first commit for takway.ai
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IrvingGao committed
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from typing import Tuple, Dict
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import copy
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import os
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from funasr.register import tables
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class LinearTransform(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(LinearTransform, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.linear = nn.Linear(input_dim, output_dim, bias=False)
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def forward(self, input):
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output = self.linear(input)
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return output
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class AffineTransform(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(AffineTransform, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.linear = nn.Linear(input_dim, output_dim)
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def forward(self, input):
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output = self.linear(input)
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return output
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class RectifiedLinear(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(RectifiedLinear, self).__init__()
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self.dim = input_dim
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self.relu = nn.ReLU()
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self.dropout = nn.Dropout(0.1)
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def forward(self, input):
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out = self.relu(input)
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return out
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class FSMNBlock(nn.Module):
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def __init__(
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self,
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input_dim: int,
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output_dim: int,
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lorder=None,
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rorder=None,
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lstride=1,
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rstride=1,
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):
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super(FSMNBlock, self).__init__()
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self.dim = input_dim
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if lorder is None:
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return
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self.lorder = lorder
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self.rorder = rorder
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self.lstride = lstride
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self.rstride = rstride
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self.conv_left = nn.Conv2d(
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self.dim, self.dim, [lorder, 1], dilation=[lstride, 1], groups=self.dim, bias=False
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)
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if self.rorder > 0:
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self.conv_right = nn.Conv2d(
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self.dim, self.dim, [rorder, 1], dilation=[rstride, 1], groups=self.dim, bias=False
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)
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else:
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self.conv_right = None
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def forward(self, input: torch.Tensor, cache: torch.Tensor):
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x = torch.unsqueeze(input, 1)
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x_per = x.permute(0, 3, 2, 1) # B D T C
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cache = cache.to(x_per.device)
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y_left = torch.cat((cache, x_per), dim=2)
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cache = y_left[:, :, -(self.lorder - 1) * self.lstride :, :]
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y_left = self.conv_left(y_left)
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out = x_per + y_left
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if self.conv_right is not None:
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# maybe need to check
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y_right = F.pad(x_per, [0, 0, 0, self.rorder * self.rstride])
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y_right = y_right[:, :, self.rstride :, :]
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y_right = self.conv_right(y_right)
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out += y_right
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out_per = out.permute(0, 3, 2, 1)
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output = out_per.squeeze(1)
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return output, cache
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class BasicBlock(nn.Module):
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def __init__(
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self,
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linear_dim: int,
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proj_dim: int,
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lorder: int,
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rorder: int,
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lstride: int,
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rstride: int,
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stack_layer: int,
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):
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super(BasicBlock, self).__init__()
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self.lorder = lorder
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self.rorder = rorder
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self.lstride = lstride
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self.rstride = rstride
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self.stack_layer = stack_layer
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self.linear = LinearTransform(linear_dim, proj_dim)
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self.fsmn_block = FSMNBlock(proj_dim, proj_dim, lorder, rorder, lstride, rstride)
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self.affine = AffineTransform(proj_dim, linear_dim)
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self.relu = RectifiedLinear(linear_dim, linear_dim)
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def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]):
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x1 = self.linear(input) # B T D
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cache_layer_name = "cache_layer_{}".format(self.stack_layer)
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if cache_layer_name not in cache:
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cache[cache_layer_name] = torch.zeros(
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x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1
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)
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x2, cache[cache_layer_name] = self.fsmn_block(x1, cache[cache_layer_name])
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x3 = self.affine(x2)
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x4 = self.relu(x3)
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return x4
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class BasicBlock_export(nn.Module):
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def __init__(
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self,
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model,
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):
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super(BasicBlock_export, self).__init__()
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self.linear = model.linear
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self.fsmn_block = model.fsmn_block
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self.affine = model.affine
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self.relu = model.relu
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def forward(self, input: torch.Tensor, in_cache: torch.Tensor):
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x = self.linear(input) # B T D
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# cache_layer_name = 'cache_layer_{}'.format(self.stack_layer)
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# if cache_layer_name not in in_cache:
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# in_cache[cache_layer_name] = torch.zeros(x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1)
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x, out_cache = self.fsmn_block(x, in_cache)
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x = self.affine(x)
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x = self.relu(x)
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return x, out_cache
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class FsmnStack(nn.Sequential):
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def __init__(self, *args):
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super(FsmnStack, self).__init__(*args)
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def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]):
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x = input
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for module in self._modules.values():
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x = module(x, cache)
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return x
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"""
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FSMN net for keyword spotting
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input_dim: input dimension
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linear_dim: fsmn input dimensionll
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proj_dim: fsmn projection dimension
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lorder: fsmn left order
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rorder: fsmn right order
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num_syn: output dimension
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fsmn_layers: no. of sequential fsmn layers
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"""
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@tables.register("encoder_classes", "FSMN")
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class FSMN(nn.Module):
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def __init__(
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self,
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input_dim: int,
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input_affine_dim: int,
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fsmn_layers: int,
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linear_dim: int,
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proj_dim: int,
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lorder: int,
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rorder: int,
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lstride: int,
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rstride: int,
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output_affine_dim: int,
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output_dim: int,
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):
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super().__init__()
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self.input_dim = input_dim
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self.input_affine_dim = input_affine_dim
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self.fsmn_layers = fsmn_layers
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self.linear_dim = linear_dim
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self.proj_dim = proj_dim
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self.output_affine_dim = output_affine_dim
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self.output_dim = output_dim
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self.in_linear1 = AffineTransform(input_dim, input_affine_dim)
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self.in_linear2 = AffineTransform(input_affine_dim, linear_dim)
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self.relu = RectifiedLinear(linear_dim, linear_dim)
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self.fsmn = FsmnStack(
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*[
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BasicBlock(linear_dim, proj_dim, lorder, rorder, lstride, rstride, i)
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for i in range(fsmn_layers)
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]
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)
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self.out_linear1 = AffineTransform(linear_dim, output_affine_dim)
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self.out_linear2 = AffineTransform(output_affine_dim, output_dim)
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self.softmax = nn.Softmax(dim=-1)
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def fuse_modules(self):
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pass
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def forward(
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self, input: torch.Tensor, cache: Dict[str, torch.Tensor]
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
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"""
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Args:
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input (torch.Tensor): Input tensor (B, T, D)
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cache: when cache is not None, the forward is in streaming. The type of cache is a dict, egs,
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{'cache_layer_1': torch.Tensor(B, T1, D)}, T1 is equal to self.lorder. It is {} for the 1st frame
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"""
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x1 = self.in_linear1(input)
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x2 = self.in_linear2(x1)
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x3 = self.relu(x2)
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x4 = self.fsmn(x3, cache) # self.cache will update automatically in self.fsmn
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x5 = self.out_linear1(x4)
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x6 = self.out_linear2(x5)
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x7 = self.softmax(x6)
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return x7
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@tables.register("encoder_classes", "FSMNExport")
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class FSMNExport(nn.Module):
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def __init__(
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self,
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model,
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**kwargs,
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):
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super().__init__()
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# self.input_dim = input_dim
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# self.input_affine_dim = input_affine_dim
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# self.fsmn_layers = fsmn_layers
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# self.linear_dim = linear_dim
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# self.proj_dim = proj_dim
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# self.output_affine_dim = output_affine_dim
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# self.output_dim = output_dim
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#
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# self.in_linear1 = AffineTransform(input_dim, input_affine_dim)
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# self.in_linear2 = AffineTransform(input_affine_dim, linear_dim)
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# self.relu = RectifiedLinear(linear_dim, linear_dim)
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# self.fsmn = FsmnStack(*[BasicBlock(linear_dim, proj_dim, lorder, rorder, lstride, rstride, i) for i in
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# range(fsmn_layers)])
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# self.out_linear1 = AffineTransform(linear_dim, output_affine_dim)
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# self.out_linear2 = AffineTransform(output_affine_dim, output_dim)
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# self.softmax = nn.Softmax(dim=-1)
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self.in_linear1 = model.in_linear1
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self.in_linear2 = model.in_linear2
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self.relu = model.relu
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# self.fsmn = model.fsmn
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self.out_linear1 = model.out_linear1
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self.out_linear2 = model.out_linear2
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self.softmax = model.softmax
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self.fsmn = model.fsmn
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for i, d in enumerate(model.fsmn):
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if isinstance(d, BasicBlock):
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self.fsmn[i] = BasicBlock_export(d)
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def fuse_modules(self):
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pass
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def forward(
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self,
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input: torch.Tensor,
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*args,
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):
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"""
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Args:
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input (torch.Tensor): Input tensor (B, T, D)
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in_cache: when in_cache is not None, the forward is in streaming. The type of in_cache is a dict, egs,
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{'cache_layer_1': torch.Tensor(B, T1, D)}, T1 is equal to self.lorder. It is {} for the 1st frame
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"""
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x = self.in_linear1(input)
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x = self.in_linear2(x)
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x = self.relu(x)
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# x4 = self.fsmn(x3, in_cache) # self.in_cache will update automatically in self.fsmn
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out_caches = list()
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for i, d in enumerate(self.fsmn):
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in_cache = args[i]
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x, out_cache = d(x, in_cache)
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out_caches.append(out_cache)
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x = self.out_linear1(x)
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x = self.out_linear2(x)
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x = self.softmax(x)
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return x, out_caches
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"""
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one deep fsmn layer
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dimproj: projection dimension, input and output dimension of memory blocks
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dimlinear: dimension of mapping layer
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lorder: left order
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rorder: right order
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lstride: left stride
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rstride: right stride
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"""
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@tables.register("encoder_classes", "DFSMN")
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class DFSMN(nn.Module):
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def __init__(self, dimproj=64, dimlinear=128, lorder=20, rorder=1, lstride=1, rstride=1):
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super(DFSMN, self).__init__()
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self.lorder = lorder
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self.rorder = rorder
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self.lstride = lstride
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self.rstride = rstride
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self.expand = AffineTransform(dimproj, dimlinear)
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self.shrink = LinearTransform(dimlinear, dimproj)
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self.conv_left = nn.Conv2d(
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dimproj, dimproj, [lorder, 1], dilation=[lstride, 1], groups=dimproj, bias=False
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)
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if rorder > 0:
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self.conv_right = nn.Conv2d(
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dimproj, dimproj, [rorder, 1], dilation=[rstride, 1], groups=dimproj, bias=False
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)
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else:
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self.conv_right = None
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def forward(self, input):
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f1 = F.relu(self.expand(input))
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p1 = self.shrink(f1)
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x = torch.unsqueeze(p1, 1)
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x_per = x.permute(0, 3, 2, 1)
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y_left = F.pad(x_per, [0, 0, (self.lorder - 1) * self.lstride, 0])
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if self.conv_right is not None:
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y_right = F.pad(x_per, [0, 0, 0, (self.rorder) * self.rstride])
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y_right = y_right[:, :, self.rstride :, :]
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out = x_per + self.conv_left(y_left) + self.conv_right(y_right)
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else:
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out = x_per + self.conv_left(y_left)
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out1 = out.permute(0, 3, 2, 1)
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output = input + out1.squeeze(1)
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return output
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"""
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build stacked dfsmn layers
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"""
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def buildDFSMNRepeats(linear_dim=128, proj_dim=64, lorder=20, rorder=1, fsmn_layers=6):
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repeats = [
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nn.Sequential(DFSMN(proj_dim, linear_dim, lorder, rorder, 1, 1)) for i in range(fsmn_layers)
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]
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return nn.Sequential(*repeats)
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if __name__ == "__main__":
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fsmn = FSMN(400, 140, 4, 250, 128, 10, 2, 1, 1, 140, 2599)
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print(fsmn)
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num_params = sum(p.numel() for p in fsmn.parameters())
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print("the number of model params: {}".format(num_params))
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x = torch.zeros(128, 200, 400) # batch-size * time * dim
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y, _ = fsmn(x) # batch-size * time * dim
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print("input shape: {}".format(x.shape))
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print("output shape: {}".format(y.shape))
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print(fsmn.to_kaldi_net())
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@@ -0,0 +1,65 @@
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
|
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# MIT License (https://opensource.org/licenses/MIT)
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import types
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import torch
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from funasr.register import tables
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def export_rebuild_model(model, **kwargs):
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
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model.encoder = encoder_class(model.encoder, onnx=is_onnx)
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model.forward = types.MethodType(export_forward, model)
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model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
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model.export_input_names = types.MethodType(export_input_names, model)
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model.export_output_names = types.MethodType(export_output_names, model)
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model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
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model.export_name = types.MethodType(export_name, model)
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return model
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def export_forward(self, feats: torch.Tensor, *args, **kwargs):
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scores, out_caches = self.encoder(feats, *args)
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return scores, out_caches
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def export_dummy_inputs(self, data_in=None, frame=30):
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if data_in is None:
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speech = torch.randn(1, frame, self.encoder_conf.get("input_dim"))
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else:
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speech = None # Undo
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cache_frames = self.encoder_conf.get("lorder") + self.encoder_conf.get("rorder") - 1
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in_cache0 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
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in_cache1 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
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in_cache2 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
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in_cache3 = torch.randn(1, self.encoder_conf.get("proj_dim"), cache_frames, 1)
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return (speech, in_cache0, in_cache1, in_cache2, in_cache3)
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|
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|
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def export_input_names(self):
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return ["speech", "in_cache0", "in_cache1", "in_cache2", "in_cache3"]
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def export_output_names(self):
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return ["logits", "out_cache0", "out_cache1", "out_cache2", "out_cache3"]
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|
||||
def export_dynamic_axes(self):
|
||||
return {
|
||||
"speech": {1: "feats_length"},
|
||||
}
|
||||
|
||||
|
||||
def export_name(
|
||||
self,
|
||||
):
|
||||
return "model.onnx"
|
||||
@@ -0,0 +1,916 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- encoding: utf-8 -*-
|
||||
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
|
||||
# MIT License (https://opensource.org/licenses/MIT)
|
||||
|
||||
import os
|
||||
import json
|
||||
import time
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from enum import Enum
|
||||
from dataclasses import dataclass
|
||||
from funasr.register import tables
|
||||
from typing import List, Tuple, Dict, Any, Optional
|
||||
|
||||
from funasr.utils.datadir_writer import DatadirWriter
|
||||
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
||||
|
||||
|
||||
class VadStateMachine(Enum):
|
||||
kVadInStateStartPointNotDetected = 1
|
||||
kVadInStateInSpeechSegment = 2
|
||||
kVadInStateEndPointDetected = 3
|
||||
|
||||
|
||||
class FrameState(Enum):
|
||||
kFrameStateInvalid = -1
|
||||
kFrameStateSpeech = 1
|
||||
kFrameStateSil = 0
|
||||
|
||||
|
||||
# final voice/unvoice state per frame
|
||||
class AudioChangeState(Enum):
|
||||
kChangeStateSpeech2Speech = 0
|
||||
kChangeStateSpeech2Sil = 1
|
||||
kChangeStateSil2Sil = 2
|
||||
kChangeStateSil2Speech = 3
|
||||
kChangeStateNoBegin = 4
|
||||
kChangeStateInvalid = 5
|
||||
|
||||
|
||||
class VadDetectMode(Enum):
|
||||
kVadSingleUtteranceDetectMode = 0
|
||||
kVadMutipleUtteranceDetectMode = 1
|
||||
|
||||
|
||||
class VADXOptions:
|
||||
"""
|
||||
Author: Speech Lab of DAMO Academy, Alibaba Group
|
||||
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
|
||||
https://arxiv.org/abs/1803.05030
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sample_rate: int = 16000,
|
||||
detect_mode: int = VadDetectMode.kVadMutipleUtteranceDetectMode.value,
|
||||
snr_mode: int = 0,
|
||||
max_end_silence_time: int = 800,
|
||||
max_start_silence_time: int = 3000,
|
||||
do_start_point_detection: bool = True,
|
||||
do_end_point_detection: bool = True,
|
||||
window_size_ms: int = 200,
|
||||
sil_to_speech_time_thres: int = 150,
|
||||
speech_to_sil_time_thres: int = 150,
|
||||
speech_2_noise_ratio: float = 1.0,
|
||||
do_extend: int = 1,
|
||||
lookback_time_start_point: int = 200,
|
||||
lookahead_time_end_point: int = 100,
|
||||
max_single_segment_time: int = 60000,
|
||||
nn_eval_block_size: int = 8,
|
||||
dcd_block_size: int = 4,
|
||||
snr_thres: int = -100.0,
|
||||
noise_frame_num_used_for_snr: int = 100,
|
||||
decibel_thres: int = -100.0,
|
||||
speech_noise_thres: float = 0.6,
|
||||
fe_prior_thres: float = 1e-4,
|
||||
silence_pdf_num: int = 1,
|
||||
sil_pdf_ids: List[int] = [0],
|
||||
speech_noise_thresh_low: float = -0.1,
|
||||
speech_noise_thresh_high: float = 0.3,
|
||||
output_frame_probs: bool = False,
|
||||
frame_in_ms: int = 10,
|
||||
frame_length_ms: int = 25,
|
||||
**kwargs,
|
||||
):
|
||||
self.sample_rate = sample_rate
|
||||
self.detect_mode = detect_mode
|
||||
self.snr_mode = snr_mode
|
||||
self.max_end_silence_time = max_end_silence_time
|
||||
self.max_start_silence_time = max_start_silence_time
|
||||
self.do_start_point_detection = do_start_point_detection
|
||||
self.do_end_point_detection = do_end_point_detection
|
||||
self.window_size_ms = window_size_ms
|
||||
self.sil_to_speech_time_thres = sil_to_speech_time_thres
|
||||
self.speech_to_sil_time_thres = speech_to_sil_time_thres
|
||||
self.speech_2_noise_ratio = speech_2_noise_ratio
|
||||
self.do_extend = do_extend
|
||||
self.lookback_time_start_point = lookback_time_start_point
|
||||
self.lookahead_time_end_point = lookahead_time_end_point
|
||||
self.max_single_segment_time = max_single_segment_time
|
||||
self.nn_eval_block_size = nn_eval_block_size
|
||||
self.dcd_block_size = dcd_block_size
|
||||
self.snr_thres = snr_thres
|
||||
self.noise_frame_num_used_for_snr = noise_frame_num_used_for_snr
|
||||
self.decibel_thres = decibel_thres
|
||||
self.speech_noise_thres = speech_noise_thres
|
||||
self.fe_prior_thres = fe_prior_thres
|
||||
self.silence_pdf_num = silence_pdf_num
|
||||
self.sil_pdf_ids = sil_pdf_ids
|
||||
self.speech_noise_thresh_low = speech_noise_thresh_low
|
||||
self.speech_noise_thresh_high = speech_noise_thresh_high
|
||||
self.output_frame_probs = output_frame_probs
|
||||
self.frame_in_ms = frame_in_ms
|
||||
self.frame_length_ms = frame_length_ms
|
||||
|
||||
|
||||
class E2EVadSpeechBufWithDoa(object):
|
||||
"""
|
||||
Author: Speech Lab of DAMO Academy, Alibaba Group
|
||||
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
|
||||
https://arxiv.org/abs/1803.05030
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.start_ms = 0
|
||||
self.end_ms = 0
|
||||
self.buffer = []
|
||||
self.contain_seg_start_point = False
|
||||
self.contain_seg_end_point = False
|
||||
self.doa = 0
|
||||
|
||||
def Reset(self):
|
||||
self.start_ms = 0
|
||||
self.end_ms = 0
|
||||
self.buffer = []
|
||||
self.contain_seg_start_point = False
|
||||
self.contain_seg_end_point = False
|
||||
self.doa = 0
|
||||
|
||||
|
||||
class E2EVadFrameProb(object):
|
||||
"""
|
||||
Author: Speech Lab of DAMO Academy, Alibaba Group
|
||||
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
|
||||
https://arxiv.org/abs/1803.05030
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.noise_prob = 0.0
|
||||
self.speech_prob = 0.0
|
||||
self.score = 0.0
|
||||
self.frame_id = 0
|
||||
self.frm_state = 0
|
||||
|
||||
|
||||
class WindowDetector(object):
|
||||
"""
|
||||
Author: Speech Lab of DAMO Academy, Alibaba Group
|
||||
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
|
||||
https://arxiv.org/abs/1803.05030
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
window_size_ms: int,
|
||||
sil_to_speech_time: int,
|
||||
speech_to_sil_time: int,
|
||||
frame_size_ms: int,
|
||||
):
|
||||
self.window_size_ms = window_size_ms
|
||||
self.sil_to_speech_time = sil_to_speech_time
|
||||
self.speech_to_sil_time = speech_to_sil_time
|
||||
self.frame_size_ms = frame_size_ms
|
||||
|
||||
self.win_size_frame = int(window_size_ms / frame_size_ms)
|
||||
self.win_sum = 0
|
||||
self.win_state = [0] * self.win_size_frame # 初始化窗
|
||||
|
||||
self.cur_win_pos = 0
|
||||
self.pre_frame_state = FrameState.kFrameStateSil
|
||||
self.cur_frame_state = FrameState.kFrameStateSil
|
||||
self.sil_to_speech_frmcnt_thres = int(sil_to_speech_time / frame_size_ms)
|
||||
self.speech_to_sil_frmcnt_thres = int(speech_to_sil_time / frame_size_ms)
|
||||
|
||||
self.voice_last_frame_count = 0
|
||||
self.noise_last_frame_count = 0
|
||||
self.hydre_frame_count = 0
|
||||
|
||||
def Reset(self) -> None:
|
||||
self.cur_win_pos = 0
|
||||
self.win_sum = 0
|
||||
self.win_state = [0] * self.win_size_frame
|
||||
self.pre_frame_state = FrameState.kFrameStateSil
|
||||
self.cur_frame_state = FrameState.kFrameStateSil
|
||||
self.voice_last_frame_count = 0
|
||||
self.noise_last_frame_count = 0
|
||||
self.hydre_frame_count = 0
|
||||
|
||||
def GetWinSize(self) -> int:
|
||||
return int(self.win_size_frame)
|
||||
|
||||
def DetectOneFrame(
|
||||
self, frameState: FrameState, frame_count: int, cache: dict = {}
|
||||
) -> AudioChangeState:
|
||||
cur_frame_state = FrameState.kFrameStateSil
|
||||
if frameState == FrameState.kFrameStateSpeech:
|
||||
cur_frame_state = 1
|
||||
elif frameState == FrameState.kFrameStateSil:
|
||||
cur_frame_state = 0
|
||||
else:
|
||||
return AudioChangeState.kChangeStateInvalid
|
||||
self.win_sum -= self.win_state[self.cur_win_pos]
|
||||
self.win_sum += cur_frame_state
|
||||
self.win_state[self.cur_win_pos] = cur_frame_state
|
||||
self.cur_win_pos = (self.cur_win_pos + 1) % self.win_size_frame
|
||||
|
||||
if (
|
||||
self.pre_frame_state == FrameState.kFrameStateSil
|
||||
and self.win_sum >= self.sil_to_speech_frmcnt_thres
|
||||
):
|
||||
self.pre_frame_state = FrameState.kFrameStateSpeech
|
||||
return AudioChangeState.kChangeStateSil2Speech
|
||||
|
||||
if (
|
||||
self.pre_frame_state == FrameState.kFrameStateSpeech
|
||||
and self.win_sum <= self.speech_to_sil_frmcnt_thres
|
||||
):
|
||||
self.pre_frame_state = FrameState.kFrameStateSil
|
||||
return AudioChangeState.kChangeStateSpeech2Sil
|
||||
|
||||
if self.pre_frame_state == FrameState.kFrameStateSil:
|
||||
return AudioChangeState.kChangeStateSil2Sil
|
||||
if self.pre_frame_state == FrameState.kFrameStateSpeech:
|
||||
return AudioChangeState.kChangeStateSpeech2Speech
|
||||
return AudioChangeState.kChangeStateInvalid
|
||||
|
||||
def FrameSizeMs(self) -> int:
|
||||
return int(self.frame_size_ms)
|
||||
|
||||
|
||||
class Stats(object):
|
||||
def __init__(
|
||||
self,
|
||||
sil_pdf_ids,
|
||||
max_end_sil_frame_cnt_thresh,
|
||||
speech_noise_thres,
|
||||
):
|
||||
self.data_buf_start_frame = 0
|
||||
self.frm_cnt = 0
|
||||
self.latest_confirmed_speech_frame = 0
|
||||
self.lastest_confirmed_silence_frame = -1
|
||||
self.continous_silence_frame_count = 0
|
||||
self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
|
||||
self.confirmed_start_frame = -1
|
||||
self.confirmed_end_frame = -1
|
||||
self.number_end_time_detected = 0
|
||||
self.sil_frame = 0
|
||||
self.sil_pdf_ids = sil_pdf_ids
|
||||
self.noise_average_decibel = -100.0
|
||||
self.pre_end_silence_detected = False
|
||||
self.next_seg = True
|
||||
|
||||
self.output_data_buf = []
|
||||
self.output_data_buf_offset = 0
|
||||
self.frame_probs = []
|
||||
self.max_end_sil_frame_cnt_thresh = max_end_sil_frame_cnt_thresh
|
||||
self.speech_noise_thres = speech_noise_thres
|
||||
self.scores = None
|
||||
self.max_time_out = False
|
||||
self.decibel = []
|
||||
self.data_buf = None
|
||||
self.data_buf_all = None
|
||||
self.waveform = None
|
||||
self.last_drop_frames = 0
|
||||
|
||||
|
||||
@tables.register("model_classes", "FsmnVADStreaming")
|
||||
class FsmnVADStreaming(nn.Module):
|
||||
"""
|
||||
Author: Speech Lab of DAMO Academy, Alibaba Group
|
||||
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
|
||||
https://arxiv.org/abs/1803.05030
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
encoder: str = None,
|
||||
encoder_conf: Optional[Dict] = None,
|
||||
vad_post_args: Dict[str, Any] = None,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__()
|
||||
self.vad_opts = VADXOptions(**kwargs)
|
||||
|
||||
encoder_class = tables.encoder_classes.get(encoder)
|
||||
encoder = encoder_class(**encoder_conf)
|
||||
self.encoder = encoder
|
||||
self.encoder_conf = encoder_conf
|
||||
|
||||
def ResetDetection(self, cache: dict = {}):
|
||||
cache["stats"].continous_silence_frame_count = 0
|
||||
cache["stats"].latest_confirmed_speech_frame = 0
|
||||
cache["stats"].lastest_confirmed_silence_frame = -1
|
||||
cache["stats"].confirmed_start_frame = -1
|
||||
cache["stats"].confirmed_end_frame = -1
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
|
||||
cache["windows_detector"].Reset()
|
||||
cache["stats"].sil_frame = 0
|
||||
cache["stats"].frame_probs = []
|
||||
|
||||
if cache["stats"].output_data_buf:
|
||||
assert cache["stats"].output_data_buf[-1].contain_seg_end_point == True
|
||||
drop_frames = int(cache["stats"].output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
|
||||
real_drop_frames = drop_frames - cache["stats"].last_drop_frames
|
||||
cache["stats"].last_drop_frames = drop_frames
|
||||
cache["stats"].data_buf_all = cache["stats"].data_buf_all[
|
||||
real_drop_frames
|
||||
* int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) :
|
||||
]
|
||||
cache["stats"].decibel = cache["stats"].decibel[real_drop_frames:]
|
||||
cache["stats"].scores = cache["stats"].scores[:, real_drop_frames:, :]
|
||||
|
||||
def ComputeDecibel(self, cache: dict = {}) -> None:
|
||||
frame_sample_length = int(self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000)
|
||||
frame_shift_length = int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000)
|
||||
if cache["stats"].data_buf_all is None:
|
||||
cache["stats"].data_buf_all = cache["stats"].waveform[
|
||||
0
|
||||
] # cache["stats"].data_buf is pointed to cache["stats"].waveform[0]
|
||||
cache["stats"].data_buf = cache["stats"].data_buf_all
|
||||
else:
|
||||
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
|
||||
)
|
||||
)
|
||||
|
||||
def ComputeScores(self, feats: torch.Tensor, cache: dict = {}) -> None:
|
||||
scores = self.encoder(feats, cache=cache["encoder"]).to("cpu") # return B * T * D
|
||||
assert (
|
||||
scores.shape[1] == feats.shape[1]
|
||||
), "The shape between feats and scores does not match"
|
||||
self.vad_opts.nn_eval_block_size = scores.shape[1]
|
||||
cache["stats"].frm_cnt += scores.shape[1] # count total frames
|
||||
if cache["stats"].scores is None:
|
||||
cache["stats"].scores = scores # the first calculation
|
||||
else:
|
||||
cache["stats"].scores = torch.cat((cache["stats"].scores, scores), dim=1)
|
||||
|
||||
def PopDataBufTillFrame(self, frame_idx: int, cache: dict = {}) -> None: # need check again
|
||||
while cache["stats"].data_buf_start_frame < frame_idx:
|
||||
if len(cache["stats"].data_buf) >= int(
|
||||
self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000
|
||||
):
|
||||
cache["stats"].data_buf_start_frame += 1
|
||||
cache["stats"].data_buf = cache["stats"].data_buf_all[
|
||||
(cache["stats"].data_buf_start_frame - cache["stats"].last_drop_frames)
|
||||
* int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000) :
|
||||
]
|
||||
|
||||
def PopDataToOutputBuf(
|
||||
self,
|
||||
start_frm: int,
|
||||
frm_cnt: int,
|
||||
first_frm_is_start_point: bool,
|
||||
last_frm_is_end_point: bool,
|
||||
end_point_is_sent_end: bool,
|
||||
cache: dict = {},
|
||||
) -> None:
|
||||
self.PopDataBufTillFrame(start_frm, cache=cache)
|
||||
expected_sample_number = int(
|
||||
frm_cnt * self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000
|
||||
)
|
||||
if last_frm_is_end_point:
|
||||
extra_sample = max(
|
||||
0,
|
||||
int(
|
||||
self.vad_opts.frame_length_ms * self.vad_opts.sample_rate / 1000
|
||||
- self.vad_opts.sample_rate * self.vad_opts.frame_in_ms / 1000
|
||||
),
|
||||
)
|
||||
expected_sample_number += int(extra_sample)
|
||||
if end_point_is_sent_end:
|
||||
expected_sample_number = max(expected_sample_number, len(cache["stats"].data_buf))
|
||||
if len(cache["stats"].data_buf) < expected_sample_number:
|
||||
print("error in calling pop data_buf\n")
|
||||
|
||||
if len(cache["stats"].output_data_buf) == 0 or first_frm_is_start_point:
|
||||
cache["stats"].output_data_buf.append(E2EVadSpeechBufWithDoa())
|
||||
cache["stats"].output_data_buf[-1].Reset()
|
||||
cache["stats"].output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms
|
||||
cache["stats"].output_data_buf[-1].end_ms = cache["stats"].output_data_buf[-1].start_ms
|
||||
cache["stats"].output_data_buf[-1].doa = 0
|
||||
cur_seg = cache["stats"].output_data_buf[-1]
|
||||
if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms:
|
||||
print("warning\n")
|
||||
out_pos = len(cur_seg.buffer) # cur_seg.buff现在没做任何操作
|
||||
data_to_pop = 0
|
||||
if end_point_is_sent_end:
|
||||
data_to_pop = expected_sample_number
|
||||
else:
|
||||
data_to_pop = int(
|
||||
frm_cnt * self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000
|
||||
)
|
||||
if data_to_pop > len(cache["stats"].data_buf):
|
||||
print('VAD data_to_pop is bigger than cache["stats"].data_buf.size()!!!\n')
|
||||
data_to_pop = len(cache["stats"].data_buf)
|
||||
expected_sample_number = len(cache["stats"].data_buf)
|
||||
|
||||
cur_seg.doa = 0
|
||||
for sample_cpy_out in range(0, data_to_pop):
|
||||
# cur_seg.buffer[out_pos ++] = data_buf_.back();
|
||||
out_pos += 1
|
||||
for sample_cpy_out in range(data_to_pop, expected_sample_number):
|
||||
# cur_seg.buffer[out_pos++] = data_buf_.back()
|
||||
out_pos += 1
|
||||
if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms:
|
||||
print("Something wrong with the VAD algorithm\n")
|
||||
cache["stats"].data_buf_start_frame += frm_cnt
|
||||
cur_seg.end_ms = (start_frm + frm_cnt) * self.vad_opts.frame_in_ms
|
||||
if first_frm_is_start_point:
|
||||
cur_seg.contain_seg_start_point = True
|
||||
if last_frm_is_end_point:
|
||||
cur_seg.contain_seg_end_point = True
|
||||
|
||||
def OnSilenceDetected(self, valid_frame: int, cache: dict = {}):
|
||||
cache["stats"].lastest_confirmed_silence_frame = valid_frame
|
||||
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
|
||||
self.PopDataBufTillFrame(valid_frame, cache=cache)
|
||||
|
||||
# silence_detected_callback_
|
||||
# pass
|
||||
|
||||
def OnVoiceDetected(self, valid_frame: int, cache: dict = {}) -> None:
|
||||
cache["stats"].latest_confirmed_speech_frame = valid_frame
|
||||
self.PopDataToOutputBuf(valid_frame, 1, False, False, False, cache=cache)
|
||||
|
||||
def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache: dict = {}) -> None:
|
||||
if self.vad_opts.do_start_point_detection:
|
||||
pass
|
||||
if cache["stats"].confirmed_start_frame != -1:
|
||||
print("not reset vad properly\n")
|
||||
else:
|
||||
cache["stats"].confirmed_start_frame = start_frame
|
||||
|
||||
if (
|
||||
not fake_result
|
||||
and cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected
|
||||
):
|
||||
self.PopDataToOutputBuf(
|
||||
cache["stats"].confirmed_start_frame, 1, True, False, False, cache=cache
|
||||
)
|
||||
|
||||
def OnVoiceEnd(
|
||||
self, end_frame: int, fake_result: bool, is_last_frame: bool, cache: dict = {}
|
||||
) -> None:
|
||||
for t in range(cache["stats"].latest_confirmed_speech_frame + 1, end_frame):
|
||||
self.OnVoiceDetected(t, cache=cache)
|
||||
if self.vad_opts.do_end_point_detection:
|
||||
pass
|
||||
if cache["stats"].confirmed_end_frame != -1:
|
||||
print("not reset vad properly\n")
|
||||
else:
|
||||
cache["stats"].confirmed_end_frame = end_frame
|
||||
if not fake_result:
|
||||
cache["stats"].sil_frame = 0
|
||||
self.PopDataToOutputBuf(
|
||||
cache["stats"].confirmed_end_frame, 1, False, True, is_last_frame, cache=cache
|
||||
)
|
||||
cache["stats"].number_end_time_detected += 1
|
||||
|
||||
def MaybeOnVoiceEndIfLastFrame(
|
||||
self, is_final_frame: bool, cur_frm_idx: int, cache: dict = {}
|
||||
) -> None:
|
||||
if is_final_frame:
|
||||
self.OnVoiceEnd(cur_frm_idx, False, True, cache=cache)
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
||||
|
||||
def GetLatency(self, cache: dict = {}) -> int:
|
||||
return int(self.LatencyFrmNumAtStartPoint(cache=cache) * self.vad_opts.frame_in_ms)
|
||||
|
||||
def LatencyFrmNumAtStartPoint(self, cache: dict = {}) -> int:
|
||||
vad_latency = cache["windows_detector"].GetWinSize()
|
||||
if self.vad_opts.do_extend:
|
||||
vad_latency += int(self.vad_opts.lookback_time_start_point / self.vad_opts.frame_in_ms)
|
||||
return vad_latency
|
||||
|
||||
def GetFrameState(self, t: int, cache: dict = {}):
|
||||
frame_state = FrameState.kFrameStateInvalid
|
||||
cur_decibel = cache["stats"].decibel[t]
|
||||
cur_snr = cur_decibel - cache["stats"].noise_average_decibel
|
||||
# for each frame, calc log posterior probability of each state
|
||||
if cur_decibel < self.vad_opts.decibel_thres:
|
||||
frame_state = FrameState.kFrameStateSil
|
||||
self.DetectOneFrame(frame_state, t, False, cache=cache)
|
||||
return frame_state
|
||||
|
||||
sum_score = 0.0
|
||||
noise_prob = 0.0
|
||||
assert len(cache["stats"].sil_pdf_ids) == self.vad_opts.silence_pdf_num
|
||||
if len(cache["stats"].sil_pdf_ids) > 0:
|
||||
assert len(cache["stats"].scores) == 1 # 只支持batch_size = 1的测试
|
||||
sil_pdf_scores = [
|
||||
cache["stats"].scores[0][t][sil_pdf_id] for sil_pdf_id in cache["stats"].sil_pdf_ids
|
||||
]
|
||||
sum_score = sum(sil_pdf_scores)
|
||||
noise_prob = math.log(sum_score) * self.vad_opts.speech_2_noise_ratio
|
||||
total_score = 1.0
|
||||
sum_score = total_score - sum_score
|
||||
speech_prob = math.log(sum_score)
|
||||
if self.vad_opts.output_frame_probs:
|
||||
frame_prob = E2EVadFrameProb()
|
||||
frame_prob.noise_prob = noise_prob
|
||||
frame_prob.speech_prob = speech_prob
|
||||
frame_prob.score = sum_score
|
||||
frame_prob.frame_id = t
|
||||
cache["stats"].frame_probs.append(frame_prob)
|
||||
if math.exp(speech_prob) >= math.exp(noise_prob) + cache["stats"].speech_noise_thres:
|
||||
if cur_snr >= self.vad_opts.snr_thres and cur_decibel >= self.vad_opts.decibel_thres:
|
||||
frame_state = FrameState.kFrameStateSpeech
|
||||
else:
|
||||
frame_state = FrameState.kFrameStateSil
|
||||
else:
|
||||
frame_state = FrameState.kFrameStateSil
|
||||
if cache["stats"].noise_average_decibel < -99.9:
|
||||
cache["stats"].noise_average_decibel = cur_decibel
|
||||
else:
|
||||
cache["stats"].noise_average_decibel = (
|
||||
cur_decibel
|
||||
+ cache["stats"].noise_average_decibel
|
||||
* (self.vad_opts.noise_frame_num_used_for_snr - 1)
|
||||
) / self.vad_opts.noise_frame_num_used_for_snr
|
||||
|
||||
return frame_state
|
||||
|
||||
def forward(
|
||||
self,
|
||||
feats: torch.Tensor,
|
||||
waveform: torch.tensor,
|
||||
cache: dict = {},
|
||||
is_final: bool = False,
|
||||
**kwargs,
|
||||
):
|
||||
# if len(cache) == 0:
|
||||
# self.AllResetDetection()
|
||||
# self.waveform = waveform # compute decibel for each frame
|
||||
cache["stats"].waveform = waveform
|
||||
is_streaming_input = kwargs.get("is_streaming_input", True)
|
||||
self.ComputeDecibel(cache=cache)
|
||||
self.ComputeScores(feats, cache=cache)
|
||||
if not is_final:
|
||||
self.DetectCommonFrames(cache=cache)
|
||||
else:
|
||||
self.DetectLastFrames(cache=cache)
|
||||
segments = []
|
||||
for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
|
||||
segment_batch = []
|
||||
if len(cache["stats"].output_data_buf) > 0:
|
||||
for i in range(
|
||||
cache["stats"].output_data_buf_offset, len(cache["stats"].output_data_buf)
|
||||
):
|
||||
if (
|
||||
is_streaming_input
|
||||
): # in this case, return [beg, -1], [], [-1, end], [beg, end]
|
||||
if not cache["stats"].output_data_buf[i].contain_seg_start_point:
|
||||
continue
|
||||
if (
|
||||
not cache["stats"].next_seg
|
||||
and not cache["stats"].output_data_buf[i].contain_seg_end_point
|
||||
):
|
||||
continue
|
||||
start_ms = (
|
||||
cache["stats"].output_data_buf[i].start_ms
|
||||
if cache["stats"].next_seg
|
||||
else -1
|
||||
)
|
||||
if cache["stats"].output_data_buf[i].contain_seg_end_point:
|
||||
end_ms = cache["stats"].output_data_buf[i].end_ms
|
||||
cache["stats"].next_seg = True
|
||||
cache["stats"].output_data_buf_offset += 1
|
||||
else:
|
||||
end_ms = -1
|
||||
cache["stats"].next_seg = False
|
||||
segment = [start_ms, end_ms]
|
||||
|
||||
else: # in this case, return [beg, end]
|
||||
|
||||
if not is_final and (
|
||||
not cache["stats"].output_data_buf[i].contain_seg_start_point
|
||||
or not cache["stats"].output_data_buf[i].contain_seg_end_point
|
||||
):
|
||||
continue
|
||||
segment = [
|
||||
cache["stats"].output_data_buf[i].start_ms,
|
||||
cache["stats"].output_data_buf[i].end_ms,
|
||||
]
|
||||
cache["stats"].output_data_buf_offset += 1 # need update this parameter
|
||||
|
||||
segment_batch.append(segment)
|
||||
|
||||
if segment_batch:
|
||||
segments.append(segment_batch)
|
||||
# if is_final:
|
||||
# # reset class variables and clear the dict for the next query
|
||||
# self.AllResetDetection()
|
||||
return segments
|
||||
|
||||
def init_cache(self, cache: dict = {}, **kwargs):
|
||||
|
||||
cache["frontend"] = {}
|
||||
cache["prev_samples"] = torch.empty(0)
|
||||
cache["encoder"] = {}
|
||||
|
||||
if kwargs.get("max_end_silence_time") is not None:
|
||||
# update the max_end_silence_time
|
||||
self.vad_opts.max_end_silence_time = kwargs.get("max_end_silence_time")
|
||||
|
||||
windows_detector = WindowDetector(
|
||||
self.vad_opts.window_size_ms,
|
||||
self.vad_opts.sil_to_speech_time_thres,
|
||||
self.vad_opts.speech_to_sil_time_thres,
|
||||
self.vad_opts.frame_in_ms,
|
||||
)
|
||||
windows_detector.Reset()
|
||||
|
||||
stats = Stats(
|
||||
sil_pdf_ids=self.vad_opts.sil_pdf_ids,
|
||||
max_end_sil_frame_cnt_thresh=self.vad_opts.max_end_silence_time
|
||||
- self.vad_opts.speech_to_sil_time_thres,
|
||||
speech_noise_thres=self.vad_opts.speech_noise_thres,
|
||||
)
|
||||
cache["windows_detector"] = windows_detector
|
||||
cache["stats"] = stats
|
||||
return cache
|
||||
|
||||
def inference(
|
||||
self,
|
||||
data_in,
|
||||
data_lengths=None,
|
||||
key: list = None,
|
||||
tokenizer=None,
|
||||
frontend=None,
|
||||
cache: dict = {},
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
if len(cache) == 0:
|
||||
self.init_cache(cache, **kwargs)
|
||||
|
||||
meta_data = {}
|
||||
chunk_size = kwargs.get("chunk_size", 60000) # 50ms
|
||||
chunk_stride_samples = int(chunk_size * frontend.fs / 1000)
|
||||
|
||||
time1 = time.perf_counter()
|
||||
is_streaming_input = (
|
||||
kwargs.get("is_streaming_input", False)
|
||||
if chunk_size >= 15000
|
||||
else kwargs.get("is_streaming_input", True)
|
||||
)
|
||||
is_final = (
|
||||
kwargs.get("is_final", False) if is_streaming_input else kwargs.get("is_final", True)
|
||||
)
|
||||
cfg = {"is_final": is_final, "is_streaming_input": is_streaming_input}
|
||||
audio_sample_list = load_audio_text_image_video(
|
||||
data_in,
|
||||
fs=frontend.fs,
|
||||
audio_fs=kwargs.get("fs", 16000),
|
||||
data_type=kwargs.get("data_type", "sound"),
|
||||
tokenizer=tokenizer,
|
||||
cache=cfg,
|
||||
)
|
||||
_is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True
|
||||
is_streaming_input = cfg["is_streaming_input"]
|
||||
time2 = time.perf_counter()
|
||||
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
||||
assert len(audio_sample_list) == 1, "batch_size must be set 1"
|
||||
|
||||
audio_sample = torch.cat((cache["prev_samples"], audio_sample_list[0]))
|
||||
|
||||
n = int(len(audio_sample) // chunk_stride_samples + int(_is_final))
|
||||
m = int(len(audio_sample) % chunk_stride_samples * (1 - int(_is_final)))
|
||||
segments = []
|
||||
for i in range(n):
|
||||
kwargs["is_final"] = _is_final and i == n - 1
|
||||
audio_sample_i = audio_sample[i * chunk_stride_samples : (i + 1) * chunk_stride_samples]
|
||||
|
||||
# extract fbank feats
|
||||
speech, speech_lengths = extract_fbank(
|
||||
[audio_sample_i],
|
||||
data_type=kwargs.get("data_type", "sound"),
|
||||
frontend=frontend,
|
||||
cache=cache["frontend"],
|
||||
is_final=kwargs["is_final"],
|
||||
)
|
||||
time3 = time.perf_counter()
|
||||
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
||||
meta_data["batch_data_time"] = (
|
||||
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
||||
)
|
||||
speech = speech.to(device=kwargs["device"])
|
||||
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
||||
|
||||
batch = {
|
||||
"feats": speech,
|
||||
"waveform": cache["frontend"]["waveforms"],
|
||||
"is_final": kwargs["is_final"],
|
||||
"cache": cache,
|
||||
"is_streaming_input": is_streaming_input,
|
||||
}
|
||||
segments_i = self.forward(**batch)
|
||||
if len(segments_i) > 0:
|
||||
segments.extend(*segments_i)
|
||||
|
||||
cache["prev_samples"] = audio_sample[:-m]
|
||||
if _is_final:
|
||||
self.init_cache(cache)
|
||||
|
||||
ibest_writer = None
|
||||
if kwargs.get("output_dir") is not None:
|
||||
if not hasattr(self, "writer"):
|
||||
self.writer = DatadirWriter(kwargs.get("output_dir"))
|
||||
ibest_writer = self.writer[f"{1}best_recog"]
|
||||
|
||||
results = []
|
||||
result_i = {"key": key[0], "value": segments}
|
||||
# if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
|
||||
# result_i = json.dumps(result_i)
|
||||
|
||||
results.append(result_i)
|
||||
|
||||
if ibest_writer is not None:
|
||||
ibest_writer["text"][key[0]] = segments
|
||||
|
||||
return results, meta_data
|
||||
|
||||
def export(self, **kwargs):
|
||||
|
||||
from .export_meta import export_rebuild_model
|
||||
|
||||
models = export_rebuild_model(model=self, **kwargs)
|
||||
return models
|
||||
|
||||
def DetectCommonFrames(self, cache: dict = {}) -> int:
|
||||
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
|
||||
return 0
|
||||
for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
|
||||
frame_state = FrameState.kFrameStateInvalid
|
||||
frame_state = self.GetFrameState(
|
||||
cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache
|
||||
)
|
||||
self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
|
||||
|
||||
return 0
|
||||
|
||||
def DetectLastFrames(self, cache: dict = {}) -> int:
|
||||
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
|
||||
return 0
|
||||
for i in range(self.vad_opts.nn_eval_block_size - 1, -1, -1):
|
||||
frame_state = FrameState.kFrameStateInvalid
|
||||
frame_state = self.GetFrameState(
|
||||
cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache
|
||||
)
|
||||
if i != 0:
|
||||
self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
|
||||
else:
|
||||
self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1, True, cache=cache)
|
||||
|
||||
return 0
|
||||
|
||||
def DetectOneFrame(
|
||||
self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool, cache: dict = {}
|
||||
) -> None:
|
||||
tmp_cur_frm_state = FrameState.kFrameStateInvalid
|
||||
if cur_frm_state == FrameState.kFrameStateSpeech:
|
||||
if math.fabs(1.0) > self.vad_opts.fe_prior_thres:
|
||||
tmp_cur_frm_state = FrameState.kFrameStateSpeech
|
||||
else:
|
||||
tmp_cur_frm_state = FrameState.kFrameStateSil
|
||||
elif cur_frm_state == FrameState.kFrameStateSil:
|
||||
tmp_cur_frm_state = FrameState.kFrameStateSil
|
||||
state_change = cache["windows_detector"].DetectOneFrame(
|
||||
tmp_cur_frm_state, cur_frm_idx, cache=cache
|
||||
)
|
||||
frm_shift_in_ms = self.vad_opts.frame_in_ms
|
||||
if AudioChangeState.kChangeStateSil2Speech == state_change:
|
||||
silence_frame_count = cache["stats"].continous_silence_frame_count
|
||||
cache["stats"].continous_silence_frame_count = 0
|
||||
cache["stats"].pre_end_silence_detected = False
|
||||
start_frame = 0
|
||||
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
|
||||
start_frame = max(
|
||||
cache["stats"].data_buf_start_frame,
|
||||
cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache),
|
||||
)
|
||||
self.OnVoiceStart(start_frame, cache=cache)
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
|
||||
for t in range(start_frame + 1, cur_frm_idx + 1):
|
||||
self.OnVoiceDetected(t, cache=cache)
|
||||
elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
|
||||
for t in range(cache["stats"].latest_confirmed_speech_frame + 1, cur_frm_idx):
|
||||
self.OnVoiceDetected(t, cache=cache)
|
||||
if (
|
||||
cur_frm_idx - cache["stats"].confirmed_start_frame + 1
|
||||
> self.vad_opts.max_single_segment_time / frm_shift_in_ms
|
||||
):
|
||||
self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
||||
elif not is_final_frame:
|
||||
self.OnVoiceDetected(cur_frm_idx, cache=cache)
|
||||
else:
|
||||
self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
|
||||
else:
|
||||
pass
|
||||
elif AudioChangeState.kChangeStateSpeech2Sil == state_change:
|
||||
cache["stats"].continous_silence_frame_count = 0
|
||||
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
|
||||
pass
|
||||
elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
|
||||
if (
|
||||
cur_frm_idx - cache["stats"].confirmed_start_frame + 1
|
||||
> self.vad_opts.max_single_segment_time / frm_shift_in_ms
|
||||
):
|
||||
self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
||||
elif not is_final_frame:
|
||||
self.OnVoiceDetected(cur_frm_idx, cache=cache)
|
||||
else:
|
||||
self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
|
||||
else:
|
||||
pass
|
||||
elif AudioChangeState.kChangeStateSpeech2Speech == state_change:
|
||||
cache["stats"].continous_silence_frame_count = 0
|
||||
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
|
||||
if (
|
||||
cur_frm_idx - cache["stats"].confirmed_start_frame + 1
|
||||
> self.vad_opts.max_single_segment_time / frm_shift_in_ms
|
||||
):
|
||||
cache["stats"].max_time_out = True
|
||||
self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
||||
elif not is_final_frame:
|
||||
self.OnVoiceDetected(cur_frm_idx, cache=cache)
|
||||
else:
|
||||
self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
|
||||
else:
|
||||
pass
|
||||
elif AudioChangeState.kChangeStateSil2Sil == state_change:
|
||||
cache["stats"].continous_silence_frame_count += 1
|
||||
if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
|
||||
# silence timeout, return zero length decision
|
||||
if (
|
||||
(self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value)
|
||||
and (
|
||||
cache["stats"].continous_silence_frame_count * frm_shift_in_ms
|
||||
> self.vad_opts.max_start_silence_time
|
||||
)
|
||||
) or (is_final_frame and cache["stats"].number_end_time_detected == 0):
|
||||
for t in range(cache["stats"].lastest_confirmed_silence_frame + 1, cur_frm_idx):
|
||||
self.OnSilenceDetected(t, cache=cache)
|
||||
self.OnVoiceStart(0, True, cache=cache)
|
||||
self.OnVoiceEnd(0, True, False, cache=cache)
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
||||
else:
|
||||
if cur_frm_idx >= self.LatencyFrmNumAtStartPoint(cache=cache):
|
||||
self.OnSilenceDetected(
|
||||
cur_frm_idx - self.LatencyFrmNumAtStartPoint(cache=cache), cache=cache
|
||||
)
|
||||
elif cache["stats"].vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
|
||||
if (
|
||||
cache["stats"].continous_silence_frame_count * frm_shift_in_ms
|
||||
>= cache["stats"].max_end_sil_frame_cnt_thresh
|
||||
):
|
||||
lookback_frame = int(
|
||||
cache["stats"].max_end_sil_frame_cnt_thresh / frm_shift_in_ms
|
||||
)
|
||||
if self.vad_opts.do_extend:
|
||||
lookback_frame -= int(
|
||||
self.vad_opts.lookahead_time_end_point / frm_shift_in_ms
|
||||
)
|
||||
lookback_frame -= 1
|
||||
lookback_frame = max(0, lookback_frame)
|
||||
self.OnVoiceEnd(cur_frm_idx - lookback_frame, False, False, cache=cache)
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
||||
elif (
|
||||
cur_frm_idx - cache["stats"].confirmed_start_frame + 1
|
||||
> self.vad_opts.max_single_segment_time / frm_shift_in_ms
|
||||
):
|
||||
self.OnVoiceEnd(cur_frm_idx, False, False, cache=cache)
|
||||
cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
|
||||
elif self.vad_opts.do_extend and not is_final_frame:
|
||||
if cache["stats"].continous_silence_frame_count <= int(
|
||||
self.vad_opts.lookahead_time_end_point / frm_shift_in_ms
|
||||
):
|
||||
self.OnVoiceDetected(cur_frm_idx, cache=cache)
|
||||
else:
|
||||
self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
|
||||
else:
|
||||
pass
|
||||
|
||||
if (
|
||||
cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected
|
||||
and self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value
|
||||
):
|
||||
self.ResetDetection(cache=cache)
|
||||
@@ -0,0 +1,62 @@
|
||||
# This is an example that demonstrates how to configure a model file.
|
||||
# You can modify the configuration according to your own requirements.
|
||||
|
||||
# to print the register_table:
|
||||
# from funasr.register import tables
|
||||
# tables.print()
|
||||
|
||||
# network architecture
|
||||
model: FsmnVADStreaming
|
||||
model_conf:
|
||||
sample_rate: 16000
|
||||
detect_mode: 1
|
||||
snr_mode: 0
|
||||
max_end_silence_time: 800
|
||||
max_start_silence_time: 3000
|
||||
do_start_point_detection: True
|
||||
do_end_point_detection: True
|
||||
window_size_ms: 200
|
||||
sil_to_speech_time_thres: 150
|
||||
speech_to_sil_time_thres: 150
|
||||
speech_2_noise_ratio: 1.0
|
||||
do_extend: 1
|
||||
lookback_time_start_point: 200
|
||||
lookahead_time_end_point: 100
|
||||
max_single_segment_time: 60000
|
||||
snr_thres: -100.0
|
||||
noise_frame_num_used_for_snr: 100
|
||||
decibel_thres: -100.0
|
||||
speech_noise_thres: 0.6
|
||||
fe_prior_thres: 0.0001
|
||||
silence_pdf_num: 1
|
||||
sil_pdf_ids: [0]
|
||||
speech_noise_thresh_low: -0.1
|
||||
speech_noise_thresh_high: 0.3
|
||||
output_frame_probs: False
|
||||
frame_in_ms: 10
|
||||
frame_length_ms: 25
|
||||
|
||||
encoder: FSMN
|
||||
encoder_conf:
|
||||
input_dim: 400
|
||||
input_affine_dim: 140
|
||||
fsmn_layers: 4
|
||||
linear_dim: 250
|
||||
proj_dim: 128
|
||||
lorder: 20
|
||||
rorder: 0
|
||||
lstride: 1
|
||||
rstride: 0
|
||||
output_affine_dim: 140
|
||||
output_dim: 248
|
||||
|
||||
frontend: WavFrontend
|
||||
frontend_conf:
|
||||
fs: 16000
|
||||
window: hamming
|
||||
n_mels: 80
|
||||
frame_length: 25
|
||||
frame_shift: 10
|
||||
dither: 0.0
|
||||
lfr_m: 5
|
||||
lfr_n: 1
|
||||
Reference in new issue
Block a user