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import sys
from typing import Tuple, Union
import librosa
import numpy as np
import torch
import torchaudio
def stereo_to_mono(audio: torch.Tensor) -> torch.Tensor:
r"""Converts a stereo audio tensor to mono by taking the mean across channels.
Args:
audio (torch.Tensor): Input audio tensor of shape (channels, samples).
Returns:
torch.Tensor: Mono audio tensor of shape (1, samples).
"""
return torch.mean(audio, 0, True)
def resample(wav: np.ndarray, orig_sr: int, target_sr: int) -> np.ndarray:
r"""Resamples an audio waveform from the original sampling rate to the target sampling rate.
Args:
wav (np.ndarray): The audio waveform to be resampled.
orig_sr (int): The original sampling rate of the audio waveform.
target_sr (int): The target sampling rate to resample the audio waveform to.
Returns:
np.ndarray: The resampled audio waveform.
"""
return librosa.resample(wav, orig_sr=orig_sr, target_sr=target_sr)
def safe_load(path: str, sr: Union[int, None]) -> Tuple[np.ndarray, int]:
r"""Load an audio file from disk and return its content as a numpy array.
Args:
path (str): The path to the audio file.
sr (int or None): The target sampling rate. If None, the original sampling rate is used.
Returns:
Tuple[np.ndarray, int]: A tuple containing the audio content as a numpy array and the actual sampling rate.
"""
try:
audio, sr_actual = torchaudio.load(path) # type: ignore
if audio.shape[0] > 0:
audio = stereo_to_mono(audio)
audio = audio.squeeze(0)
if sr_actual != sr and sr is not None:
audio = resample(audio.numpy(), orig_sr=sr_actual, target_sr=sr)
sr_actual = sr
else:
audio = audio.numpy()
except Exception as e:
raise type(e)(
f"The following error happened loading the file {path} ... \n" + str(e),
).with_traceback(sys.exc_info()[2])
return audio, sr_actual
def preprocess_audio(
audio: torch.Tensor, sr_actual: int, sr: Union[int, None],
) -> Tuple[torch.Tensor, int]:
r"""Preprocesses audio by converting stereo to mono, resampling if necessary, and returning the audio tensor and sample rate.
Args:
audio (torch.Tensor): The audio tensor to preprocess.
sr_actual (int): The actual sample rate of the audio.
sr (Union[int, None]): The target sample rate to resample the audio to, if necessary.
Returns:
Tuple[torch.Tensor, int]: The preprocessed audio tensor and sample rate.
"""
try:
if audio.shape[0] > 0:
audio = stereo_to_mono(audio)
audio = audio.squeeze(0)
if sr_actual != sr and sr is not None:
audio_np = resample(audio.numpy(), orig_sr=sr_actual, target_sr=sr)
# Convert back to torch tensor
audio = torch.from_numpy(audio_np)
sr_actual = sr
except Exception as e:
raise type(e)(
f"The following error happened while processing the audio ... \n {e!s}",
).with_traceback(sys.exc_info()[2])
return audio, sr_actual
def normalize_loudness(wav: torch.Tensor) -> torch.Tensor:
r"""Normalize the loudness of an audio waveform.
Args:
wav (torch.Tensor): The input waveform.
Returns:
torch.Tensor: The normalized waveform.
Examples:
>>> wav = np.array([1.0, 2.0, 3.0])
>>> normalize_loudness(wav)
tensor([0.33333333, 0.66666667, 1. ])
"""
return wav / torch.max(torch.abs(wav))
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