optimize ComputeDecibel in fsmn-vad model by using numpy (#2174)

StevenH

2024-10-26 1254e8aee1e3200acc2b4752c6822bfc1a21b22f

optimize ComputeDecibel in fsmn-vad model by using numpy (#2174)

 funasr/models/fsmn_vad_streaming/model.py

@@ -8,6 +8,7 @@
import time
import math
import torch
import numpy as np
from torch import nn
from enum import Enum
from dataclasses import dataclass
@@ -334,18 +335,17 @@
            cache["stats"].data_buf_all = torch.cat(
                (cache["stats"].data_buf_all, cache["stats"].waveform[0])
            )
        for offset in range(
            0, cache["stats"].waveform.shape[1] - frame_sample_length + 1, frame_shift_length
        ):
            cache["stats"].decibel.append(
                10
                * math.log10(
                    (cache["stats"].waveform[0][offset : offset + frame_sample_length])
                    .square()
                    .sum()
                    + 0.000001
                )
            )
            
        waveform_numpy = cache["stats"].waveform.numpy()

        offsets = np.arange(0, waveform_numpy.shape[1] - frame_sample_length + 1, frame_shift_length)
        frames = waveform_numpy[0, offsets[:, np.newaxis] + np.arange(frame_sample_length)]

        decibel_numpy = 10 * np.log10(np.sum(np.square(frames), axis=1) + 0.000001)
        decibel_numpy = decibel_numpy.tolist()

        cache["stats"].decibel.extend(decibel_numpy)


    def ComputeScores(self, feats: torch.Tensor, cache: dict = {}) -> None:
        scores = self.encoder(feats, cache=cache["encoder"]).to("cpu")  # return B * T * D