Stop using whitenoise.wav, mp3 and torchaudio.load in sox effect test

Part of #764

 - Replace `whitenoise.wav` with on-the-fly data generation
 - Replace `torchaudio.load` with `common_utils.load_wav`
 - Replace `steam-train-whistle-daniel_simon.mp3` with `.wav`

test/common_utils/data_utils.py

@@ -21,9 +21,10 @@ def get_whitenoise(
     seed: int = 0,
     dtype: Union[str, torch.dtype] = "float32",
     device: Union[str, torch.device] = "cpu",
+    channels_first=True,
+    scale_factor: float = 1,
 ):
     """Generate pseudo audio data with whitenoise
-
     Args:
         sample_rate: Sampling rate
         duration: Length of the resulting Tensor in seconds.
@@ -32,19 +33,34 @@ def get_whitenoise(
             Note that this function does not modify global random generator state.
         dtype: Torch dtype
         device: device
+        channels_first: whether first dimension is n_channels
+        scale_factor: scale the Tensor before clamping and quantization
     Returns:
         Tensor: shape of (n_channels, sample_rate * duration)
     """
     if isinstance(dtype, str):
         dtype = getattr(torch, dtype)
-    shape = [n_channels, sample_rate * duration]
+    if dtype not in [torch.float32, torch.int32, torch.int16, torch.uint8]:
+        raise NotImplementedError(f'dtype {dtype} is not supported.')
     # According to the doc, folking rng on all CUDA devices is slow when there are many CUDA devices,
     # so we only folk on CPU, generate values and move the data to the given device
     with torch.random.fork_rng([]):
         torch.random.manual_seed(seed)
-        tensor = torch.randn(shape, dtype=dtype, device='cpu')
+        tensor = torch.randn([sample_rate * duration], dtype=torch.float32, device='cpu')
     tensor /= 2.0
+    tensor *= scale_factor
     tensor.clamp_(-1.0, 1.0)
+    if dtype == torch.int32:
+        tensor *= (tensor > 0) * 2147483647 + (tensor < 0) * 2147483648
+    if dtype == torch.int16:
+        tensor *= (tensor > 0) * 32767 + (tensor < 0) * 32768
+    if dtype == torch.uint8:
+        tensor *= (tensor > 0) * 127 + (tensor < 0) * 128
+        tensor += 128
+    tensor = tensor.to(dtype)
+    tensor = tensor.repeat([n_channels, 1])
+    if not channels_first:
+        tensor = tensor.t()
     return tensor.to(device=device)