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Piper Documentation

utils.py

Code Explained

The provided code contains two utility functions: to_gpu and audio_float_to_int16. These functions are designed to handle tensor operations and audio data normalization, respectively. Here’s a detailed explanation:

1. to_gpu

This function moves a PyTorch tensor to the GPU for accelerated computation.

  • Input:

    • x: A PyTorch tensor that needs to be transferred to the GPU.

  • Behavior:

    • The contiguous() method ensures that the tensor’s memory layout is contiguous, which is often required for efficient GPU operations.
    • The cuda(non_blocking=True) method transfers the tensor to the GPU. The non_blocking=True argument allows the transfer to occur asynchronously if the tensor is pinned in memory, improving performance in certain scenarios.

  • Output:

    • Returns the tensor in GPU memory, ready for computation.

This function is a common utility in deep learning workflows where tensors need to be processed on the GPU for faster computation.

2. audio_float_to_int16

This function normalizes audio data and converts it from floating-point format to 16-bit integer format.

  • Inputs:

    • audio: A NumPy array representing the audio signal in floating-point format.
    • max_wav_value: A scaling factor, typically set to 32767.0, which corresponds to the maximum value for 16-bit audio.

  • Behavior:

    • The audio signal is normalized by scaling it to the range defined by max_wav_value. The normalization ensures that the audio’s amplitude fits within the 16-bit integer range.
    • The np.clip function ensures that the values are bounded between -max_wav_value and max_wav_value, preventing overflow.
    • The astype("int16") method converts the normalized audio to 16-bit integer format.

  • Output:

    • Returns the audio signal as a NumPy array in 16-bit integer format.

This function is particularly useful in audio processing pipelines where audio data needs to be prepared for storage, playback, or further processing in a standard format.

Use Cases

  • to_gpu: Used in machine learning models to transfer tensors to the GPU for training or inference.
  • audio_float_to_int16: Used in audio preprocessing pipelines to convert raw audio data into a format compatible with audio playback systems or storage formats like WAV files.

Both functions are lightweight and efficient, making them essential utilities in their respective domains.

Source Code

import numpy as np
import torch




def to_gpu(x: torch.Tensor) -> torch.Tensor:
    return x.contiguous().cuda(non_blocking=True)




def audio_float_to_int16(
    audio: np.ndarray, max_wav_value: float = 32767.0
) -> np.ndarray:
    """Normalize audio and convert to int16 range"""
    audio_norm = audio * (max_wav_value / max(0.01, np.max(np.abs(audio))))
    audio_norm = np.clip(audio_norm, -max_wav_value, max_wav_value)
    audio_norm = audio_norm.astype("int16")
    return audio_norm