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from typing import Optional
import scipy.io.wavfile
import torch
def normalize_wav(tensor: torch.Tensor) -> torch.Tensor:
if tensor.dtype == torch.float32:
pass
elif tensor.dtype == torch.int32:
tensor = tensor.to(torch.float32)
tensor[tensor > 0] /= 2147483647.0
tensor[tensor < 0] /= 2147483648.0
elif tensor.dtype == torch.int16:
tensor = tensor.to(torch.float32)
tensor[tensor > 0] /= 32767.0
tensor[tensor < 0] /= 32768.0
elif tensor.dtype == torch.uint8:
tensor = tensor.to(torch.float32) - 128
tensor[tensor > 0] /= 127.0
tensor[tensor < 0] /= 128.0
return tensor
def get_wav_data(
dtype: str,
num_channels: int,
*,
num_frames: Optional[int] = None,
normalize: bool = True,
channels_first: bool = True,
):
"""Generate linear signal of the given dtype and num_channels
Data range is
[-1.0, 1.0] for float32,
[-2147483648, 2147483647] for int32
[-32768, 32767] for int16
[0, 255] for uint8
num_frames allow to change the linear interpolation parameter.
Default values are 256 for uint8, else 1 << 16.
1 << 16 as default is so that int16 value range is completely covered.
"""
dtype_ = getattr(torch, dtype)
if num_frames is None:
if dtype == "uint8":
num_frames = 256
else:
num_frames = 1 << 16
if dtype == "uint8":
base = torch.linspace(0, 255, num_frames, dtype=dtype_)
elif dtype == "int8":
base = torch.linspace(-128, 127, num_frames, dtype=dtype_)
elif dtype == "float32":
base = torch.linspace(-1.0, 1.0, num_frames, dtype=dtype_)
elif dtype == "float64":
base = torch.linspace(-1.0, 1.0, num_frames, dtype=dtype_)
elif dtype == "int32":
base = torch.linspace(-2147483648, 2147483647, num_frames, dtype=dtype_)
elif dtype == "int16":
base = torch.linspace(-32768, 32767, num_frames, dtype=dtype_)
else:
raise NotImplementedError(f"Unsupported dtype {dtype}")
data = base.repeat([num_channels, 1])
if not channels_first:
data = data.transpose(1, 0)
if normalize:
data = normalize_wav(data)
return data
def load_wav(path: str, normalize=True, channels_first=True) -> torch.Tensor:
"""Load wav file without torchaudio"""
sample_rate, data = scipy.io.wavfile.read(path)
data = torch.from_numpy(data.copy())
if data.ndim == 1:
data = data.unsqueeze(1)
if normalize:
data = normalize_wav(data)
if channels_first:
data = data.transpose(1, 0)
return data, sample_rate
def save_wav(path, data, sample_rate, channels_first=True):
"""Save wav file without torchaudio"""
if channels_first:
data = data.transpose(1, 0)
scipy.io.wavfile.write(path, sample_rate, data.numpy())
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