Torch Pitch Shift

Warnings

• breaking Older versions of `torch-pitch-shift` had specific compatibility requirements with `torchaudio` versions. For instance, v1.2.2 and v1.2.1 introduced backwards compatibility and explicit support for `torchaudio<=0.11.0` and `torchaudio v0.11` respectively. Users experiencing issues should check their `torchaudio` version and update `torch-pitch-shift` accordingly.
  Fix: Upgrade to `torch-pitch-shift>=1.2.2` and ensure `torchaudio` is within a compatible range, or explicitly manage `torchaudio` version based on `torch-pitch-shift` release notes.
• gotcha For improved audio quality and to reduce distortion during pitch-shifting, ensure you are using `torch-pitch-shift` version 1.2.0 or newer. This version introduced the `hop_length` argument, which significantly impacts output quality.
  Fix: Upgrade to `torch-pitch-shift>=1.2.0` and consider utilizing the `hop_length` argument for better results.
• gotcha The `get_fast_shifts` utility function may fail to compute valid pitch-shift ratios for certain sample rates or transpose ranges, raising an error. This typically occurs when no efficient fractional shifts can be found for the given parameters.
  Fix: Review the `sample_rate` and the `validate_fn` (transpose range) arguments passed to `get_fast_shifts`. Adjust the range or accept that not all desired shifts may be 'fast' for specific audio characteristics.
• gotcha The library has been flagged for using dynamic code execution (e.g., `eval()`) by security analysis tools like Socket.dev. While not necessarily a vulnerability, this practice can pose security risks and may prevent the code from running in environments with strict security policies.
  Fix: Be aware of this characteristic if deploying in highly secure or constrained environments. Review the source code for specific `eval()` usages if concerns arise.
• gotcha While `torch-pitch-shift` is designed for speed with PyTorch/CUDA, performing pitch shifts on CPU can be significantly slower. Related libraries have also noted performance bottlenecks on CPU for similar operations, which might apply here.
  Fix: Prioritize GPU usage for performance-critical applications. If CPU usage is unavoidable, profile your code and consider batching operations to mitigate slowdowns.

Install

• pip install torch-pitch-shift Install latest version

Imports

• pitch_shift

  from torch_pitch_shift import pitch_shift

• get_fast_shifts

  from torch_pitch_shift import get_fast_shifts

• functional.pitch_shift

  from torch_pitch_shift import pitch_shift

  The `torch-pitch-shift` library provides its own `pitch_shift` function, which has different arguments and internal implementation from `torchaudio.functional.pitch_shift`. Ensure you are importing from `torch_pitch_shift` if you intend to use this library's specific functionality.

Quickstart

This quickstart demonstrates how to generate a dummy audio waveform, apply a pitch shift of 3 semitones using the `pitch_shift` function, and print the resulting tensor shape. In a practical application, `waveform` would be loaded from an audio file (e.g., using `torchaudio.load`).

import torch
from torch_pitch_shift import pitch_shift

# Create a dummy mono audio waveform (batch_size, channels, samples)
sample_rate = 44100
duration_seconds = 2
num_samples = sample_rate * duration_seconds
waveform = torch.randn(1, 1, num_samples, dtype=torch.float32)

# Define the pitch shift amount in semitones
shift_semitones = 3.0 # Shift up by 3 semitones

# Perform the pitch shift
pitch_shifted_waveform = pitch_shift(waveform, shift_semitones, sample_rate)

print(f"Original waveform shape: {waveform.shape}")
print(f"Pitch-shifted waveform shape: {pitch_shifted_waveform.shape}")
# In a real scenario, you would now save or play 'pitch_shifted_waveform'