Update utils.py

utils.py

@@ -4,8 +4,11 @@ from typing import Iterable
 import shutil
 import subprocess
 import re
+from collections.abc import Mapping
+import torch
 
 import server
+from .logger import logger
 
 BIGMIN = -(2**53-1)
 BIGMAX = (2**53-1)
@@ -45,7 +48,7 @@ else:
     except:
         if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
             raise
-        print("Failed to import imageio_ffmpeg")
+        logger.warn("Failed to import imageio_ffmpeg")
     if "VHS_USE_IMAGEIO_FFMPEG" in os.environ:
         ffmpeg_path = imageio_ffmpeg_path
     else:
@@ -57,7 +60,7 @@ else:
         if os.path.isfile("ffmpeg.exe"):
             ffmpeg_paths.append(os.path.abspath("ffmpeg.exe"))
         if len(ffmpeg_paths) == 0:
-            print("No valid ffmpeg found.")
+            logger.error("No valid ffmpeg found.")
             ffmpeg_path = None
         elif len(ffmpeg_paths) == 1:
             #Evaluation of suitability isn't required, can take sole option
@@ -153,32 +156,51 @@ def requeue_workflow(requeue_required=(-1,True)):
         requeue_workflow_unchecked()
 
 def get_audio(file, start_time=0, duration=0):
-    args = [ffmpeg_path, "-v", "error", "-i", file]
+    args = [ffmpeg_path, "-i", file]
     if start_time > 0:
         args += ["-ss", str(start_time)]
     if duration > 0:
         args += ["-t", str(duration)]
     try:
-        res =  subprocess.run(args + ["-f", "wav", "-"],
-                              stdout=subprocess.PIPE, check=True).stdout
+        #TODO: scan for sample rate and maintain
+        res =  subprocess.run(args + ["-f", "f32le", "-"],
+                              capture_output=True, check=True)
+        audio = torch.frombuffer(bytearray(res.stdout), dtype=torch.float32)
     except subprocess.CalledProcessError as e:
-        print(f"Failed to extract audio from: {file}")
-        return False
-    return res
-
-
-def lazy_eval(func):
-    class Cache:
-        def __init__(self, func):
-            self.res = None
-            self.func = func
-        def get(self):
-            if self.res is None:
-                self.res = self.func()
-            return self.res
-    cache = Cache(func)
-    return lambda : cache.get()
+        logger.warning(f"Failed to extract audio from: {file}")
+        audio = torch.zeros(1,2)
+    match = re.search(', (\\d+) Hz, (\\w+), ',res.stderr.decode('utf-8'))
+    if match:
+        ar = int(match.group(1))
+        #NOTE: Just throwing an error for other channel types right now
+        #Will deal with issues if they come
+        ac = {"mono": 1, "stereo": 2}[match.group(2)]
+    else:
+        ar = 44100
+        ac = 2
+    audio = audio.reshape((-1,ac)).transpose(0,1).unsqueeze(0)
+    return {'waveform': audio, 'sample_rate': ar}
 
+class LazyAudioMap(Mapping):
+    def __init__(self, file, start_time, duration):
+        self.file = file
+        self.start_time=start_time
+        self.duration=duration
+        self._dict=None
+    def __getitem__(self, key):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return self._dict[key]
+    def __iter__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return iter(self._dict)
+    def __len__(self):
+        if self._dict is None:
+            self._dict = get_audio(self.file, self.start_time, self.duration)
+        return len(self._dict)
+def lazy_get_audio(file, start_time=0, duration=0):
+    return LazyAudioMap(file, start_time, duration)
 
 def is_url(url):
     return url.split("://")[0] in ["http", "https"]