support onnxruntime of streaming vad & bug fix

凌匀

2023-04-12 435a5906e538de4c975c7847acfd99772881e3f1

support onnxruntime of streaming vad & bug fix

 funasr/runtime/python/onnxruntime/demo_vad_offline.py

New file
@@ -0,0 +1,11 @@
import soundfile
from funasr_onnx.vad_bin import Fsmn_vad


model_dir = "/mnt/ailsa.zly/tfbase/espnet_work/FunASR_dev_zly/export/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
wav_path = "/mnt/ailsa.zly/tfbase/espnet_work/FunASR_dev_zly/egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/vad_example_16k.wav"
model = Fsmn_vad(model_dir)

#offline vad
result = model(wav_path)
print(result)

 funasr/runtime/python/onnxruntime/demo_vad_online.py

File was renamed from funasr/runtime/python/onnxruntime/demo_vad.py
@@ -1,21 +1,18 @@
import soundfile
from funasr_onnx import Fsmn_vad
from funasr_onnx.vad_online_bin import Fsmn_vad


model_dir = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch"
wav_path = "/Users/zhifu/Downloads/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav"
model_dir = "/mnt/ailsa.zly/tfbase/espnet_work/FunASR_dev_zly/export/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
wav_path = "/mnt/ailsa.zly/tfbase/espnet_work/FunASR_dev_zly/egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/vad_example_16k.wav"
model = Fsmn_vad(model_dir)

#offline vad
# result = model(wav_path)
# print(result)

#online vad
##online vad
speech, sample_rate = soundfile.read(wav_path)
speech_length = speech.shape[0]

#
sample_offset = 0
step = 160 * 10
step = 1600
param_dict = {'in_cache': []}
for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
    if sample_offset + step >= speech_length - 1:
@@ -26,5 +23,6 @@
    param_dict['is_final'] = is_final
    segments_result = model(audio_in=speech[sample_offset: sample_offset + step],
                            param_dict=param_dict)
    print(segments_result)
    if segments_result:
        print(segments_result)


 funasr/runtime/python/onnxruntime/funasr_onnx/utils/e2e_vad.py

@@ -439,10 +439,9 @@
                        - 1)) / self.vad_opts.noise_frame_num_used_for_snr

        return frame_state
     

    def __call__(self, score: np.ndarray, waveform: np.ndarray,
                is_final: bool = False, max_end_sil: int = 800
                is_final: bool = False, max_end_sil: int = 800, online: bool = False
                ):
        self.max_end_sil_frame_cnt_thresh = max_end_sil - self.vad_opts.speech_to_sil_time_thres
        self.waveform = waveform  # compute decibel for each frame
@@ -457,20 +456,29 @@
            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 self.output_data_buf[i].contain_seg_start_point:
                        continue
                    if not self.next_seg and not self.output_data_buf[i].contain_seg_end_point:
                        continue
                    start_ms = self.output_data_buf[i].start_ms if self.next_seg else -1
                    if self.output_data_buf[i].contain_seg_end_point:
                        end_ms = self.output_data_buf[i].end_ms
                        self.next_seg = True
                        self.output_data_buf_offset += 1
                    if online:
                        if not self.output_data_buf[i].contain_seg_start_point:
                            continue
                        if not self.next_seg and not self.output_data_buf[i].contain_seg_end_point:
                            continue
                        start_ms = self.output_data_buf[i].start_ms if self.next_seg else -1
                        if self.output_data_buf[i].contain_seg_end_point:
                            end_ms = self.output_data_buf[i].end_ms
                            self.next_seg = True
                            self.output_data_buf_offset += 1
                        else:
                            end_ms = -1
                            self.next_seg = False
                    else:
                        end_ms = -1
                        self.next_seg = False
                        if not self.output_data_buf[i].contain_seg_start_point or not self.output_data_buf[
                            i].contain_seg_end_point:
                            continue
                        start_ms = self.output_data_buf[i].start_ms
                        end_ms = self.output_data_buf[i].end_ms
                        self.output_data_buf_offset += 1
                    segment = [start_ms, end_ms]
                    segment_batch.append(segment)

            if segment_batch:
                segments.append(segment_batch)
        if is_final:
@@ -605,3 +613,4 @@
        if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
                self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value:
            self.ResetDetection()


 funasr/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py

@@ -1,6 +1,7 @@
# -*- encoding: utf-8 -*-
from pathlib import Path
from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
import copy

import numpy as np
from typeguard import check_argument_types
@@ -153,6 +154,187 @@
        cmvn = np.array([means, vars])
        return cmvn


class WavFrontendOnline(WavFrontend):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        # self.fbank_fn = knf.OnlineFbank(self.opts)
        # add variables
        self.frame_sample_length = int(self.opts.frame_opts.frame_length_ms * self.opts.frame_opts.samp_freq / 1000)
        self.frame_shift_sample_length = int(self.opts.frame_opts.frame_shift_ms * self.opts.frame_opts.samp_freq / 1000)
        self.waveform = None
        self.reserve_waveforms = None
        self.input_cache = None
        self.lfr_splice_cache = []

    @staticmethod
    # inputs has catted the cache
    def apply_lfr(inputs: np.ndarray, lfr_m: int, lfr_n: int, is_final: bool = False) -> Tuple[
        np.ndarray, np.ndarray, int]:
        """
        Apply lfr with data
        """

        LFR_inputs = []
        T = inputs.shape[0]  # include the right context
        T_lfr = int(np.ceil((T - (lfr_m - 1) // 2) / lfr_n))  # minus the right context: (lfr_m - 1) // 2
        splice_idx = T_lfr
        for i in range(T_lfr):
            if lfr_m <= T - i * lfr_n:
                LFR_inputs.append((inputs[i * lfr_n:i * lfr_n + lfr_m]).reshape(1, -1))
            else:  # process last LFR frame
                if is_final:
                    num_padding = lfr_m - (T - i * lfr_n)
                    frame = (inputs[i * lfr_n:]).reshape(-1)
                    for _ in range(num_padding):
                        frame = np.hstack((frame, inputs[-1]))
                    LFR_inputs.append(frame)
                else:
                    # update splice_idx and break the circle
                    splice_idx = i
                    break
        splice_idx = min(T - 1, splice_idx * lfr_n)
        lfr_splice_cache = inputs[splice_idx:, :]
        LFR_outputs = np.vstack(LFR_inputs)
        return LFR_outputs.astype(np.float32), lfr_splice_cache, splice_idx

    @staticmethod
    def compute_frame_num(sample_length: int, frame_sample_length: int, frame_shift_sample_length: int) -> int:
        frame_num = int((sample_length - frame_sample_length) / frame_shift_sample_length + 1)
        return frame_num if frame_num >= 1 and sample_length >= frame_sample_length else 0


    def fbank(
            self,
            input: np.ndarray,
            input_lengths: np.ndarray
    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
        self.fbank_fn = knf.OnlineFbank(self.opts)
        batch_size = input.shape[0]
        if self.input_cache is None:
            self.input_cache = np.empty((batch_size, 0), dtype=np.float32)
        input = np.concatenate((self.input_cache, input), axis=1)
        frame_num = self.compute_frame_num(input.shape[-1], self.frame_sample_length, self.frame_shift_sample_length)
        # update self.in_cache
        self.input_cache = input[:, -(input.shape[-1] - frame_num * self.frame_shift_sample_length):]
        waveforms = np.empty(0, dtype=np.int16)
        feats_pad = np.empty(0, dtype=np.float32)
        feats_lens = np.empty(0, dtype=np.int32)
        if frame_num:
            waveforms = []
            feats = []
            feats_lens = []
            for i in range(batch_size):
                waveform = input[i]
                waveforms.append(
                    waveform[:((frame_num - 1) * self.frame_shift_sample_length + self.frame_sample_length)])
                waveform = waveform * (1 << 15)
                
                self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
                frames = self.fbank_fn.num_frames_ready
                mat = np.empty([frames, self.opts.mel_opts.num_bins])
                for i in range(frames):
                    mat[i, :] = self.fbank_fn.get_frame(i)
                feat = mat.astype(np.float32)
                feat_len = np.array(mat.shape[0]).astype(np.int32)
                feats.append(mat)
                feats_lens.append(feat_len)

            waveforms = np.stack(waveforms)
            feats_lens = np.array(feats_lens)
            feats_pad = np.array(feats)
        self.fbanks = feats_pad
        self.fbanks_lens = copy.deepcopy(feats_lens)
        return waveforms, feats_pad, feats_lens

    def get_fbank(self) -> Tuple[np.ndarray, np.ndarray]:
        return self.fbanks, self.fbanks_lens

    def lfr_cmvn(
            self,
            input: np.ndarray,
            input_lengths: np.ndarray,
            is_final: bool = False
    ) -> Tuple[np.ndarray, np.ndarray, List[int]]:
        batch_size = input.shape[0]
        feats = []
        feats_lens = []
        lfr_splice_frame_idxs = []
        for i in range(batch_size):
            mat = input[i, :input_lengths[i], :]
            lfr_splice_frame_idx = -1
            if self.lfr_m != 1 or self.lfr_n != 1:
                # update self.lfr_splice_cache in self.apply_lfr
                mat, self.lfr_splice_cache[i], lfr_splice_frame_idx = self.apply_lfr(mat, self.lfr_m, self.lfr_n,
                                                                                     is_final)
            if self.cmvn_file is not None:
                mat = self.apply_cmvn(mat)
            feat_length = mat.shape[0]
            feats.append(mat)
            feats_lens.append(feat_length)
            lfr_splice_frame_idxs.append(lfr_splice_frame_idx)

        feats_lens = np.array(feats_lens)
        feats_pad = np.array(feats)
        return feats_pad, feats_lens, lfr_splice_frame_idxs


    def extract_fbank(
            self, input: np.ndarray, input_lengths: np.ndarray, is_final: bool = False
    ) -> Tuple[np.ndarray, np.ndarray]:
        batch_size = input.shape[0]
        assert batch_size == 1, 'we support to extract feature online only when the batch size is equal to 1 now'
        waveforms, feats, feats_lengths = self.fbank(input, input_lengths)  # input shape: B T D
        if feats.shape[0]:
            self.waveforms = waveforms if self.reserve_waveforms is None else np.concatenate(
                (self.reserve_waveforms, waveforms), axis=1)
            if not self.lfr_splice_cache:
                for i in range(batch_size):
                    self.lfr_splice_cache.append(np.expand_dims(feats[i][0, :], axis=0).repeat((self.lfr_m - 1) // 2, axis=0))
            
            if feats_lengths[0] + self.lfr_splice_cache[0].shape[0] >= self.lfr_m:
                lfr_splice_cache_np = np.stack(self.lfr_splice_cache)  # B T D
                feats = np.concatenate((lfr_splice_cache_np, feats), axis=1)
                feats_lengths += lfr_splice_cache_np[0].shape[0]
                frame_from_waveforms = int(
                    (self.waveforms.shape[1] - self.frame_sample_length) / self.frame_shift_sample_length + 1)
                minus_frame = (self.lfr_m - 1) // 2 if self.reserve_waveforms is None else 0
                feats, feats_lengths, lfr_splice_frame_idxs = self.lfr_cmvn(feats, feats_lengths, is_final)
                if self.lfr_m == 1:
                    self.reserve_waveforms = None
                else:
                    reserve_frame_idx = lfr_splice_frame_idxs[0] - minus_frame
                    # print('reserve_frame_idx:  ' + str(reserve_frame_idx))
                    # print('frame_frame:  ' + str(frame_from_waveforms))
                    self.reserve_waveforms = self.waveforms[:, reserve_frame_idx * self.frame_shift_sample_length:frame_from_waveforms * self.frame_shift_sample_length]
                    sample_length = (frame_from_waveforms - 1) * self.frame_shift_sample_length + self.frame_sample_length
                    self.waveforms = self.waveforms[:, :sample_length]
            else:
                # update self.reserve_waveforms and self.lfr_splice_cache
                self.reserve_waveforms = self.waveforms[:,
                                         :-(self.frame_sample_length - self.frame_shift_sample_length)]
                for i in range(batch_size):
                    self.lfr_splice_cache[i] = np.concatenate((self.lfr_splice_cache[i], feats[i]), axis=0)
                return np.empty(0, dtype=np.float32), feats_lengths
        else:
            if is_final:
                self.waveforms = waveforms if self.reserve_waveforms is None else self.reserve_waveforms
                feats = np.stack(self.lfr_splice_cache)
                feats_lengths = np.zeros(batch_size, dtype=np.int32) + feats.shape[1]
                feats, feats_lengths, _ = self.lfr_cmvn(feats, feats_lengths, is_final)
        if is_final:
            self.cache_reset()
        return feats, feats_lengths

    def get_waveforms(self):
        return self.waveforms

    def cache_reset(self):
        self.fbank_fn = knf.OnlineFbank(self.opts)
        self.reserve_waveforms = None
        self.input_cache = None
        self.lfr_splice_cache = []

def load_bytes(input):
    middle_data = np.frombuffer(input, dtype=np.int16)
    middle_data = np.asarray(middle_data)
@@ -188,4 +370,4 @@
    return feat, feat_len

if __name__ == '__main__':
    test()
    test()

 funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py

@@ -59,37 +59,48 @@
        
    
    def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List:
        # waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
		
        param_dict = kwargs.get('param_dict', dict())
        is_final = param_dict.get('is_final', False)
        audio_in_cache = param_dict.get('audio_in_cache', None)
        audio_in_cum = audio_in
        if audio_in_cache is not None:
            audio_in_cum = np.concatenate((audio_in_cache, audio_in_cum))
        param_dict['audio_in_cache'] = audio_in_cum
        feats, feats_len = self.extract_feat([audio_in_cum])
		
        in_cache = param_dict.get('in_cache', list())
        in_cache = self.prepare_cache(in_cache)
        beg_idx = param_dict.get('beg_idx',0)
        feats = feats[:, beg_idx:beg_idx+8, :]
        param_dict['beg_idx'] = beg_idx + feats.shape[1]
        try:
            inputs = [feats]
            inputs.extend(in_cache)
            scores, out_caches = self.infer(inputs)
            param_dict['in_cache'] = out_caches
            segments = self.vad_scorer(scores, audio_in[None, :], is_final=is_final, max_end_sil=self.max_end_sil)
            # print(segments)
            if len(segments) == 1 and segments[0][0][1] != -1:
                self.frontend.reset_status()
        waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
        waveform_nums = len(waveform_list)
        is_final = kwargs.get('kwargs', False)

        segments = [[]] * self.batch_size
        for beg_idx in range(0, waveform_nums, self.batch_size):
            
			
        except ONNXRuntimeError:
            logging.warning(traceback.format_exc())
            logging.warning("input wav is silence or noise")
            segments = []
            end_idx = min(waveform_nums, beg_idx + self.batch_size)
            waveform = waveform_list[beg_idx:end_idx]
            feats, feats_len = self.extract_feat(waveform)
            waveform = np.array(waveform)
            param_dict = kwargs.get('param_dict', dict())
            in_cache = param_dict.get('in_cache', list())
            in_cache = self.prepare_cache(in_cache)
            try:
                t_offset = 0
                step = int(min(feats_len.max(), 6000))
                for t_offset in range(0, int(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
                    feats_package = feats[:, t_offset:int(t_offset + step), :]
                    waveform_package = waveform[:, t_offset * 160:min(waveform.shape[-1], (int(t_offset + step) - 1) * 160 + 400)]

                    inputs = [feats_package]
                    # inputs = [feats]
                    inputs.extend(in_cache)
                    scores, out_caches = self.infer(inputs)
                    in_cache = out_caches
                    segments_part = self.vad_scorer(scores, waveform_package, is_final=is_final, max_end_sil=self.max_end_sil, online=False)
                    # segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)

                    if segments_part:
                        for batch_num in range(0, self.batch_size):
                            segments[batch_num] += segments_part[batch_num]
				
            except ONNXRuntimeError:
                # logging.warning(traceback.format_exc())
                logging.warning("input wav is silence or noise")
                segments = ''
    
        return segments

@@ -140,4 +151,4 @@
        outputs = self.ort_infer(feats)
        scores, out_caches = outputs[0], outputs[1:]
        return scores, out_caches
	
	

 funasr/runtime/python/onnxruntime/funasr_onnx/vad_online_bin.py

New file
@@ -0,0 +1,134 @@
# -*- encoding: utf-8 -*-

import os.path
from pathlib import Path
from typing import List, Union, Tuple

import copy
import librosa
import numpy as np

from .utils.utils import (ONNXRuntimeError,
                          OrtInferSession, get_logger,
                          read_yaml)
from .utils.frontend import WavFrontendOnline
from .utils.e2e_vad import E2EVadModel

logging = get_logger()


class Fsmn_vad():
    def __init__(self, model_dir: Union[str, Path] = None,
                 batch_size: int = 1,
                 device_id: Union[str, int] = "-1",
                 quantize: bool = False,
                 intra_op_num_threads: int = 4,
                 max_end_sil: int = None,
                 ):
		
        if not Path(model_dir).exists():
            raise FileNotFoundError(f'{model_dir} does not exist.')
		
        model_file = os.path.join(model_dir, 'model.onnx')
        if quantize:
            model_file = os.path.join(model_dir, 'model_quant.onnx')
        config_file = os.path.join(model_dir, 'vad.yaml')
        cmvn_file = os.path.join(model_dir, 'vad.mvn')
        config = read_yaml(config_file)
		
        self.frontend = WavFrontendOnline(
            cmvn_file=cmvn_file,
            **config['frontend_conf']
        )
        self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
        self.batch_size = batch_size
        self.vad_scorer = E2EVadModel(config["vad_post_conf"])
        self.max_end_sil = max_end_sil if max_end_sil is not None else config["vad_post_conf"]["max_end_silence_time"]
        self.encoder_conf = config["encoder_conf"]
	
    def prepare_cache(self, in_cache: list = []):
        if len(in_cache) > 0:
            return in_cache
        fsmn_layers = self.encoder_conf["fsmn_layers"]
        proj_dim = self.encoder_conf["proj_dim"]
        lorder = self.encoder_conf["lorder"]
        for i in range(fsmn_layers):
            cache = np.zeros((1, proj_dim, lorder-1, 1)).astype(np.float32)
            in_cache.append(cache)
        return in_cache
		
	
    def __call__(self, audio_in: np.ndarray, **kwargs) -> List:
        waveforms = np.expand_dims(audio_in, axis=0)
		
        param_dict = kwargs.get('param_dict', dict())
        is_final = param_dict.get('is_final', False)
        feats, feats_len = self.extract_feat(waveforms, is_final)
        segments = []
        if feats.size != 0:
            in_cache = param_dict.get('in_cache', list())
            in_cache = self.prepare_cache(in_cache)
            try:
                inputs = [feats]
                inputs.extend(in_cache)
                scores, out_caches = self.infer(inputs)
                param_dict['in_cache'] = out_caches
                waveforms = self.frontend.get_waveforms()
                segments = self.vad_scorer(scores, waveforms, is_final=is_final, max_end_sil=self.max_end_sil, online=True)


            except ONNXRuntimeError:
                logging.warning(traceback.format_exc())
                logging.warning("input wav is silence or noise")
                segments = []
        return segments

    def load_data(self,
                  wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
        def load_wav(path: str) -> np.ndarray:
            waveform, _ = librosa.load(path, sr=fs)
            return waveform
		
        if isinstance(wav_content, np.ndarray):
            return [wav_content]
		
        if isinstance(wav_content, str):
            return [load_wav(wav_content)]
		
        if isinstance(wav_content, list):
            return [load_wav(path) for path in wav_content]
		
        raise TypeError(
            f'The type of {wav_content} is not in [str, np.ndarray, list]')
	
    def extract_feat(self,
                     waveforms: np.ndarray, is_final: bool = False
                     ) -> Tuple[np.ndarray, np.ndarray]:
        waveforms_lens = np.zeros(waveforms.shape[0]).astype(np.int32)
        for idx, waveform in enumerate(waveforms):
            waveforms_lens[idx] = waveform.shape[-1]

        feats, feats_len = self.frontend.extract_fbank(waveforms, waveforms_lens, is_final)
        # feats.append(feat)
        # feats_len.append(feat_len)

        # feats = self.pad_feats(feats, np.max(feats_len))
        # feats_len = np.array(feats_len).astype(np.int32)
        return feats.astype(np.float32), feats_len.astype(np.int32)

    @staticmethod
    def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
        def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
            pad_width = ((0, max_feat_len - cur_len), (0, 0))
            return np.pad(feat, pad_width, 'constant', constant_values=0)
		
        feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
        feats = np.array(feat_res).astype(np.float32)
        return feats
	
    def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]:
		
        outputs = self.ort_infer(feats)
        scores, out_caches = outputs[0], outputs[1:]
        return scores, out_caches