# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import os.path
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from pathlib import Path
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from typing import List, Union, Tuple
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import copy
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import librosa
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import numpy as np
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from .utils.utils import (ONNXRuntimeError,
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OrtInferSession, get_logger,
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read_yaml)
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from .utils.frontend import WavFrontend, WavFrontendOnline
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from .utils.e2e_vad import E2EVadModel
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logging = get_logger()
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class Fsmn_vad():
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self, model_dir: Union[str, Path] = None,
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batch_size: int = 1,
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device_id: Union[str, int] = "-1",
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quantize: bool = False,
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intra_op_num_threads: int = 4,
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max_end_sil: int = None,
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cache_dir: str = None,
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**kwargs
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):
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if not Path(model_dir).exists():
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try:
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from modelscope.hub.snapshot_download import snapshot_download
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except:
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raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" \
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"\npip3 install -U modelscope\n" \
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"For the users in China, you could install with the command:\n" \
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"\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple"
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try:
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model_dir = snapshot_download(model_dir, cache_dir=cache_dir)
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except:
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raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
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model_dir)
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model_file = os.path.join(model_dir, 'model.onnx')
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if quantize:
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model_file = os.path.join(model_dir, 'model_quant.onnx')
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if not os.path.exists(model_file):
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print(".onnx is not exist, begin to export onnx")
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try:
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from funasr import AutoModel
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except:
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raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \
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"\npip3 install -U funasr\n" \
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"For the users in China, you could install with the command:\n" \
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"\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
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model = AutoModel(model=model_dir)
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model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
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config_file = os.path.join(model_dir, 'config.yaml')
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cmvn_file = os.path.join(model_dir, 'am.mvn')
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config = read_yaml(config_file)
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self.frontend = WavFrontend(
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cmvn_file=cmvn_file,
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**config['frontend_conf']
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)
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self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
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self.batch_size = batch_size
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self.vad_scorer = E2EVadModel(config["model_conf"])
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self.max_end_sil = max_end_sil if max_end_sil is not None else config["model_conf"]["max_end_silence_time"]
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self.encoder_conf = config["encoder_conf"]
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def prepare_cache(self, in_cache: list = []):
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if len(in_cache) > 0:
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return in_cache
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fsmn_layers = self.encoder_conf["fsmn_layers"]
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proj_dim = self.encoder_conf["proj_dim"]
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lorder = self.encoder_conf["lorder"]
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for i in range(fsmn_layers):
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cache = np.zeros((1, proj_dim, lorder-1, 1)).astype(np.float32)
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in_cache.append(cache)
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return in_cache
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def __call__(self, audio_in: Union[str, np.ndarray, List[str]], **kwargs) -> List:
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waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
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waveform_nums = len(waveform_list)
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is_final = kwargs.get('kwargs', False)
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segments = [[]] * self.batch_size
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for beg_idx in range(0, waveform_nums, self.batch_size):
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end_idx = min(waveform_nums, beg_idx + self.batch_size)
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waveform = waveform_list[beg_idx:end_idx]
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feats, feats_len = self.extract_feat(waveform)
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waveform = np.array(waveform)
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param_dict = kwargs.get('param_dict', dict())
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in_cache = param_dict.get('in_cache', list())
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in_cache = self.prepare_cache(in_cache)
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try:
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t_offset = 0
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step = int(min(feats_len.max(), 6000))
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for t_offset in range(0, int(feats_len), min(step, feats_len - t_offset)):
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if t_offset + step >= feats_len - 1:
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step = feats_len - t_offset
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is_final = True
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else:
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is_final = False
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feats_package = feats[:, t_offset:int(t_offset + step), :]
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waveform_package = waveform[:, t_offset * 160:min(waveform.shape[-1], (int(t_offset + step) - 1) * 160 + 400)]
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inputs = [feats_package]
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# inputs = [feats]
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inputs.extend(in_cache)
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scores, out_caches = self.infer(inputs)
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in_cache = out_caches
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segments_part = self.vad_scorer(scores, waveform_package, is_final=is_final, max_end_sil=self.max_end_sil, online=False)
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# segments = self.vad_scorer(scores, waveform[0][None, :], is_final=is_final, max_end_sil=self.max_end_sil)
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if segments_part:
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for batch_num in range(0, self.batch_size):
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segments[batch_num] += segments_part[batch_num]
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except ONNXRuntimeError:
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# logging.warning(traceback.format_exc())
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logging.warning("input wav is silence or noise")
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segments = ''
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return segments
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def load_data(self,
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wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
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def load_wav(path: str) -> np.ndarray:
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waveform, _ = librosa.load(path, sr=fs)
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return waveform
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if isinstance(wav_content, np.ndarray):
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return [wav_content]
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if isinstance(wav_content, str):
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return [load_wav(wav_content)]
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if isinstance(wav_content, list):
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return [load_wav(path) for path in wav_content]
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raise TypeError(
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f'The type of {wav_content} is not in [str, np.ndarray, list]')
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def extract_feat(self,
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waveform_list: List[np.ndarray]
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) -> Tuple[np.ndarray, np.ndarray]:
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feats, feats_len = [], []
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for waveform in waveform_list:
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speech, _ = self.frontend.fbank(waveform)
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feat, feat_len = self.frontend.lfr_cmvn(speech)
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feats.append(feat)
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feats_len.append(feat_len)
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feats = self.pad_feats(feats, np.max(feats_len))
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feats_len = np.array(feats_len).astype(np.int32)
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return feats, feats_len
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@staticmethod
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def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
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def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
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pad_width = ((0, max_feat_len - cur_len), (0, 0))
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return np.pad(feat, pad_width, 'constant', constant_values=0)
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feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
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feats = np.array(feat_res).astype(np.float32)
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return feats
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def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]:
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outputs = self.ort_infer(feats)
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scores, out_caches = outputs[0], outputs[1:]
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return scores, out_caches
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class Fsmn_vad_online():
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Deep-FSMN for Large Vocabulary Continuous Speech Recognition
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https://arxiv.org/abs/1803.05030
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"""
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def __init__(self, model_dir: Union[str, Path] = None,
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batch_size: int = 1,
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device_id: Union[str, int] = "-1",
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quantize: bool = False,
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intra_op_num_threads: int = 4,
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max_end_sil: int = None,
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cache_dir: str = None,
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**kwargs
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):
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if not Path(model_dir).exists():
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try:
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from modelscope.hub.snapshot_download import snapshot_download
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except:
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raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" \
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"\npip3 install -U modelscope\n" \
|
"For the users in China, you could install with the command:\n" \
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"\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple"
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try:
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model_dir = snapshot_download(model_dir, cache_dir=cache_dir)
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except:
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raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
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model_dir)
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model_file = os.path.join(model_dir, 'model.onnx')
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if quantize:
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model_file = os.path.join(model_dir, 'model_quant.onnx')
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if not os.path.exists(model_file):
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print(".onnx is not exist, begin to export onnx")
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try:
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from funasr import AutoModel
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except:
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raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \
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"\npip3 install -U funasr\n" \
|
"For the users in China, you could install with the command:\n" \
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"\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
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model = AutoModel(model=model_dir)
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model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
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config_file = os.path.join(model_dir, 'config.yaml')
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cmvn_file = os.path.join(model_dir, 'am.mvn')
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config = read_yaml(config_file)
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self.frontend = WavFrontendOnline(
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cmvn_file=cmvn_file,
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**config['frontend_conf']
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)
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self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
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self.batch_size = batch_size
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self.vad_scorer = E2EVadModel(config["model_conf"])
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self.max_end_sil = max_end_sil if max_end_sil is not None else config["model_conf"]["max_end_silence_time"]
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self.encoder_conf = config["encoder_conf"]
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def prepare_cache(self, in_cache: list = []):
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if len(in_cache) > 0:
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return in_cache
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fsmn_layers = self.encoder_conf["fsmn_layers"]
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proj_dim = self.encoder_conf["proj_dim"]
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lorder = self.encoder_conf["lorder"]
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for i in range(fsmn_layers):
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cache = np.zeros((1, proj_dim, lorder - 1, 1)).astype(np.float32)
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in_cache.append(cache)
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return in_cache
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def __call__(self, audio_in: np.ndarray, **kwargs) -> List:
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waveforms = np.expand_dims(audio_in, axis=0)
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param_dict = kwargs.get('param_dict', dict())
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is_final = param_dict.get('is_final', False)
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feats, feats_len = self.extract_feat(waveforms, is_final)
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segments = []
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if feats.size != 0:
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in_cache = param_dict.get('in_cache', list())
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in_cache = self.prepare_cache(in_cache)
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try:
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inputs = [feats]
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inputs.extend(in_cache)
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scores, out_caches = self.infer(inputs)
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param_dict['in_cache'] = out_caches
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waveforms = self.frontend.get_waveforms()
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segments = self.vad_scorer(scores, waveforms, is_final=is_final, max_end_sil=self.max_end_sil,
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online=True)
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except ONNXRuntimeError:
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# logging.warning(traceback.format_exc())
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logging.warning("input wav is silence or noise")
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segments = []
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return segments
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def load_data(self,
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wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
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def load_wav(path: str) -> np.ndarray:
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waveform, _ = librosa.load(path, sr=fs)
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return waveform
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if isinstance(wav_content, np.ndarray):
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return [wav_content]
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if isinstance(wav_content, str):
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return [load_wav(wav_content)]
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if isinstance(wav_content, list):
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return [load_wav(path) for path in wav_content]
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raise TypeError(
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f'The type of {wav_content} is not in [str, np.ndarray, list]')
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def extract_feat(self,
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waveforms: np.ndarray, is_final: bool = False
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) -> Tuple[np.ndarray, np.ndarray]:
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waveforms_lens = np.zeros(waveforms.shape[0]).astype(np.int32)
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for idx, waveform in enumerate(waveforms):
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waveforms_lens[idx] = waveform.shape[-1]
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feats, feats_len = self.frontend.extract_fbank(waveforms, waveforms_lens, is_final)
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# feats.append(feat)
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# feats_len.append(feat_len)
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# feats = self.pad_feats(feats, np.max(feats_len))
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# feats_len = np.array(feats_len).astype(np.int32)
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return feats.astype(np.float32), feats_len.astype(np.int32)
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@staticmethod
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def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
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def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
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pad_width = ((0, max_feat_len - cur_len), (0, 0))
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return np.pad(feat, pad_width, 'constant', constant_values=0)
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feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
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feats = np.array(feat_res).astype(np.float32)
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return feats
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def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]:
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outputs = self.ort_infer(feats)
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scores, out_caches = outputs[0], outputs[1:]
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return scores, out_caches
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