From 8dab6d184a034ca86eafa644ea0d2100aadfe27d Mon Sep 17 00:00:00 2001
From: jmwang66 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期二, 09 五月 2023 10:58:33 +0800
Subject: [PATCH] Merge pull request #473 from alibaba-damo-academy/dev_smohan
---
funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py | 173 +++++++++++++++++++++++++++++++++++++++++++++++++++++----
1 files changed, 159 insertions(+), 14 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py b/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
index 0b7ecff..022f1e7 100644
--- a/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
+++ b/funasr/runtime/python/onnxruntime/funasr_onnx/vad_bin.py
@@ -1,4 +1,6 @@
# -*- 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
@@ -11,13 +13,18 @@
from .utils.utils import (ONNXRuntimeError,
OrtInferSession, get_logger,
read_yaml)
-from .utils.frontend import WavFrontend
+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",
@@ -53,39 +60,56 @@
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)
+ cache = np.zeros((1, proj_dim, lorder-1, 1)).astype(np.float32)
in_cache.append(cache)
return in_cache
- def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
- waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
+ 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)
- asr_res = []
+ 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('cache', list())
+ 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['cache'] = out_caches
- segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)
+ 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 = ''
- asr_res.append(segments)
- return asr_res
+ return segments
def load_data(self,
wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
@@ -134,4 +158,125 @@
outputs = self.ort_infer(feats)
scores, out_caches = outputs[0], outputs[1:]
return scores, out_caches
-
\ No newline at end of file
+
+
+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
+
--
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