resolve torchaudio 2.9 utils

src/silero_vad/utils_vad.py

@@ -2,6 +2,7 @@ import torch
 import torchaudio
 from typing import Callable, List
 import warnings
+from packaging import version
 
 languages = ['ru', 'en', 'de', 'es']
 
@@ -134,40 +135,60 @@ class Validator():
         return outs
 
 
-def read_audio(path: str,
-               sampling_rate: int = 16000):
-    list_backends = torchaudio.list_audio_backends()
+def read_audio(path: str, sampling_rate: int = 16000) -> torch.Tensor:
+    ta_ver = version.parse(torchaudio.__version__)
+    if ta_ver < version.parse("2.9"):
+        try:
+            effects = [['channels', '1'],['rate', str(sampling_rate)]]
+            wav, sr = torchaudio.sox_effects.apply_effects_file(path, effects=effects)
+        except:
+            wav, sr = torchaudio.load(path)
+    else:
+        try:
+            wav, sr = torchaudio.load(path)
+        except:
+            try:
+                from torchcodec.decoders import AudioDecoder
+                samples = AudioDecoder(path).get_all_samples()
+                wav = samples.data
+                sr = samples.sample_rate
+            except ImportError:
+                raise RuntimeError(
+                    f"torchaudio version {torchaudio.__version__} requires torchcodec for audio I/O. "
+                    + "Install torchcodec or pin torchaudio < 2.9"
+                )
 
-    assert len(list_backends) > 0, 'The list of available backends is empty, please install backend manually. \
-                                    \n Recommendations: \n \tSox (UNIX OS) \n \tSoundfile (Windows OS, UNIX OS) \n \tffmpeg (Windows OS, UNIX OS)'
+    if wav.ndim > 1 and wav.size(0) > 1:
+        wav = wav.mean(dim=0, keepdim=True)
 
-    try:
-        effects = [
-            ['channels', '1'],
-            ['rate', str(sampling_rate)]
-        ]
+    if sr != sampling_rate:
+        wav = torchaudio.transforms.Resample(sr, sampling_rate)(wav)
 
-        wav, sr = torchaudio.sox_effects.apply_effects_file(path, effects=effects)
-    except:
-        wav, sr = torchaudio.load(path)
-
-        if wav.size(0) > 1:
-            wav = wav.mean(dim=0, keepdim=True)
-
-        if sr != sampling_rate:
-            transform = torchaudio.transforms.Resample(orig_freq=sr,
-                                                       new_freq=sampling_rate)
-            wav = transform(wav)
-            sr = sampling_rate
-
-    assert sr == sampling_rate
     return wav.squeeze(0)
 
 
-def save_audio(path: str,
-               tensor: torch.Tensor,
-               sampling_rate: int = 16000):
-    torchaudio.save(path, tensor.unsqueeze(0), sampling_rate, bits_per_sample=16)
+def save_audio(path: str, tensor: torch.Tensor, sampling_rate: int = 16000):
+    tensor = tensor.detach().cpu()
+    if tensor.ndim == 1:
+        tensor = tensor.unsqueeze(0)
+
+    ta_ver = version.parse(torchaudio.__version__)
+
+    try:
+        torchaudio.save(path, tensor, sampling_rate, bits_per_sample=16)
+    except Exception:
+        if ta_ver >= version.parse("2.9"):
+            try:
+                from torchcodec.encoders import AudioEncoder
+                encoder = AudioEncoder(tensor, sample_rate=16000)
+                encoder.to_file(path)
+            except ImportError:
+                raise RuntimeError(
+                    f"torchaudio version {torchaudio.__version__} requires torchcodec for saving. "
+                    + "Install torchcodec or pin torchaudio < 2.9"
+                )
+        else:
+            raise
 
 
 def init_jit_model(model_path: str,