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import soundfile
import io
from typing import Any, Tuple, Union, Optional
import numpy as np
import torch
def preprocess_wav(data: Any, incoming_sample_rate) -> Tuple[np.ndarray, int]:
segment, sample_rate = soundfile.read(
io.BytesIO(data),
dtype="float32",
always_2d=True,
frames=-1,
start=0,
format="RAW",
subtype="PCM_16",
samplerate=incoming_sample_rate,
channels=1,
)
return segment, sample_rate
def convert_waveform(
waveform: Union[np.ndarray, torch.Tensor],
sample_rate: int,
normalize_volume: bool = False,
to_mono: bool = False,
to_sample_rate: Optional[int] = None,
) -> Tuple[Union[np.ndarray, torch.Tensor], int]:
"""convert a waveform:
- to a target sample rate
- from multi-channel to mono channel
- volume normalization
Args:
waveform (numpy.ndarray or torch.Tensor): 2D original waveform
(channels x length)
sample_rate (int): original sample rate
normalize_volume (bool): perform volume normalization
to_mono (bool): convert to mono channel if having multiple channels
to_sample_rate (Optional[int]): target sample rate
Returns:
waveform (numpy.ndarray): converted 2D waveform (channels x length)
sample_rate (float): target sample rate
"""
try:
import torchaudio.sox_effects as ta_sox
except ImportError:
raise ImportError("Please install torchaudio: pip install torchaudio")
effects = []
if normalize_volume:
effects.append(["gain", "-n"])
if to_sample_rate is not None and to_sample_rate != sample_rate:
effects.append(["rate", f"{to_sample_rate}"])
if to_mono and waveform.shape[0] > 1:
effects.append(["channels", "1"])
if len(effects) > 0:
is_np_input = isinstance(waveform, np.ndarray)
_waveform = torch.from_numpy(waveform) if is_np_input else waveform
converted, converted_sample_rate = ta_sox.apply_effects_tensor(
_waveform, sample_rate, effects
)
if is_np_input:
converted = converted.numpy()
return converted, converted_sample_rate
return waveform, sample_rate |