deepgeek/lite-avatar
2
0
Fork 0
mirror of https://github.com/HumanAIGC/lite-avatar.git synced 2026-02-05 18:09:20 +08:00
Code Issues Packages Projects Releases Wiki Activity

add files

Browse Source
This commit is contained in:
烨玮
2025-02-20 12:17:03 +08:00
parent a21dd4555c
commit edd008441b
667 changed files with 473123 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

282
funasr_local/runtime/python/onnxruntime/funasr_onnx/vad_bin.py Normal file
View File

@@ -0,0 +1,282 @@
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
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 WavFrontend, WavFrontendOnline
from .utils.e2e_vad import E2EVadModel
logging = get_logger()
class Fsmn_vad():
"""
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, 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 = WavFrontend(
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: Union[str, np.ndarray, List[str]], **kwargs) -> List:
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):
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
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,
waveform_list: List[np.ndarray]
) -> Tuple[np.ndarray, np.ndarray]:
feats, feats_len = [], []
for waveform in waveform_list:
speech, _ = self.frontend.fbank(waveform)
feat, feat_len = self.frontend.lfr_cmvn(speech)
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, feats_len
@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
class Fsmn_vad_online():
"""
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, 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