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# original: https://github.com/snakers4/silero-vad/blob/master/utils_vad.py
import os
import subprocess
import torch
import numpy as np
import onnxruntime
class VoiceActivityDetection():
def __init__(self, force_onnx_cpu=True):
print("downloading ONNX model...")
path = self.download()
print("loading session")
opts = onnxruntime.SessionOptions()
opts.log_severity_level = 3
opts.inter_op_num_threads = 1
opts.intra_op_num_threads = 1
print("loading onnx model")
if force_onnx_cpu and 'CPUExecutionProvider' in onnxruntime.get_available_providers():
self.session = onnxruntime.InferenceSession(path, providers=['CPUExecutionProvider'], sess_options=opts)
else:
self.session = onnxruntime.InferenceSession(path, providers=['CUDAExecutionProvider'], sess_options=opts)
print("reset states")
self.reset_states()
self.sample_rates = [8000, 16000]
def _validate_input(self, x, sr: int):
if x.dim() == 1:
x = x.unsqueeze(0)
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 sr not in self.sample_rates:
raise ValueError(f"Supported sampling rates: {self.sample_rates} (or multiply of 16000)")
if sr / x.shape[1] > 31.25:
raise ValueError("Input audio chunk is too short")
return x, sr
def reset_states(self, batch_size=1):
self._h = np.zeros((2, batch_size, 64)).astype('float32')
self._c = np.zeros((2, batch_size, 64)).astype('float32')
self._last_sr = 0
self._last_batch_size = 0
def __call__(self, x, sr: int):
x, sr = self._validate_input(x, sr)
batch_size = x.shape[0]
if not self._last_batch_size:
self.reset_states(batch_size)
if (self._last_sr) and (self._last_sr != sr):
self.reset_states(batch_size)
if (self._last_batch_size) and (self._last_batch_size != batch_size):
self.reset_states(batch_size)
if sr in [8000, 16000]:
ort_inputs = {'input': x.numpy(), 'h': self._h, 'c': self._c, 'sr': np.array(sr, dtype='int64')}
ort_outs = self.session.run(None, ort_inputs)
out, self._h, self._c = ort_outs
else:
raise ValueError()
self._last_sr = sr
self._last_batch_size = batch_size
out = torch.tensor(out)
return out
def audio_forward(self, x, sr: int, num_samples: int = 512):
outs = []
x, sr = self._validate_input(x, sr)
if x.shape[1] % num_samples:
pad_num = num_samples - (x.shape[1] % num_samples)
x = torch.nn.functional.pad(x, (0, pad_num), 'constant', value=0.0)
self.reset_states(x.shape[0])
for i in range(0, x.shape[1], num_samples):
wavs_batch = x[:, i:i+num_samples]
out_chunk = self.__call__(wavs_batch, sr)
outs.append(out_chunk)
stacked = torch.cat(outs, dim=1)
return stacked.cpu()
@staticmethod
def download(model_url="https://github.com/snakers4/silero-vad/raw/master/files/silero_vad.onnx"):
target_dir = os.path.expanduser("~/.cache/whisper-live/")
# Ensure the target directory exists
os.makedirs(target_dir, exist_ok=True)
# Define the target file path
model_filename = os.path.join(target_dir, "silero_vad.onnx")
# Check if the model file already exists
if not os.path.exists(model_filename):
# If it doesn't exist, download the model using wget
print("Downloading VAD ONNX model...")
try:
subprocess.run(["wget", "-O", model_filename, model_url], check=True)
except subprocess.CalledProcessError:
print("Failed to download the model using wget.")
return model_filename
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