transcribe/vad.py

import os
import subprocess
import warnings

import numpy as np
import onnxruntime
import torch


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()
        if '16k' in path:
            warnings.warn('This model support only 16000 sampling rate!')
            self.sample_rates = [16000]
        else:
            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._state = torch.zeros((2, batch_size, 128)).float()
        self._context = torch.zeros(0)
        self._last_sr = 0
        self._last_batch_size = 0

    def __call__(self, x, sr: int):

        x, sr = self._validate_input(x, sr)
        num_samples = 512 if sr == 16000 else 256

        if x.shape[-1] != num_samples:
            raise ValueError(
                f"Provided number of samples is {x.shape[-1]} (Supported values: 256 for 8000 sample rate, 512 for 16000)")

        batch_size = x.shape[0]
        context_size = 64 if sr == 16000 else 32

        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 not len(self._context):
            self._context = torch.zeros(batch_size, context_size)

        x = torch.cat([self._context, x], dim=1)
        if sr in [8000, 16000]:
            ort_inputs = {'input': x.numpy(), 'state': self._state.numpy(), 'sr': np.array(sr, dtype='int64')}
            ort_outs = self.session.run(None, ort_inputs)
            out, state = ort_outs
            self._state = torch.from_numpy(state)
        else:
            raise ValueError()

        self._context = x[..., -context_size:]
        self._last_sr = sr
        self._last_batch_size = batch_size

        out = torch.from_numpy(out)
        return out

    def audio_forward(self, x, sr: int):
        outs = []
        x, sr = self._validate_input(x, sr)
        self.reset_states()
        num_samples = 512 if sr == 16000 else 256

        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)

        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/v5.0/files/silero_vad.onnx"):
        target_dir = os.path.expanduser("~/.cache/silero-vad/")

        # 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
            try:
                # subprocess.run(["wget", "-O", model_filename, model_url], check=True)
                subprocess.run(["curl", "-sL", "-o", model_filename, model_url], check=True)
            except subprocess.CalledProcessError:
                print("Failed to download the model using wget.")
        return model_filename


class VoiceActivityDetector:
    def __init__(self, threshold=0.5, frame_rate=16000):
        """
        Initializes the VoiceActivityDetector with a voice activity detection model and a threshold.

        Args:
            threshold (float, optional): The probability threshold for detecting voice activity. Defaults to 0.5.
        """
        self.model = VoiceActivityDetection()
        self.threshold = threshold
        self.frame_rate = frame_rate

    def __call__(self, audio_frame):
        """
        Determines if the given audio frame contains speech by comparing the detected speech probability against
        the threshold.

        Args:
            audio_frame (np.ndarray): The audio frame to be analyzed for voice activity. It is expected to be a
                                      NumPy array of audio samples.

        Returns:
            bool: True if the speech probability exceeds the threshold, indicating the presence of voice activity;
                  False otherwise.
        """
        speech_probs = self.model.audio_forward(torch.from_numpy(audio_frame.copy()), self.frame_rate)[0]
        return torch.any(speech_probs > self.threshold).item()