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

# -*- 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