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Merge pull request #341 from alibaba-damo-academy/dev_zly2

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support onnxruntime of streaming vad & bug fix
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zhifu gao 2023-04-13 10:05:16 +08:00 committed by GitHub
commit 030043f768
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6 changed files with 400 additions and 55 deletions
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11
funasr/runtime/python/onnxruntime/demo_vad_offline.py Normal file
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@ -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)

18
funasr/runtime/python/onnxruntime/demo_vad.py → funasr/runtime/python/onnxruntime/demo_vad_online.py
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@ -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 @@ for sample_offset in range(0, speech_length, min(step, speech_length - sample_of
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)

35
funasr/runtime/python/onnxruntime/funasr_onnx/utils/e2e_vad.py
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@ -439,10 +439,9 @@ class E2EVadModel():
- 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 @@ class E2EVadModel():
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 @@ class E2EVadModel():
if self.vad_state_machine == VadStateMachine.kVadInStateEndPointDetected and \
self.vad_opts.detect_mode == VadDetectMode.kVadMutipleUtteranceDetectMode.value:
self.ResetDetection()

184
funasr/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py
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@ -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 @@ class WavFrontend():
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 @@ def test():
return feat, feat_len
if __name__ == '__main__':
test()
test()

73
funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
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@ -59,37 +59,48 @@ class Fsmn_vad():
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 @@ class Fsmn_vad():
outputs = self.ort_infer(feats)
scores, out_caches = outputs[0], outputs[1:]
return scores, out_caches

134
funasr/runtime/python/onnxruntime/funasr_onnx/vad_online_bin.py Normal file
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@ -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