add multiple of 16k sr support

utils_vad.py

@@ -29,6 +29,11 @@ class OnnxWrapper():
         if x.dim() > 2:
             raise ValueError(f"Too many dimensions for input audio chunk {x.dim()}")
 
+        if sr != 16000 and (sr % 16000 == 0):
+            step = sr // 16000
+            x = x[::step]
+            sr = 16000
+
         if x.shape[0] > 1:
             raise ValueError("Onnx model does not support batching")
 
@@ -177,6 +182,14 @@ def get_speech_timestamps(audio: torch.Tensor,
         if len(audio.shape) > 1:
             raise ValueError("More than one dimension in audio. Are you trying to process audio with 2 channels?")
 
+    if sampling_rate > 16000 and (sampling_rate % 16000 == 0):
+        step = sampling_rate // 16000
+        sampling_rate = 16000
+        audio = audio[::step]
+        warnings.warn('Sampling rate is a multiply of 16000, casting to 16000 manually!')
+    else:
+        step = 1
+
     if sampling_rate == 8000 and window_size_samples > 768:
         warnings.warn('window_size_samples is too big for 8000 sampling_rate! Better set window_size_samples to 256, 512 or 1536 for 8000 sample rate!')
     if window_size_samples not in [256, 512, 768, 1024, 1536]:
@@ -247,6 +260,10 @@ def get_speech_timestamps(audio: torch.Tensor,
         for speech_dict in speeches:
             speech_dict['start'] = round(speech_dict['start'] / sampling_rate, 1)
             speech_dict['end'] = round(speech_dict['end'] / sampling_rate, 1)
+    elif step > 1:
+        for speech_dict in speeches:
+            speech_dict['start'] *= step
+            speech_dict['end'] *= step
 
     if visualize_probs:
         make_visualization(speech_probs, window_size_samples / sampling_rate)
@@ -353,6 +370,10 @@ class VADIterator:
         self.model = model
         self.threshold = threshold
         self.sampling_rate = sampling_rate
+
+        if sampling_rate not in [8000, 16000]:
+            raise ValueError('VADIterator does not support sampling rates other than [8000, 16000]')
+
         self.min_silence_samples = sampling_rate * min_silence_duration_ms / 1000
         self.speech_pad_samples = sampling_rate * speech_pad_ms / 1000
         self.reset_states()