New `ffmpeg` backend changes samples when saving WAVE

🐛 Describe the bug

Snippet to reproduce the error is provided below. Adding backend="sox" or backend="soundfile" to torchaudio.save removes the issue.

import os
from tempfile import NamedTemporaryFile

os.environ["TORCHAUDIO_USE_BACKEND_DISPATCHER"] = "1"

import torch
import torchaudio

torch.manual_seed(0)

noise = torch.rand(1, 32000, dtype=torch.float32)

with NamedTemporaryFile(suffix=".wav") as f:
    torchaudio.save(f.name, noise, sample_rate=16000)
    f.flush()
    f.seek(0)
    noise_load, _ = torchaudio.load(f)
    
torch.testing.assert_close(noise_load, noise)

Output:

Traceback (most recent call last):
  File "/Users/pzelasko/Library/Application Support/JetBrains/PyCharm2023.1/scratches/scratch_12.py", line 19, in <module>
    torch.testing.assert_close(noise_load, noise)
  File "/Users/pzelasko/miniconda3/envs/lhotse-py3.10/lib/python3.10/site-packages/torch/testing/_comparison.py", line 1511, in assert_close
    raise error_metas[0].to_error(msg)
AssertionError: Tensor-likes are not close!

Mismatched elements: 9760 / 32000 (30.5%)
Greatest absolute difference: 1.52587890625e-05 at index (0, 134) (up to 1e-05 allowed)
Greatest relative difference: 1.0 at index (0, 24308) (up to 1.3e-06 allowed)

Versions

Collecting environment information...
PyTorch version: 2.0.0
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 13.3.1 (arm64)
GCC version: Could not collect
Clang version: 14.0.3 (clang-1403.0.22.14.1)
CMake version: version 3.25.0
Libc version: N/A

Python version: 3.10.4 (main, Mar 31 2022, 03:37:37) [Clang 12.0.0 ] (64-bit runtime)
Python platform: macOS-13.3.1-arm64-arm-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Apple M1 Max

Versions of relevant libraries:
[pip3] flake8==5.0.4
[pip3] k2==1.23.4.dev20230412+cpu.torch2.0.0
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.5
[pip3] torch==2.0.0
[pip3] torchaudio==2.0.0
[pip3] torchvision==0.15.0
[conda] k2 1.23.4.dev20230412+cpu.torch2.0.0 pypi_0 pypi
[conda] numpy 1.23.5 py310hb93e574_0
[conda] numpy-base 1.23.5 py310haf87e8b_0
[conda] pytorch 2.0.0 py3.10_0 pytorch
[conda] torch 1.12.1 pypi_0 pypi
[conda] torchaudio 2.0.0 py310_cpu pytorch
[conda] torchvision 0.15.0 py310_cpu pytorch

Start by running the provided Python reproducer through torchaudio.save and torchaudio.load with the default ffmpeg backend, then compare it with sox or soundfile. Trace the backend-dispatch path used by these entry points; done means saving and loading the generated WAVE preserves the samples within torch.testing.assert_close while alternate backends remain unaffected.