feat(audio): use PyAV instead of ffmpeg (#31)

* feat(audio): use PyAV instead of ffmpeg

replaced usage of ffmpeg in favor of PyAV (`av`)

* refactor(audio): store all of the audio related functions in the `infer.lib.audio`

refactors previous commit to have singular functions for each task, all located in `infer.lib.audio`

* fix(audio): remove downsample_audio from mdxnet.py

it is no longer needed, since it's imported from infer.lib.audio

* docs: remove every ffmpeg mention in the documentation to avoid confusion

* chore(requirements): remove ffmpeg-python and ffmpy from all requirements

* fix(audio): fix loading for UVR

wrapped gathering of META info from the stream into a function

fixes loading for UVR

* fix(audio): use np.frombuffer() instead of direct conversion of the resampled frames

this fixes traceback on preprocessing

* feat(audio): pre-allocate decoded_audio array in the load_audio function

this should improve performance, even if just a little

* Revert "docs: remove every ffmpeg mention in the documentation to avoid confusion"

This reverts commit 1e05bbce03.

* chore(format): run black on dev

* fix(requirements): revert removal of ffmpeg in unitest.yml and Dockerfile

* Revert "fix(requirements): revert removal of ffmpeg in unitest.yml and Dockerfile"

This reverts commit e28a0eebb2.

* feat(audio): pre-allocate numpy array to store the AudioFrame data in ndarray of dtype float32

* chore(format): run black on dev

* fix(audio): fix the decoded_audio size estimation

in estimated_total_samples we multiply by `sr` instead of `container.streams.audio[0].rate` since we want to estimate size of the OUTPUT file, not the input one. - Added dynamic resizing, in case something goes wrong and the size of decoded_audio is estimated incorrectly

Fixed function `load_audio` when the input audio's samplerate does not match the desired samplerate (`sr`)

* chore(format): run black on dev

* refactor(audio): remove `clean_path()` function as it serves no purpose anymore

* docs: remove everything related to ffmpeg

this includes everything except for formats support specification in the training_tips docs, since it has nothing to do with what ffmpeg does/did but rather what audio formats are supported (all the ones that ffmpeg supports!)

* docs: fix order of the steps in preparation in the READMEs

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

infer/modules/uvr5/mdxnet.py

@@ -8,6 +8,9 @@ import numpy as np
 import soundfile as sf
 import torch
 from tqdm import tqdm
+import av
+
+from infer.lib.audio import downsample_audio
 
 cpu = torch.device("cpu")
 
@@ -218,20 +221,8 @@ class Predictor:
             sf.write(path_other, opt, rate)
             opt_path_vocal = path_vocal[:-4] + ".%s" % format
             opt_path_other = path_other[:-4] + ".%s" % format
-            if os.path.exists(path_vocal):
-                os.system(f'ffmpeg -i "{path_vocal}" -vn "{opt_path_vocal}" -q:a 2 -y')
-                if os.path.exists(opt_path_vocal):
-                    try:
-                        os.remove(path_vocal)
-                    except:
-                        pass
-            if os.path.exists(path_other):
-                os.system(f'ffmpeg -i "{path_other}" -vn "{opt_path_other}" -q:a 2 -y')
-                if os.path.exists(opt_path_other):
-                    try:
-                        os.remove(path_other)
-                    except:
-                        pass
+            downsample_audio(path_vocal, opt_path_vocal, format)
+            downsample_audio(path_other, opt_path_other, format)
 
 
 class MDXNetDereverb: