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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): | |
| path = self.download() | |
| opts = onnxruntime.SessionOptions() | |
| opts.log_severity_level = 3 | |
| opts.inter_op_num_threads = 1 | |
| opts.intra_op_num_threads = 1 | |
| 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) | |
| 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() | |
| 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 | |