforms against the text prompt by computing similarity scores.

class ClapEncoderWrapper(torch.nn.Module):
    def __init__(self, encoder):
        super().__init__()
        encoder.eval()
        self.encoder = encoder

    def forward(self, input_ids, attention_mask):
        return self.encoder.get_text_features(input_ids, attention_mask)


clap_text_encoder_ir_path = models_base_folder / "clap_text_encoder.xml"

if not clap_text_encoder_ir_path.exists():
    with torch.no_grad():
        ov_model = ov.convert_model(
            ClapEncoderWrapper(pipe.text_encoder),  # model instance
            example_input={
                "input_ids": torch.ones((1, 512), dtype=torch.long),
                "attention_mask": torch.ones((1, 512), dtype=torch.long),
            },  # inputs for model tracing
        )
    ov.save_model(ov_model, clap_text_encoder_ir_path)
    del ov_model
    cleanup_torchscript_cache()
    gc.collect()
    print("Text Encoder successfully converted to IR")
else:
    print(f"Text Encoder will be loaded from {clap_text_encoder_ir_path}")
Text Encoder will be loaded from clap_text_encoder.xml

T5 Text Encoder Conversion

As second frozen text-encoder, AudioLDM2 uses the T5, specifically the google/flan-t5-large variant.

The text-encoder is responsible for transforming the input prompt, for example, “birds singing in the forest” into an embedding space that can be understood by the U-Net. It is usually a simple transformer-based encoder that maps a sequence of input tokens to a sequence of latent text embeddings.

The input of the text encoder is tensor input_ids, which contains indexes of tokens from text processed by the tokenizer and padded to the maximum length accepted by the model. Model outputs are two tensors: last_hidden_state - hidden state from the last MultiHeadAttention layer in the model and pooler_out - pooled output for whole model hidden states.

t5_text_encoder_ir_path = models_base_folder / "t5_text_encoder.xml"

if not t5_text_encoder_ir_path.exists():
    pipe.text_encoder_2.eval()
    with torch.no_grad():
        ov_model = ov.convert_model(
            pipe.text_encoder_2,  # model instance
            example_input=torch.ones((1, 7), dtype=torch.long),  # inputs for model tracing
        )
    ov.save_model(ov_model, t5_text_encoder_ir_path)
    del ov_model
    cleanup_torchscript_cache()
    gc.collect()
    print("Text Encoder successfully converted to IR")
else:
    print(f"Text Encoder will be loaded from {t5_text_encoder_ir_path}")
Text Encoder will be loaded from t5_text_encoder.xml

Projection model conversion

A trained model used to linearly project the hidden-states from the first and second text encoder models and insert learned Start Of Sequence and End Of Sequence token embeddings. The projected hidden-states from the two text encoders are concatenated to give the input to the language model.

projection_model_ir_path = models_base_folder / "projection_model.xml"

projection_model_inputs = {
    "hidden_states": torch.randn((1, 1, 512), dtype=torch.float32),
    "hidden_states_1": torch.randn((1, 7, 1024), dtype=torch.float32),
    "attention_mask": torch.ones((1, 1), dtype=torch.int64),
    "attention_mask_1": torch.ones((1, 7), dtype=torch.int64),
}

if not projection_model_ir_path.exists():
    pipe.projection_model.eval()
    with torch.no_grad():
        ov_model = ov.convert_model(
            pipe.projection_model,  # model instance
            example_input=projection_model_inputs,  # inputs for model tracing
        )
    ov.save_model(ov_model, projection_model_ir_path)
    del ov_model
    cleanup_torchscript_cache()
    gc.collect()
    print("The Projection Model successfully converted to IR")
else:
    print(f"The Projection Model will be loaded from {projection_model_ir_path}")
The Projection Model will be loaded from projection_model.xml

GPT-2 conversion

GPT-2 is an auto-regressive language model used to generate a sequence of hidden-states conditioned on the projected outputs from the two text encoders.

language_model_ir_path = models_base_folder / "language_model.xml"

language_model_inputs = {
    "inputs_embeds": torch.randn((1, 12, 768), dtype=torch.float32),
    "attention_mask": torch.ones((1, 12), dtype=torch.int64),
}

if not language_model_ir_path.exists():
    pipe.language_model.config.torchscript = True
    pipe.language_model.eval()
    pipe.language_model.__call__ = partial(
        pipe.language_model.__call__,
        kwargs={"past_key_values": None, "use_cache": False, "return_dict": False},
    )
    with torch.no_grad():
        ov_model = ov.convert_model(
            pipe.language_model,  # model instance
            example_input=language_model_inputs,  # inputs for model tracing
        )

    ov_model.inputs[0].get_node().set_partial_shape(ov.PartialShape([1, -1]))
    ov_model.inputs[0].get_node().set_element_type(ov.Type.i64)
    ov_model.inputs[1].get_node().set_partial_shape(ov.PartialShape([1, -1, 768]))
    ov_model.inputs[1].get_node().set_element_type(ov.Type.f32)

    ov_model.validate_nodes_and_infer_types()

    ov.save_model(ov_model, language_model_ir_path)
    del ov_model
    cleanup_torchscript_cache()
    gc.collect()
    print("The Projection Model successfully converted to IR")
else:
    print(f"The Projection Model will be loaded from {language_model_ir_path}")
The Projection Model will be loaded from language_model.xml

Vocoder conversion

SpeechT5 HiFi-GAN Vocoder is used to convert the mel-spectrogram latents to the final audio waveform.

vocoder_ir_path = models_base_folder / "vocoder.xml"

if not vocoder_ir_path.exists():
    pipe.vocoder.eval()
    with torch.no_grad():
        ov_model = ov.convert_model(
            pipe.vocoder,  # model instance
            example_input=torch.ones((1, 700, 64), dtype=torch.float32),  # inputs for model tracing
        )
    ov.save_model(ov_model, vocoder_ir_path)
    del ov_model
    cleanup_torchscript_cache()
    gc.collect()
    print("The Vocoder successfully converted to IR")
else:
    print(f"The Vocoder will be loaded from {vocoder_ir_path}")
The Vocoder will be loaded from vocoder.xml

UNet conversion

The UNet model is used to denoise the encoded audio latents. The process of UNet model conversion remains the same, like for original Stable Diffusion model.

unet_ir_path = models_base_folder / "unet.xml"

pipe.unet.eval()
unet_inputs = {
    "sample": torch.randn((2, 8, 75, 16), dtype=torch.float32),
    "timestep": torch.tensor(1, dtype=torch.int64),
    "encoder_hidden_states": torch.randn((2, 8, 768), dtype=torch.float32),
    "encoder_hidden_states_1": torch.randn((2, 7, 1024), dtype=torch.float32),
    "encoder_attention_mask_1": torch.ones((2, 7), dtype=torch.int64),
}

if not unet_ir_path.exists():
    with torch.no_grad():
        ov_model = ov.convert_model(pipe.unet, example_input=unet_inputs)

    ov_model.inputs[0].get_node().set_partial_shape(ov.PartialShape((2, 8, -1, 16)))
    ov_model.inputs[2].get_node().set_partial_shape(ov.PartialShape((2, 8, 768)))
    ov_model.inputs[3].get_node().set_partial_shape(ov.PartialShape((2, -1, 1024)))
    ov_model.inputs[4].get_node().set_partial_shape(ov.PartialShape((2, -1)))
    ov_model.validate_nodes_and_infer_types()

    ov.save_model(ov_model, unet_ir_path)

    del ov_model
    cleanup_torchscript_cache()
    gc.collect()
    print("Unet successfully converted to IR")
else:
    print(f"Unet will be loaded from {unet_ir_path}")
Unet will be loaded from unet.xml

VAE Decoder conversion

The VAE model has two parts, an encoder, and a decoder. The encoder is used to convert the image into a low-dimensional latent representation, which will serve as the input to the U-Net model. The decoder, conversely, transforms the latent representation back into an image.

During latent diffusion training, the encoder is used to get the latent representations (latents) of the images for the forward diffusion process, which applies more and more noise at each step. During inference, the denoised latents generated by the reverse diffusion process are converted back into images using the VAE decoder. During inference, we will see that we only need the VAE decoder. You can find instructions on how to convert the encoder part in a stable diffusion notebook.

vae_ir_path = models_base_folder / "vae.xml"


class VAEDecoderWrapper(torch.nn.Module):
    def __init__(self, vae):
        super().__init__()
        vae.eval()
        self.vae = vae

    def forward(self, latents):
        return self.vae.decode(latents)


if not vae_ir_path.exists():
    vae_decoder = VAEDecoderWrapper(pipe.vae)
    latents = torch.zeros((1, 8, 175, 16))

    vae_decoder.eval()
    with torch.no_grad():
        ov_model = ov.convert_model(vae_decoder, example_input=latents)
        ov.save_model(ov_model, vae_ir_path)
    del ov_model
    cleanup_torchscript_cache()
    gc.collect()
    print("VAE decoder successfully converted to IR")
else:
    print(f"VAE decoder will be loaded from {vae_ir_path}")
VAE decoder will be loaded from vae.xml

Select inference device for AudioLDM2 pipeline

select device from dropdown list for running inference using OpenVINO

import ipywidgets as widgets

core = ov.Core()

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value="CPU",
    description="Device:",
    disabled=False,
)

device
Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')

Adapt OpenVINO models to the original pipeline

Here we create wrapper classes for all three OpenVINO models that we want to embed in the original inference pipeline. Here are some of the things to consider when adapting an OV model: - Make sure that parameters passed by the original pipeline are forwarded to the compiled OV model properly; sometimes the OV model uses only a portion of the input arguments and some are ignored, sometimes you need to convert the argument to another data type or unwrap some data structures such as tuples or dictionaries. - Do guarantee that the wrapper class returns results to the pipeline in an expected format. In the example below you can see how we pack OV model outputs into special named tuples to adapt them for the pipeline. - Pay attention to the model method used in the original pipeline for calling the model - it may be not the forward method! Refer to the OVClapEncoderWrapper to see how we wrap OV model inference into the get_text_features method.

class OVClapEncoderWrapper:
    def __init__(self, encoder_ir, config):
        self.encoder = core.compile_model(encoder_ir, device.value)
        self.config = config

    def get_text_features(self, input_ids, attention_mask, **_):
        last_hidden_state = self.encoder([input_ids, attention_mask])[0]
        return torch.from_numpy(last_hidden_state)


class OVT5EncoderWrapper:
    def __init__(self, encoder_ir, config):
        self.encoder = core.compile_model(encoder_ir, device.value)
        self.config = config
        self.dtype = self.config.torch_dtype

    def __call__(self, input_ids, **_):
        last_hidden_state = self.encoder(input_ids)[0]
        return torch.from_numpy(last_hidden_state)[None, ...]


class OVVocoderWrapper:
    def __init__(self, vocoder_ir, config):
        self.vocoder = core.compile_model(vocoder_ir, device.value)
        self.config = config

    def __call__(self, mel_spectrogram, **_):
        waveform = self.vocoder(mel_spectrogram)[0]
        return torch.from_numpy(waveform)


class OVProjectionModelWrapper:
    def __init__(self, proj_model_ir, config):
        self.proj_model = core.compile_model(proj_model_ir, device.value)
        self.config = config
        self.output_type = namedtuple("ProjectionOutput", ["hidden_states", "attention_mask"])

    def __call__(self, hidden_states, hidden_states_1, attention_mask, attention_mask_1, **_):
        output = self.proj_model(
            {
                "hidden_states": hidden_states,
                "hidden_states_1": hidden_states_1,
                "attention_mask": attention_mask,
                "attention_mask_1": attention_mask_1,
            }
        )
        return self.output_type(torch.from_numpy(output[0]), torch.from_numpy(output[1]))


class OVUnetWrapper:
    def __init__(self, unet_ir, config):
        self.unet = core.compile_model(unet_ir, device.value)
        self.config = config

    def __call__(self, sample, timestep, encoder_hidden_states, encoder_hidden_states_1, encoder_attention_mask_1, **_):
        output = self.unet(
            {
                "sample": sample,
                "timestep": timestep,
                "encoder_hidden_states": encoder_hidden_states,
                "encoder_hidden_states_1": encoder_hidden_states_1,
                "encoder_attention_mask_1": encoder_attention_mask_1,
            }
        )
        return (torch.from_numpy(output[0]),)


class OVVaeDecoderWrapper:
    def __init__(self, vae_ir, config):
        self.vae = core.compile_model(vae_ir, device.value)
        self.config = config
        self.output_type = namedtuple("VaeOutput", ["sample"])

    def decode(self, latents, **_):
        last_hidden_state = self.vae(latents)[0]
        return self.output_type(torch.from_numpy(last_hidden_state))


def generate_language_model(gpt_2: ov.CompiledModel, inputs_embeds: torch.Tensor, attention_mask: torch.Tensor, max_new_tokens: int = 8, **_) -> torch.Tensor:
    """
    Generates a sequence of hidden-states from the language model, conditioned on the embedding inputs.
    """
    if not max_new_tokens:
        max_new_tokens = 8
    inputs_embeds = inputs_embeds.cpu().numpy()
    attention_mask = attention_mask.cpu().numpy()
    for _ in range(max_new_tokens):
        # forward pass to get next hidden states
        output = gpt_2({"inputs_embeds": inputs_embeds, "attention_mask": attention_mask})

        next_hidden_states = output[0]

        # Update the model input
        inputs_embeds = np.concatenate([inputs_embeds, next_hidden_states[:, -1:, :]], axis=1)
        attention_mask = np.concatenate([attention_mask, np.ones((attention_mask.shape[0], 1))], axis=1)
    return torch.from_numpy(inputs_embeds[:, -max_new_tokens:, :])

Now we initialize the wrapper objects and load them to the HF pipeline

pipe = AudioLDM2Pipeline.from_pretrained(MODEL_ID)
pipe.config.torchscript = True
pipe.config.return_dict = False

np.random.seed(0)
torch.manual_seed(0)

pipe.text_encoder = OVClapEncoderWrapper(clap_text_encoder_ir_path, pipe.text_encoder.config)
pipe.text_encoder_2 = OVT5EncoderWrapper(t5_text_encoder_ir_path, pipe.text_encoder_2.config)
pipe.projection_model = OVProjectionModelWrapper(projection_model_ir_path, pipe.projection_model.config)
pipe.vocoder = OVVocoderWrapper(vocoder_ir_path, pipe.vocoder.config)
pipe.unet = OVUnetWrapper(unet_ir_path, pipe.unet.config)
pipe.vae = OVVaeDecoderWrapper(vae_ir_path, pipe.vae.config)

pipe.generate_language_model = partial(generate_language_model, core.compile_model(language_model_ir_path, device.value))

gc.collect()

prompt = "birds singing in the forest"
negative_prompt = "Low quality"
audio = pipe(
    prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=150,
    audio_length_in_s=3.0,
).audios[0]

sampling_rate = 16000
Audio(audio, rate=sampling_rate)
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