Optimizing Bark with Half Precision and CPU Offloading

in

First, we can convert all the numbers in our model from full precision (which is like having an infinite number of decimal places) to half precision (which only has a finite number of decimal places). This reduces the memory footprint and speeds up inference by 50%.

Here’s how you do it:

# Import the BarkModel class from the transformers library
from transformers import BarkModel

# Import the torch library
import torch

# Check if CUDA is available and assign the device accordingly
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the BarkModel with the pretrained weights from the "suno/bark-small" model
# Convert the model's data type to half precision (torch.float16) to reduce memory footprint and speed up inference
# Move the model to the specified device (CPU or GPU)
model = BarkModel.from_pretrained("suno/bark-small", torch_dtype=torch.float16).to(device)

Now, let’s say you have this other fancy new feature called CPU offloading that allows us to use multiple cores on our computer instead of just one. This can significantly reduce the time it takes for Bark to generate speech because each sub-model in Bark is run sequentially (meaning only one at a time), so we can take advantage of parallel processing by running them simultaneously on different cores.

Here’s how you do it:

# Import the necessary libraries
from transformers import BarkModel
import torch
import numpy as np

# Check if CUDA is available and set the device accordingly
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the BarkModel from the pretrained model "suno/bark-small" and move it to the specified device
model = BarkModel.from_pretrained("suno/bark-small", torch_dtype=torch.float16).to(device)

# Define a function that takes in some text and generates speech using CPU offloading
def generate_speech(text):
    # Tokenize the input text and convert it to a tensor, then move it to the specified device
    input = torch.tensor([[tokenize(text)]], dtype=torch.long).to(device)
    # Run the BarkModel and get the generated speech as a tensor, returning a dictionary with the output
    output = model(input, return_dict=True)[0]["output"]
    # Convert the output to an array of raw audio data (in 16-bit signed integer format) and scale it for better quality
    audio = torch.nn.functional.softmax(output[0], dim=-1).cpu().numpy() * 32768
    # Split the generated speech into chunks that are roughly equal in length (to avoid any awkward pauses or stuttering) and take the first one as output
    return np.array_split(audio, int(len(text)/5))[0]

By using half precision and CPU offloading, we can significantly reduce the memory footprint and accelerate inference for Bark without sacrificing its accuracy.

SICORPS