From c0b186b5b6e950472920964932ba3de546e06dbf Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 12 一月 2024 22:48:30 +0800
Subject: [PATCH] funasr1.0 streaming
---
funasr/frontends/wav_frontend.py | 3
/dev/null | 62 ------
examples/industrial_data_pretraining/fsmn_vad_streaming/infer.sh | 2
funasr/models/fsmn_vad_streaming/model.py | 479 +++++++++++++++++++++++------------------------
examples/industrial_data_pretraining/paraformer-zh-spk/demo.py | 2
examples/industrial_data_pretraining/paraformer-zh-spk/infer.sh | 2
examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py | 11
examples/industrial_data_pretraining/bicif_paraformer/demo.py | 4
examples/industrial_data_pretraining/seaco_paraformer/infer.sh | 2
examples/industrial_data_pretraining/seaco_paraformer/demo.py | 2
10 files changed, 245 insertions(+), 324 deletions(-)
diff --git a/examples/industrial_data_pretraining/bicif_paraformer/demo.py b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
index 16eed37..84b0e80 100644
--- a/examples/industrial_data_pretraining/bicif_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/bicif_paraformer/demo.py
@@ -8,7 +8,7 @@
model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
model_revision="v2.0.0",
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- vad_model_revision="v2.0.0",
+ vad_model_revision="v2.0.1",
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
punc_model_revision="v2.0.0",
spk_model="/Users/shixian/code/modelscope_models/speech_campplus_sv_zh-cn_16k-common",
@@ -21,7 +21,7 @@
model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
model_revision="v2.0.0",
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- vad_model_revision="v2.0.0",
+ vad_model_revision="v2.0.1",
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
punc_model_revision="v2.0.0",
spk_model="/Users/shixian/code/modelscope_models/speech_campplus_sv_zh-cn_16k-common",
diff --git a/examples/industrial_data_pretraining/fsmn_vad/demo.py b/examples/industrial_data_pretraining/fsmn_vad/demo.py
deleted file mode 100644
index 2a157ee..0000000
--- a/examples/industrial_data_pretraining/fsmn_vad/demo.py
+++ /dev/null
@@ -1,11 +0,0 @@
-#!/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)
-
-from funasr import AutoModel
-
-model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.0")
-
-res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav")
-print(res)
\ No newline at end of file
diff --git a/examples/industrial_data_pretraining/fsmn_vad/infer.sh b/examples/industrial_data_pretraining/fsmn_vad/infer.sh
deleted file mode 100644
index dedd14a..0000000
--- a/examples/industrial_data_pretraining/fsmn_vad/infer.sh
+++ /dev/null
@@ -1,11 +0,0 @@
-
-
-model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-model_revision="v2.0.0"
-
-python funasr/bin/inference.py \
-+model=${model} \
-+model_revision=${model_revision} \
-+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav" \
-+output_dir="./outputs/debug" \
-+device="cpu" \
diff --git a/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py b/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
index 6831cba..4e3cb70 100644
--- a/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
+++ b/examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py
@@ -7,11 +7,9 @@
wav_file = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav"
chunk_size = 60000 # ms
-model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_fsmn_vad_zh-cn-16k-common-streaming", model_revision="v2.0.0")
+model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.1")
-res = model(input=wav_file,
- chunk_size=chunk_size,
- )
+res = model(input=wav_file, chunk_size=chunk_size, )
print(res)
@@ -22,7 +20,7 @@
wav_file = os.path.join(model.model_path, "example/vad_example.wav")
speech, sample_rate = soundfile.read(wav_file)
-chunk_stride = int(chunk_size * 16000 / 1000)
+chunk_stride = int(chunk_size * sample_rate / 1000)
cache = {}
@@ -35,4 +33,5 @@
is_final=is_final,
chunk_size=chunk_size,
)
- print(res)
+ if len(res[0]["value"]):
+ print(res)
diff --git a/examples/industrial_data_pretraining/fsmn_vad_streaming/infer.sh b/examples/industrial_data_pretraining/fsmn_vad_streaming/infer.sh
index dedd14a..08ef8bd 100644
--- a/examples/industrial_data_pretraining/fsmn_vad_streaming/infer.sh
+++ b/examples/industrial_data_pretraining/fsmn_vad_streaming/infer.sh
@@ -1,7 +1,7 @@
model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-model_revision="v2.0.0"
+model_revision="v2.0.1"
python funasr/bin/inference.py \
+model=${model} \
diff --git a/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py b/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
index 123ec41..774d757 100644
--- a/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
+++ b/examples/industrial_data_pretraining/paraformer-zh-spk/demo.py
@@ -8,7 +8,7 @@
model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
model_revision="v2.0.0",
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- vad_model_revision="v2.0.0",
+ vad_model_revision="v2.0.1",
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
punc_model_revision="v2.0.0",
spk_model="damo/speech_campplus_sv_zh-cn_16k-common",
diff --git a/examples/industrial_data_pretraining/paraformer-zh-spk/infer.sh b/examples/industrial_data_pretraining/paraformer-zh-spk/infer.sh
index c2325a3..a457401 100644
--- a/examples/industrial_data_pretraining/paraformer-zh-spk/infer.sh
+++ b/examples/industrial_data_pretraining/paraformer-zh-spk/infer.sh
@@ -2,7 +2,7 @@
model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.0"
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-vad_model_revision="v2.0.0"
+vad_model_revision="v2.0.1"
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
punc_model_revision="v2.0.0"
spk_model="damo/speech_campplus_sv_zh-cn_16k-common"
diff --git a/examples/industrial_data_pretraining/seaco_paraformer/demo.py b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
index 84be0d8..63f155e 100644
--- a/examples/industrial_data_pretraining/seaco_paraformer/demo.py
+++ b/examples/industrial_data_pretraining/seaco_paraformer/demo.py
@@ -8,7 +8,7 @@
model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
model_revision="v2.0.0",
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
- vad_model_revision="v2.0.0",
+ vad_model_revision="v2.0.1",
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
punc_model_revision="v2.0.0",
)
diff --git a/examples/industrial_data_pretraining/seaco_paraformer/infer.sh b/examples/industrial_data_pretraining/seaco_paraformer/infer.sh
index e92d598..26eeee1 100644
--- a/examples/industrial_data_pretraining/seaco_paraformer/infer.sh
+++ b/examples/industrial_data_pretraining/seaco_paraformer/infer.sh
@@ -2,7 +2,7 @@
model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.0"
vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
-vad_model_revision="v2.0.0"
+vad_model_revision="v2.0.1"
punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
punc_model_revision="v2.0.0"
diff --git a/funasr/frontends/wav_frontend.py b/funasr/frontends/wav_frontend.py
index f410085..9c896f1 100644
--- a/funasr/frontends/wav_frontend.py
+++ b/funasr/frontends/wav_frontend.py
@@ -402,8 +402,7 @@
self, input: torch.Tensor, input_lengths: torch.Tensor, cache: dict = {}, **kwargs
):
is_final = kwargs.get("is_final", False)
- reset = kwargs.get("reset", False)
- if len(cache) == 0 or reset:
+ if len(cache) == 0:
self.init_cache(cache)
batch_size = input.shape[0]
diff --git a/funasr/models/fsmn_vad/__init__.py b/funasr/models/fsmn_vad/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/funasr/models/fsmn_vad/__init__.py
+++ /dev/null
diff --git a/funasr/models/fsmn_vad/encoder.py b/funasr/models/fsmn_vad/encoder.py
deleted file mode 100755
index a0a379d..0000000
--- a/funasr/models/fsmn_vad/encoder.py
+++ /dev/null
@@ -1,303 +0,0 @@
-from typing import Tuple, Dict
-import copy
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from funasr.register import tables
-
-class LinearTransform(nn.Module):
-
- def __init__(self, input_dim, output_dim):
- super(LinearTransform, self).__init__()
- self.input_dim = input_dim
- self.output_dim = output_dim
- self.linear = nn.Linear(input_dim, output_dim, bias=False)
-
- def forward(self, input):
- output = self.linear(input)
-
- return output
-
-
-class AffineTransform(nn.Module):
-
- def __init__(self, input_dim, output_dim):
- super(AffineTransform, self).__init__()
- self.input_dim = input_dim
- self.output_dim = output_dim
- self.linear = nn.Linear(input_dim, output_dim)
-
- def forward(self, input):
- output = self.linear(input)
-
- return output
-
-
-class RectifiedLinear(nn.Module):
-
- def __init__(self, input_dim, output_dim):
- super(RectifiedLinear, self).__init__()
- self.dim = input_dim
- self.relu = nn.ReLU()
- self.dropout = nn.Dropout(0.1)
-
- def forward(self, input):
- out = self.relu(input)
- return out
-
-
-class FSMNBlock(nn.Module):
-
- def __init__(
- self,
- input_dim: int,
- output_dim: int,
- lorder=None,
- rorder=None,
- lstride=1,
- rstride=1,
- ):
- super(FSMNBlock, self).__init__()
-
- self.dim = input_dim
-
- if lorder is None:
- return
-
- self.lorder = lorder
- self.rorder = rorder
- self.lstride = lstride
- self.rstride = rstride
-
- self.conv_left = nn.Conv2d(
- self.dim, self.dim, [lorder, 1], dilation=[lstride, 1], groups=self.dim, bias=False)
-
- if self.rorder > 0:
- self.conv_right = nn.Conv2d(
- self.dim, self.dim, [rorder, 1], dilation=[rstride, 1], groups=self.dim, bias=False)
- else:
- self.conv_right = None
-
- def forward(self, input: torch.Tensor, cache: torch.Tensor):
- x = torch.unsqueeze(input, 1)
- x_per = x.permute(0, 3, 2, 1) # B D T C
-
- cache = cache.to(x_per.device)
- y_left = torch.cat((cache, x_per), dim=2)
- cache = y_left[:, :, -(self.lorder - 1) * self.lstride:, :]
- y_left = self.conv_left(y_left)
- out = x_per + y_left
-
- if self.conv_right is not None:
- # maybe need to check
- y_right = F.pad(x_per, [0, 0, 0, self.rorder * self.rstride])
- y_right = y_right[:, :, self.rstride:, :]
- y_right = self.conv_right(y_right)
- out += y_right
-
- out_per = out.permute(0, 3, 2, 1)
- output = out_per.squeeze(1)
-
- return output, cache
-
-
-class BasicBlock(nn.Sequential):
- def __init__(self,
- linear_dim: int,
- proj_dim: int,
- lorder: int,
- rorder: int,
- lstride: int,
- rstride: int,
- stack_layer: int
- ):
- super(BasicBlock, self).__init__()
- self.lorder = lorder
- self.rorder = rorder
- self.lstride = lstride
- self.rstride = rstride
- self.stack_layer = stack_layer
- self.linear = LinearTransform(linear_dim, proj_dim)
- self.fsmn_block = FSMNBlock(proj_dim, proj_dim, lorder, rorder, lstride, rstride)
- self.affine = AffineTransform(proj_dim, linear_dim)
- self.relu = RectifiedLinear(linear_dim, linear_dim)
-
- def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]):
- x1 = self.linear(input) # B T D
- cache_layer_name = 'cache_layer_{}'.format(self.stack_layer)
- if cache_layer_name not in cache:
- cache[cache_layer_name] = torch.zeros(x1.shape[0], x1.shape[-1], (self.lorder - 1) * self.lstride, 1)
- x2, cache[cache_layer_name] = self.fsmn_block(x1, cache[cache_layer_name])
- x3 = self.affine(x2)
- x4 = self.relu(x3)
- return x4
-
-
-class FsmnStack(nn.Sequential):
- def __init__(self, *args):
- super(FsmnStack, self).__init__(*args)
-
- def forward(self, input: torch.Tensor, cache: Dict[str, torch.Tensor]):
- x = input
- for module in self._modules.values():
- x = module(x, cache)
- return x
-
-
-'''
-FSMN net for keyword spotting
-input_dim: input dimension
-linear_dim: fsmn input dimensionll
-proj_dim: fsmn projection dimension
-lorder: fsmn left order
-rorder: fsmn right order
-num_syn: output dimension
-fsmn_layers: no. of sequential fsmn layers
-'''
-
-@tables.register("encoder_classes", "FSMN")
-class FSMN(nn.Module):
- def __init__(
- self,
- input_dim: int,
- input_affine_dim: int,
- fsmn_layers: int,
- linear_dim: int,
- proj_dim: int,
- lorder: int,
- rorder: int,
- lstride: int,
- rstride: int,
- output_affine_dim: int,
- output_dim: int
- ):
- super(FSMN, self).__init__()
-
- self.input_dim = input_dim
- self.input_affine_dim = input_affine_dim
- self.fsmn_layers = fsmn_layers
- self.linear_dim = linear_dim
- self.proj_dim = proj_dim
- self.output_affine_dim = output_affine_dim
- self.output_dim = output_dim
-
- self.in_linear1 = AffineTransform(input_dim, input_affine_dim)
- self.in_linear2 = AffineTransform(input_affine_dim, linear_dim)
- self.relu = RectifiedLinear(linear_dim, linear_dim)
- self.fsmn = FsmnStack(*[BasicBlock(linear_dim, proj_dim, lorder, rorder, lstride, rstride, i) for i in
- range(fsmn_layers)])
- self.out_linear1 = AffineTransform(linear_dim, output_affine_dim)
- self.out_linear2 = AffineTransform(output_affine_dim, output_dim)
- self.softmax = nn.Softmax(dim=-1)
-
- def fuse_modules(self):
- pass
-
- def forward(
- self,
- input: torch.Tensor,
- cache: Dict[str, torch.Tensor]
- ) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
- """
- Args:
- input (torch.Tensor): Input tensor (B, T, D)
- cache: when cache is not None, the forward is in streaming. The type of cache is a dict, egs,
- {'cache_layer_1': torch.Tensor(B, T1, D)}, T1 is equal to self.lorder. It is {} for the 1st frame
- """
-
- x1 = self.in_linear1(input)
- x2 = self.in_linear2(x1)
- x3 = self.relu(x2)
- x4 = self.fsmn(x3, cache) # self.cache will update automatically in self.fsmn
- x5 = self.out_linear1(x4)
- x6 = self.out_linear2(x5)
- x7 = self.softmax(x6)
-
- return x7
-
-
-'''
-one deep fsmn layer
-dimproj: projection dimension, input and output dimension of memory blocks
-dimlinear: dimension of mapping layer
-lorder: left order
-rorder: right order
-lstride: left stride
-rstride: right stride
-'''
-
-@tables.register("encoder_classes", "DFSMN")
-class DFSMN(nn.Module):
-
- def __init__(self, dimproj=64, dimlinear=128, lorder=20, rorder=1, lstride=1, rstride=1):
- super(DFSMN, self).__init__()
-
- self.lorder = lorder
- self.rorder = rorder
- self.lstride = lstride
- self.rstride = rstride
-
- self.expand = AffineTransform(dimproj, dimlinear)
- self.shrink = LinearTransform(dimlinear, dimproj)
-
- self.conv_left = nn.Conv2d(
- dimproj, dimproj, [lorder, 1], dilation=[lstride, 1], groups=dimproj, bias=False)
-
- if rorder > 0:
- self.conv_right = nn.Conv2d(
- dimproj, dimproj, [rorder, 1], dilation=[rstride, 1], groups=dimproj, bias=False)
- else:
- self.conv_right = None
-
- def forward(self, input):
- f1 = F.relu(self.expand(input))
- p1 = self.shrink(f1)
-
- x = torch.unsqueeze(p1, 1)
- x_per = x.permute(0, 3, 2, 1)
-
- y_left = F.pad(x_per, [0, 0, (self.lorder - 1) * self.lstride, 0])
-
- if self.conv_right is not None:
- y_right = F.pad(x_per, [0, 0, 0, (self.rorder) * self.rstride])
- y_right = y_right[:, :, self.rstride:, :]
- out = x_per + self.conv_left(y_left) + self.conv_right(y_right)
- else:
- out = x_per + self.conv_left(y_left)
-
- out1 = out.permute(0, 3, 2, 1)
- output = input + out1.squeeze(1)
-
- return output
-
-
-'''
-build stacked dfsmn layers
-'''
-
-
-def buildDFSMNRepeats(linear_dim=128, proj_dim=64, lorder=20, rorder=1, fsmn_layers=6):
- repeats = [
- nn.Sequential(
- DFSMN(proj_dim, linear_dim, lorder, rorder, 1, 1))
- for i in range(fsmn_layers)
- ]
-
- return nn.Sequential(*repeats)
-
-
-if __name__ == '__main__':
- fsmn = FSMN(400, 140, 4, 250, 128, 10, 2, 1, 1, 140, 2599)
- print(fsmn)
-
- num_params = sum(p.numel() for p in fsmn.parameters())
- print('the number of model params: {}'.format(num_params))
- x = torch.zeros(128, 200, 400) # batch-size * time * dim
- y, _ = fsmn(x) # batch-size * time * dim
- print('input shape: {}'.format(x.shape))
- print('output shape: {}'.format(y.shape))
-
- print(fsmn.to_kaldi_net())
diff --git a/funasr/models/fsmn_vad/model.py b/funasr/models/fsmn_vad/model.py
deleted file mode 100644
index b31e061..0000000
--- a/funasr/models/fsmn_vad/model.py
+++ /dev/null
@@ -1,740 +0,0 @@
-from enum import Enum
-from typing import List, Tuple, Dict, Any
-import logging
-import os
-import json
-import torch
-from torch import nn
-import math
-from typing import Optional
-import time
-from funasr.register import tables
-from funasr.utils.load_utils import load_audio_text_image_video,extract_fbank
-from funasr.utils.datadir_writer import DatadirWriter
-from torch.nn.utils.rnn import pad_sequence
-from funasr.train_utils.device_funcs import to_device
-
-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 # 鍒濆鍖栫獥
-
- 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) -> 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)
-
-
-@tables.register("model_classes", "FsmnVAD")
-class FsmnVAD(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)
- self.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)
-
- encoder_class = tables.encoder_classes.get(encoder.lower())
- encoder = encoder_class(**encoder_conf)
- self.encoder = encoder
- # init variables
- 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 = self.vad_opts.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 = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres
- self.speech_noise_thres = self.vad_opts.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
-
- def AllResetDetection(self):
- 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 = self.vad_opts.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 = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres
- self.speech_noise_thres = self.vad_opts.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
- self.windows_detector.Reset()
-
- def ResetDetection(self):
- self.continous_silence_frame_count = 0
- self.latest_confirmed_speech_frame = 0
- self.lastest_confirmed_silence_frame = -1
- self.confirmed_start_frame = -1
- self.confirmed_end_frame = -1
- self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
- self.windows_detector.Reset()
- self.sil_frame = 0
- self.frame_probs = []
-
- if self.output_data_buf:
- assert self.output_data_buf[-1].contain_seg_end_point == True
- drop_frames = int(self.output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
- real_drop_frames = drop_frames - self.last_drop_frames
- self.last_drop_frames = drop_frames
- self.data_buf_all = self.data_buf_all[real_drop_frames * int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
- self.decibel = self.decibel[real_drop_frames:]
- self.scores = self.scores[:, real_drop_frames:, :]
-
- def ComputeDecibel(self) -> 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 self.data_buf_all is None:
- self.data_buf_all = self.waveform[0] # self.data_buf is pointed to self.waveform[0]
- self.data_buf = self.data_buf_all
- else:
- self.data_buf_all = torch.cat((self.data_buf_all, self.waveform[0]))
- for offset in range(0, self.waveform.shape[1] - frame_sample_length + 1, frame_shift_length):
- self.decibel.append(
- 10 * math.log10((self.waveform[0][offset: offset + frame_sample_length]).square().sum() + \
- 0.000001))
-
- def ComputeScores(self, feats: torch.Tensor, cache: Dict[str, torch.Tensor]) -> None:
- scores = self.encoder(feats, cache).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]
- self.frm_cnt += scores.shape[1] # count total frames
- if self.scores is None:
- self.scores = scores # the first calculation
- else:
- self.scores = torch.cat((self.scores, scores), dim=1)
-
- def PopDataBufTillFrame(self, frame_idx: int) -> None: # need check again
- while self.data_buf_start_frame < frame_idx:
- if len(self.data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):
- self.data_buf_start_frame += 1
- self.data_buf = self.data_buf_all[(self.data_buf_start_frame - self.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) -> None:
- self.PopDataBufTillFrame(start_frm)
- 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(self.data_buf))
- if len(self.data_buf) < expected_sample_number:
- print('error in calling pop data_buf\n')
-
- if len(self.output_data_buf) == 0 or first_frm_is_start_point:
- self.output_data_buf.append(E2EVadSpeechBufWithDoa())
- self.output_data_buf[-1].Reset()
- self.output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms
- self.output_data_buf[-1].end_ms = self.output_data_buf[-1].start_ms
- self.output_data_buf[-1].doa = 0
- cur_seg = self.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鐜板湪娌″仛浠讳綍鎿嶄綔
- 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(self.data_buf):
- print('VAD data_to_pop is bigger than self.data_buf.size()!!!\n')
- data_to_pop = len(self.data_buf)
- expected_sample_number = len(self.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')
- self.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):
- self.lastest_confirmed_silence_frame = valid_frame
- if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- self.PopDataBufTillFrame(valid_frame)
- # silence_detected_callback_
- # pass
-
- def OnVoiceDetected(self, valid_frame: int) -> None:
- self.latest_confirmed_speech_frame = valid_frame
- self.PopDataToOutputBuf(valid_frame, 1, False, False, False)
-
- def OnVoiceStart(self, start_frame: int, fake_result: bool = False) -> None:
- if self.vad_opts.do_start_point_detection:
- pass
- if self.confirmed_start_frame != -1:
- print('not reset vad properly\n')
- else:
- self.confirmed_start_frame = start_frame
-
- if not fake_result and self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- self.PopDataToOutputBuf(self.confirmed_start_frame, 1, True, False, False)
-
- def OnVoiceEnd(self, end_frame: int, fake_result: bool, is_last_frame: bool) -> None:
- for t in range(self.latest_confirmed_speech_frame + 1, end_frame):
- self.OnVoiceDetected(t)
- if self.vad_opts.do_end_point_detection:
- pass
- if self.confirmed_end_frame != -1:
- print('not reset vad properly\n')
- else:
- self.confirmed_end_frame = end_frame
- if not fake_result:
- self.sil_frame = 0
- self.PopDataToOutputBuf(self.confirmed_end_frame, 1, False, True, is_last_frame)
- self.number_end_time_detected += 1
-
- def MaybeOnVoiceEndIfLastFrame(self, is_final_frame: bool, cur_frm_idx: int) -> None:
- if is_final_frame:
- self.OnVoiceEnd(cur_frm_idx, False, True)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
-
- def GetLatency(self) -> int:
- return int(self.LatencyFrmNumAtStartPoint() * self.vad_opts.frame_in_ms)
-
- def LatencyFrmNumAtStartPoint(self) -> int:
- vad_latency = self.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):
- frame_state = FrameState.kFrameStateInvalid
- cur_decibel = self.decibel[t]
- cur_snr = cur_decibel - self.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)
- return frame_state
-
- sum_score = 0.0
- noise_prob = 0.0
- assert len(self.sil_pdf_ids) == self.vad_opts.silence_pdf_num
- if len(self.sil_pdf_ids) > 0:
- assert len(self.scores) == 1 # 鍙敮鎸乥atch_size = 1鐨勬祴璇�
- sil_pdf_scores = [self.scores[0][t][sil_pdf_id] for sil_pdf_id in self.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
- self.frame_probs.append(frame_prob)
- if math.exp(speech_prob) >= math.exp(noise_prob) + self.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 self.noise_average_decibel < -99.9:
- self.noise_average_decibel = cur_decibel
- else:
- self.noise_average_decibel = (cur_decibel + self.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[str, torch.Tensor] = dict(),
- is_final: bool = False
- ):
- if not cache:
- self.AllResetDetection()
- self.waveform = waveform # compute decibel for each frame
- self.ComputeDecibel()
- self.ComputeScores(feats, cache)
- if not is_final:
- self.DetectCommonFrames()
- else:
- self.DetectLastFrames()
- segments = []
- for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
- segment_batch = []
- if len(self.output_data_buf) > 0:
- for i in range(self.output_data_buf_offset, len(self.output_data_buf)):
- if not is_final and (not self.output_data_buf[i].contain_seg_start_point or not self.output_data_buf[
- i].contain_seg_end_point):
- continue
- segment = [self.output_data_buf[i].start_ms, self.output_data_buf[i].end_ms]
- segment_batch.append(segment)
- self.output_data_buf_offset += 1 # need update this parameter
- 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, cache
-
- def generate(self,
- data_in,
- data_lengths=None,
- key: list = None,
- tokenizer=None,
- frontend=None,
- **kwargs,
- ):
-
-
- meta_data = {}
- audio_sample_list = [data_in]
- if isinstance(data_in, torch.Tensor): # fbank
- speech, speech_lengths = data_in, data_lengths
- if len(speech.shape) < 3:
- speech = speech[None, :, :]
- if speech_lengths is None:
- speech_lengths = speech.shape[1]
- else:
- # extract fbank feats
- time1 = time.perf_counter()
- audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000))
- time2 = time.perf_counter()
- meta_data["load_data"] = f"{time2 - time1:0.3f}"
- speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
- frontend=frontend)
- 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"])
-
- # b. Forward Encoder streaming
- t_offset = 0
- feats = speech
- feats_len = speech_lengths.max().item()
- waveform = pad_sequence(audio_sample_list, batch_first=True).to(device=kwargs["device"]) # data: [batch, N]
- cache = kwargs.get("cache", {})
- batch_size = kwargs.get("batch_size", 1)
- step = min(feats_len, 6000)
- segments = [[]] * batch_size
-
- for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
- if t_offset + step >= feats_len - 1:
- step = feats_len - t_offset
- is_final = True
- else:
- is_final = False
- batch = {
- "feats": feats[:, t_offset:t_offset + step, :],
- "waveform": waveform[:, t_offset * 160:min(waveform.shape[-1], (t_offset + step - 1) * 160 + 400)],
- "is_final": is_final,
- "cache": cache
- }
-
-
- batch = to_device(batch, device=kwargs["device"])
- segments_part, cache = self.forward(**batch)
- if segments_part:
- for batch_num in range(0, batch_size):
- segments[batch_num] += segments_part[batch_num]
-
- ibest_writer = None
- if ibest_writer is None and kwargs.get("output_dir") is not None:
- writer = DatadirWriter(kwargs.get("output_dir"))
- ibest_writer = writer[f"{1}best_recog"]
-
- results = []
- for i in range(batch_size):
-
-
- if ibest_writer is not None:
- ibest_writer["text"][key[i]] = segments[i]
-
- result_i = {"key": key[i], "value": segments[i]}
- results.append(result_i)
-
- if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
- results[i] = json.dumps(results[i])
-
- return results, meta_data
-
- def DetectCommonFrames(self) -> int:
- if self.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(self.frm_cnt - 1 - i - self.last_drop_frames)
- self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False)
-
- return 0
-
- def DetectLastFrames(self) -> int:
- if self.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(self.frm_cnt - 1 - i - self.last_drop_frames)
- if i != 0:
- self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False)
- else:
- self.DetectOneFrame(frame_state, self.frm_cnt - 1, True)
-
- return 0
-
- def DetectOneFrame(self, cur_frm_state: FrameState, cur_frm_idx: int, is_final_frame: bool) -> 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 = self.windows_detector.DetectOneFrame(tmp_cur_frm_state, cur_frm_idx)
- frm_shift_in_ms = self.vad_opts.frame_in_ms
- if AudioChangeState.kChangeStateSil2Speech == state_change:
- silence_frame_count = self.continous_silence_frame_count
- self.continous_silence_frame_count = 0
- self.pre_end_silence_detected = False
- start_frame = 0
- if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- start_frame = max(self.data_buf_start_frame, cur_frm_idx - self.LatencyFrmNumAtStartPoint())
- self.OnVoiceStart(start_frame)
- self.vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
- for t in range(start_frame + 1, cur_frm_idx + 1):
- self.OnVoiceDetected(t)
- elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- for t in range(self.latest_confirmed_speech_frame + 1, cur_frm_idx):
- self.OnVoiceDetected(t)
- if cur_frm_idx - self.confirmed_start_frame + 1 > \
- self.vad_opts.max_single_segment_time / frm_shift_in_ms:
- self.OnVoiceEnd(cur_frm_idx, False, False)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif not is_final_frame:
- self.OnVoiceDetected(cur_frm_idx)
- else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
- else:
- pass
- elif AudioChangeState.kChangeStateSpeech2Sil == state_change:
- self.continous_silence_frame_count = 0
- if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- pass
- elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if cur_frm_idx - self.confirmed_start_frame + 1 > \
- self.vad_opts.max_single_segment_time / frm_shift_in_ms:
- self.OnVoiceEnd(cur_frm_idx, False, False)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif not is_final_frame:
- self.OnVoiceDetected(cur_frm_idx)
- else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
- else:
- pass
- elif AudioChangeState.kChangeStateSpeech2Speech == state_change:
- self.continous_silence_frame_count = 0
- if self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if cur_frm_idx - self.confirmed_start_frame + 1 > \
- self.vad_opts.max_single_segment_time / frm_shift_in_ms:
- self.max_time_out = True
- self.OnVoiceEnd(cur_frm_idx, False, False)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif not is_final_frame:
- self.OnVoiceDetected(cur_frm_idx)
- else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
- else:
- pass
- elif AudioChangeState.kChangeStateSil2Sil == state_change:
- self.continous_silence_frame_count += 1
- if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- # silence timeout, return zero length decision
- if ((self.vad_opts.detect_mode == VadDetectMode.kVadSingleUtteranceDetectMode.value) and (
- self.continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
- or (is_final_frame and self.number_end_time_detected == 0):
- for t in range(self.lastest_confirmed_silence_frame + 1, cur_frm_idx):
- self.OnSilenceDetected(t)
- self.OnVoiceStart(0, True)
- self.OnVoiceEnd(0, True, False);
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- else:
- if cur_frm_idx >= self.LatencyFrmNumAtStartPoint():
- self.OnSilenceDetected(cur_frm_idx - self.LatencyFrmNumAtStartPoint())
- elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if self.continous_silence_frame_count * frm_shift_in_ms >= self.max_end_sil_frame_cnt_thresh:
- lookback_frame = int(self.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)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif cur_frm_idx - self.confirmed_start_frame + 1 > \
- self.vad_opts.max_single_segment_time / frm_shift_in_ms:
- self.OnVoiceEnd(cur_frm_idx, False, False)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif self.vad_opts.do_extend and not is_final_frame:
- if self.continous_silence_frame_count <= int(
- self.vad_opts.lookahead_time_end_point / frm_shift_in_ms):
- self.OnVoiceDetected(cur_frm_idx)
- else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
- else:
- pass
-
- if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
- self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value:
- self.ResetDetection()
-
-
-
diff --git a/funasr/models/fsmn_vad/template.yaml b/funasr/models/fsmn_vad/template.yaml
deleted file mode 100644
index 90032eb..0000000
--- a/funasr/models/fsmn_vad/template.yaml
+++ /dev/null
@@ -1,62 +0,0 @@
-# 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: FsmnVAD
-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
diff --git a/funasr/models/fsmn_vad_streaming/model.py b/funasr/models/fsmn_vad_streaming/model.py
index 9ceacf6..544fab8 100644
--- a/funasr/models/fsmn_vad_streaming/model.py
+++ b/funasr/models/fsmn_vad_streaming/model.py
@@ -11,7 +11,8 @@
from funasr.register import tables
from funasr.utils.load_utils import load_audio_text_image_video,extract_fbank
from funasr.utils.datadir_writer import DatadirWriter
-from torch.nn.utils.rnn import pad_sequence
+
+from dataclasses import dataclass
class VadStateMachine(Enum):
kVadInStateStartPointNotDetected = 1
@@ -38,7 +39,6 @@
class VadDetectMode(Enum):
kVadSingleUtteranceDetectMode = 0
kVadMutipleUtteranceDetectMode = 1
-
class VADXOptions:
"""
@@ -153,8 +153,10 @@
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):
+ 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
@@ -187,7 +189,7 @@
def GetWinSize(self) -> int:
return int(self.win_size_frame)
- def DetectOneFrame(self, frameState: FrameState, frame_count: int) -> AudioChangeState:
+ def DetectOneFrame(self, frameState: FrameState, frame_count: int, cache: dict={}) -> AudioChangeState:
cur_frame_state = FrameState.kFrameStateSil
if frameState == FrameState.kFrameStateSpeech:
cur_frame_state = 1
@@ -218,6 +220,38 @@
return int(self.frame_size_ms)
+@dataclass
+class StatsItem:
+
+ # init variables
+ data_buf_start_frame = 0
+ frm_cnt = 0
+ latest_confirmed_speech_frame = 0
+ lastest_confirmed_silence_frame = -1
+ continous_silence_frame_count = 0
+ vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
+ confirmed_start_frame = -1
+ confirmed_end_frame = -1
+ number_end_time_detected = 0
+ sil_frame = 0
+ sil_pdf_ids: list
+ noise_average_decibel = -100.0
+ pre_end_silence_detected = False
+ next_seg = True # unused
+
+ output_data_buf = []
+ output_data_buf_offset = 0
+ frame_probs = [] # unused
+ max_end_sil_frame_cnt_thresh: int
+ speech_noise_thres: float
+ scores = None
+ max_time_out = False #unused
+ decibel = []
+ data_buf = None
+ data_buf_all = None
+ waveform = None
+ last_drop_frames = 0
+
@tables.register("model_classes", "FsmnVADStreaming")
class FsmnVADStreaming(nn.Module):
"""
@@ -233,143 +267,82 @@
):
super().__init__()
self.vad_opts = VADXOptions(**kwargs)
- self.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)
-
+
encoder_class = tables.encoder_classes.get(encoder.lower())
encoder = encoder_class(**encoder_conf)
self.encoder = encoder
- # init variables
- 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 = self.vad_opts.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 = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres
- self.speech_noise_thres = self.vad_opts.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
- def AllResetDetection(self):
- 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 = self.vad_opts.sil_pdf_ids
- self.noise_average_decibel = -100.0
- self.pre_end_silence_detected = False
- self.next_seg = True
+ 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 = []
- self.output_data_buf = []
- self.output_data_buf_offset = 0
- self.frame_probs = []
- self.max_end_sil_frame_cnt_thresh = self.vad_opts.max_end_silence_time - self.vad_opts.speech_to_sil_time_thres
- self.speech_noise_thres = self.vad_opts.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
- self.windows_detector.Reset()
+ 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 ResetDetection(self):
- self.continous_silence_frame_count = 0
- self.latest_confirmed_speech_frame = 0
- self.lastest_confirmed_silence_frame = -1
- self.confirmed_start_frame = -1
- self.confirmed_end_frame = -1
- self.vad_state_machine = VadStateMachine.kVadInStateStartPointNotDetected
- self.windows_detector.Reset()
- self.sil_frame = 0
- self.frame_probs = []
-
- if self.output_data_buf:
- assert self.output_data_buf[-1].contain_seg_end_point == True
- drop_frames = int(self.output_data_buf[-1].end_ms / self.vad_opts.frame_in_ms)
- real_drop_frames = drop_frames - self.last_drop_frames
- self.last_drop_frames = drop_frames
- self.data_buf_all = self.data_buf_all[real_drop_frames * int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):]
- self.decibel = self.decibel[real_drop_frames:]
- self.scores = self.scores[:, real_drop_frames:, :]
-
- def ComputeDecibel(self) -> None:
+ 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 self.data_buf_all is None:
- self.data_buf_all = self.waveform[0] # self.data_buf is pointed to self.waveform[0]
- self.data_buf = self.data_buf_all
+ 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:
- self.data_buf_all = torch.cat((self.data_buf_all, self.waveform[0]))
- for offset in range(0, self.waveform.shape[1] - frame_sample_length + 1, frame_shift_length):
- self.decibel.append(
- 10 * math.log10((self.waveform[0][offset: offset + frame_sample_length]).square().sum() + \
+ 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[str, torch.Tensor]) -> None:
- scores = self.encoder(feats, cache).to('cpu') # return B * T * D
+ 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]
- self.frm_cnt += scores.shape[1] # count total frames
- if self.scores is None:
- self.scores = scores # the first calculation
+ cache["stats"].frm_cnt += scores.shape[1] # count total frames
+ if cache["stats"].scores is None:
+ cache["stats"].scores = scores # the first calculation
else:
- self.scores = torch.cat((self.scores, scores), dim=1)
+ cache["stats"].scores = torch.cat((cache["stats"].scores, scores), dim=1)
- def PopDataBufTillFrame(self, frame_idx: int) -> None: # need check again
- while self.data_buf_start_frame < frame_idx:
- if len(self.data_buf) >= int(self.vad_opts.frame_in_ms * self.vad_opts.sample_rate / 1000):
- self.data_buf_start_frame += 1
- self.data_buf = self.data_buf_all[(self.data_buf_start_frame - self.last_drop_frames) * int(
+ 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) -> None:
- self.PopDataBufTillFrame(start_frm)
+ 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(self.data_buf))
- if len(self.data_buf) < expected_sample_number:
+ 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(self.output_data_buf) == 0 or first_frm_is_start_point:
- self.output_data_buf.append(E2EVadSpeechBufWithDoa())
- self.output_data_buf[-1].Reset()
- self.output_data_buf[-1].start_ms = start_frm * self.vad_opts.frame_in_ms
- self.output_data_buf[-1].end_ms = self.output_data_buf[-1].start_ms
- self.output_data_buf[-1].doa = 0
- cur_seg = self.output_data_buf[-1]
+ 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鐜板湪娌″仛浠讳綍鎿嶄綔
@@ -378,10 +351,10 @@
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(self.data_buf):
- print('VAD data_to_pop is bigger than self.data_buf.size()!!!\n')
- data_to_pop = len(self.data_buf)
- expected_sample_number = len(self.data_buf)
+ 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):
@@ -392,79 +365,79 @@
out_pos += 1
if cur_seg.end_ms != start_frm * self.vad_opts.frame_in_ms:
print('Something wrong with the VAD algorithm\n')
- self.data_buf_start_frame += frm_cnt
+ 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):
- self.lastest_confirmed_silence_frame = valid_frame
- if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- self.PopDataBufTillFrame(valid_frame)
+ 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) -> None:
- self.latest_confirmed_speech_frame = valid_frame
- self.PopDataToOutputBuf(valid_frame, 1, False, False, False)
+ 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) -> None:
+ def OnVoiceStart(self, start_frame: int, fake_result: bool = False, cache:dict={}) -> None:
if self.vad_opts.do_start_point_detection:
pass
- if self.confirmed_start_frame != -1:
+ if cache["stats"].confirmed_start_frame != -1:
print('not reset vad properly\n')
else:
- self.confirmed_start_frame = start_frame
+ cache["stats"].confirmed_start_frame = start_frame
- if not fake_result and self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- self.PopDataToOutputBuf(self.confirmed_start_frame, 1, True, False, False)
+ 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) -> None:
- for t in range(self.latest_confirmed_speech_frame + 1, end_frame):
- self.OnVoiceDetected(t)
+ 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 self.confirmed_end_frame != -1:
+ if cache["stats"].confirmed_end_frame != -1:
print('not reset vad properly\n')
else:
- self.confirmed_end_frame = end_frame
+ cache["stats"].confirmed_end_frame = end_frame
if not fake_result:
- self.sil_frame = 0
- self.PopDataToOutputBuf(self.confirmed_end_frame, 1, False, True, is_last_frame)
- self.number_end_time_detected += 1
+ 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) -> None:
+ 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)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ self.OnVoiceEnd(cur_frm_idx, False, True, cache=cache)
+ cache["stats"].vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- def GetLatency(self) -> int:
- return int(self.LatencyFrmNumAtStartPoint() * self.vad_opts.frame_in_ms)
+ def GetLatency(self, cache: dict = {}) -> int:
+ return int(self.LatencyFrmNumAtStartPoint(cache=cache) * self.vad_opts.frame_in_ms)
- def LatencyFrmNumAtStartPoint(self) -> int:
- vad_latency = self.windows_detector.GetWinSize()
+ 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):
+ def GetFrameState(self, t: int, cache: dict = {}):
frame_state = FrameState.kFrameStateInvalid
- cur_decibel = self.decibel[t]
- cur_snr = cur_decibel - self.noise_average_decibel
+ 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)
+ self.DetectOneFrame(frame_state, t, False, cache=cache)
return frame_state
sum_score = 0.0
noise_prob = 0.0
- assert len(self.sil_pdf_ids) == self.vad_opts.silence_pdf_num
- if len(self.sil_pdf_ids) > 0:
- assert len(self.scores) == 1 # 鍙敮鎸乥atch_size = 1鐨勬祴璇�
- sil_pdf_scores = [self.scores[0][t][sil_pdf_id] for sil_pdf_id in self.sil_pdf_ids]
+ 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 # 鍙敮鎸乥atch_size = 1鐨勬祴璇�
+ 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
@@ -476,58 +449,69 @@
frame_prob.speech_prob = speech_prob
frame_prob.score = sum_score
frame_prob.frame_id = t
- self.frame_probs.append(frame_prob)
- if math.exp(speech_prob) >= math.exp(noise_prob) + self.speech_noise_thres:
+ 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 self.noise_average_decibel < -99.9:
- self.noise_average_decibel = cur_decibel
+ if cache["stats"].noise_average_decibel < -99.9:
+ cache["stats"].noise_average_decibel = cur_decibel
else:
- self.noise_average_decibel = (cur_decibel + self.noise_average_decibel * (
+ 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[str, torch.Tensor] = dict(),
+ def forward(self, feats: torch.Tensor, waveform: torch.tensor, cache: dict = {},
is_final: bool = False
):
- if len(cache) == 0:
- self.AllResetDetection()
- self.waveform = waveform # compute decibel for each frame
- self.ComputeDecibel()
- self.ComputeScores(feats, cache)
+ # if len(cache) == 0:
+ # self.AllResetDetection()
+ # self.waveform = waveform # compute decibel for each frame
+ cache["stats"].waveform = waveform
+ self.ComputeDecibel(cache=cache)
+ self.ComputeScores(feats, cache=cache)
if not is_final:
- self.DetectCommonFrames()
+ self.DetectCommonFrames(cache=cache)
else:
- self.DetectLastFrames()
+ self.DetectLastFrames(cache=cache)
segments = []
for batch_num in range(0, feats.shape[0]): # only support batch_size = 1 now
segment_batch = []
- if len(self.output_data_buf) > 0:
- for i in range(self.output_data_buf_offset, len(self.output_data_buf)):
- if not is_final and (not self.output_data_buf[i].contain_seg_start_point or not self.output_data_buf[
+ 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 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 = [self.output_data_buf[i].start_ms, self.output_data_buf[i].end_ms]
+ segment = [cache["stats"].output_data_buf[i].start_ms, cache["stats"].output_data_buf[i].end_ms]
segment_batch.append(segment)
- self.output_data_buf_offset += 1 # need update this parameter
+ cache["stats"].output_data_buf_offset += 1 # need update this parameter
if segment_batch:
segments.append(segment_batch)
- if is_final:
- # reset class variables and clear the dict for the next query
- self.AllResetDetection()
+ # 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"] = {}
-
+ 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)
+
+ stats = StatsItem(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 generate(self,
@@ -544,7 +528,7 @@
self.init_cache(cache, **kwargs)
meta_data = {}
- chunk_size = kwargs.get("chunk_size", 50) # 50ms
+ chunk_size = kwargs.get("chunk_size", 60000) # 50ms
chunk_stride_samples = int(chunk_size * frontend.fs / 1000)
time1 = time.perf_counter()
@@ -585,10 +569,11 @@
"feats": speech,
"waveform": cache["frontend"]["waveforms"],
"is_final": kwargs["is_final"],
- "cache": cache["encoder"]
+ "cache": cache
}
segments_i = self.forward(**batch)
- segments.extend(segments_i)
+ if len(segments_i) > 0:
+ segments.extend(*segments_i)
cache["prev_samples"] = audio_sample[:-m]
@@ -614,30 +599,30 @@
return results, meta_data
- def DetectCommonFrames(self) -> int:
- if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
+ 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(self.frm_cnt - 1 - i - self.last_drop_frames)
- self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False)
+ 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) -> int:
- if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected:
+ 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(self.frm_cnt - 1 - i - self.last_drop_frames)
+ frame_state = self.GetFrameState(cache["stats"].frm_cnt - 1 - i - cache["stats"].last_drop_frames, cache=cache)
if i != 0:
- self.DetectOneFrame(frame_state, self.frm_cnt - 1 - i, False)
+ self.DetectOneFrame(frame_state, cache["stats"].frm_cnt - 1 - i, False, cache=cache)
else:
- self.DetectOneFrame(frame_state, self.frm_cnt - 1, True)
+ 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) -> None:
+ 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:
@@ -646,101 +631,101 @@
tmp_cur_frm_state = FrameState.kFrameStateSil
elif cur_frm_state == FrameState.kFrameStateSil:
tmp_cur_frm_state = FrameState.kFrameStateSil
- state_change = self.windows_detector.DetectOneFrame(tmp_cur_frm_state, cur_frm_idx)
+ 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 = self.continous_silence_frame_count
- self.continous_silence_frame_count = 0
- self.pre_end_silence_detected = False
+ 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 self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
- start_frame = max(self.data_buf_start_frame, cur_frm_idx - self.LatencyFrmNumAtStartPoint())
- self.OnVoiceStart(start_frame)
- self.vad_state_machine = VadStateMachine.kVadInStateInSpeechSegment
+ 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)
- elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- for t in range(self.latest_confirmed_speech_frame + 1, cur_frm_idx):
- self.OnVoiceDetected(t)
- if cur_frm_idx - self.confirmed_start_frame + 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)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ 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)
+ self.OnVoiceDetected(cur_frm_idx, cache=cache)
else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
+ self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
else:
pass
elif AudioChangeState.kChangeStateSpeech2Sil == state_change:
- self.continous_silence_frame_count = 0
- if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+ cache["stats"].continous_silence_frame_count = 0
+ if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
pass
- elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if cur_frm_idx - self.confirmed_start_frame + 1 > \
+ 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)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ 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)
+ self.OnVoiceDetected(cur_frm_idx, cache=cache)
else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
+ self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
else:
pass
elif AudioChangeState.kChangeStateSpeech2Speech == state_change:
- self.continous_silence_frame_count = 0
- if self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if cur_frm_idx - self.confirmed_start_frame + 1 > \
+ 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:
- self.max_time_out = True
- self.OnVoiceEnd(cur_frm_idx, False, False)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ 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)
+ self.OnVoiceDetected(cur_frm_idx, cache=cache)
else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
+ self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
else:
pass
elif AudioChangeState.kChangeStateSil2Sil == state_change:
- self.continous_silence_frame_count += 1
- if self.vad_state_machine == VadStateMachine.kVadInStateStartPointNotDetected:
+ 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 (
- self.continous_silence_frame_count * frm_shift_in_ms > self.vad_opts.max_start_silence_time)) \
- or (is_final_frame and self.number_end_time_detected == 0):
- for t in range(self.lastest_confirmed_silence_frame + 1, cur_frm_idx):
- self.OnSilenceDetected(t)
- self.OnVoiceStart(0, True)
- self.OnVoiceEnd(0, True, False);
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ 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():
- self.OnSilenceDetected(cur_frm_idx - self.LatencyFrmNumAtStartPoint())
- elif self.vad_state_machine == VadStateMachine.kVadInStateInSpeechSegment:
- if self.continous_silence_frame_count * frm_shift_in_ms >= self.max_end_sil_frame_cnt_thresh:
- lookback_frame = int(self.max_end_sil_frame_cnt_thresh / frm_shift_in_ms)
+ 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)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
- elif cur_frm_idx - self.confirmed_start_frame + 1 > \
+ 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)
- self.vad_state_machine = VadStateMachine.kVadInStateEndPointDetected
+ 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 self.continous_silence_frame_count <= int(
+ if cache["stats"].continous_silence_frame_count <= int(
self.vad_opts.lookahead_time_end_point / frm_shift_in_ms):
- self.OnVoiceDetected(cur_frm_idx)
+ self.OnVoiceDetected(cur_frm_idx, cache=cache)
else:
- self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx)
+ self.MaybeOnVoiceEndIfLastFrame(is_final_frame, cur_frm_idx, cache=cache)
else:
pass
- if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
+ if cache["stats"].vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value:
- self.ResetDetection()
+ self.ResetDetection(cache=cache)
--
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