| | |
|---|
| | | |
|---|
| | | import os.path |
|---|
| | | from pathlib import Path |
|---|
| | | from typing import List, Union, Tuple |
|---|
| | | from typing import List, Union, Tuple, Dict |
|---|
| | | |
|---|
| | | import copy |
|---|
| | | import librosa |
|---|
| | | import numpy as np |
|---|
| | | |
|---|
| | | from .utils.utils import (ONNXRuntimeError, |
|---|
| | | OrtInferSession, get_logger, |
|---|
| | | read_yaml) |
|---|
| | | 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, |
|---|
| | | cache_dir: str = None |
|---|
| | | ): |
|---|
| | | |
|---|
| | | if not Path(model_dir).exists(): |
|---|
| | | try: |
|---|
| | | from modelscope.hub.snapshot_download import snapshot_download |
|---|
| | | except: |
|---|
| | | raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" \ |
|---|
| | | "\npip3 install -U modelscope\n" \ |
|---|
| | | "For the users in China, you could install with the command:\n" \ |
|---|
| | | "\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
|---|
| | | try: |
|---|
| | | model_dir = snapshot_download(model_dir, cache_dir=cache_dir) |
|---|
| | | except: |
|---|
| | | raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format( |
|---|
| | | model_dir) |
|---|
| | | |
|---|
| | | model_file = os.path.join(model_dir, 'model.onnx') |
|---|
| | | if quantize: |
|---|
| | | model_file = os.path.join(model_dir, 'model_quant.onnx') |
|---|
| | | if not os.path.exists(model_file): |
|---|
| | | print(".onnx is not exist, begin to export onnx") |
|---|
| | | try: |
|---|
| | | from funasr.export.export_model import ModelExport |
|---|
| | | except: |
|---|
| | | raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \ |
|---|
| | | "\npip3 install -U funasr\n" \ |
|---|
| | | "For the users in China, you could install with the command:\n" \ |
|---|
| | | "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
|---|
| | | export_model = ModelExport( |
|---|
| | | cache_dir=cache_dir, |
|---|
| | | onnx=True, |
|---|
| | | device="cpu", |
|---|
| | | quant=quantize, |
|---|
| | | ) |
|---|
| | | export_model.export(model_dir) |
|---|
| | | 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) |
|---|
| | | 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 |
|---|
| | | """ |
|---|
| | | |
|---|
| | | 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)] |
|---|
| | | 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, |
|---|
| | | cache_dir: str = None, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | |
|---|
| | | 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 not Path(model_dir).exists(): |
|---|
| | | try: |
|---|
| | | from modelscope.hub.snapshot_download import snapshot_download |
|---|
| | | except: |
|---|
| | | raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" "\npip3 install -U modelscope\n" "For the users in China, you could install with the command:\n" "\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
|---|
| | | try: |
|---|
| | | model_dir = snapshot_download(model_dir, cache_dir=cache_dir) |
|---|
| | | except: |
|---|
| | | raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format( |
|---|
| | | model_dir |
|---|
| | | ) |
|---|
| | | |
|---|
| | | 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 |
|---|
| | | model_file = os.path.join(model_dir, "model.onnx") |
|---|
| | | if quantize: |
|---|
| | | model_file = os.path.join(model_dir, "model_quant.onnx") |
|---|
| | | if not os.path.exists(model_file): |
|---|
| | | print(".onnx does not exist, begin to export onnx") |
|---|
| | | try: |
|---|
| | | from funasr import AutoModel |
|---|
| | | except: |
|---|
| | | raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" "\npip3 install -U funasr\n" "For the users in China, you could install with the command:\n" "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
|---|
| | | |
|---|
| | | 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 |
|---|
| | | model = AutoModel(model=model_dir) |
|---|
| | | model_dir = model.export(type="onnx", quantize=quantize, **kwargs) |
|---|
| | | config_file = os.path.join(model_dir, "config.yaml") |
|---|
| | | cmvn_file = os.path.join(model_dir, "am.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_config = config["model_conf"] |
|---|
| | | self.max_end_sil = ( |
|---|
| | | max_end_sil if max_end_sil is not None else config["model_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): |
|---|
| | | vad_scorer = E2EVadModel(self.vad_scorer_config) |
|---|
| | | 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 = 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 convert_to_wav(input_path, output_path): |
|---|
| | | from pydub import AudioSegment |
|---|
| | | try: |
|---|
| | | audio = AudioSegment.from_mp3(input_path) |
|---|
| | | audio.export(output_path, format="wav") |
|---|
| | | print("音频文件为mp3格式，已转换为wav格式") |
|---|
| | | |
|---|
| | | except Exception as e: |
|---|
| | | print(f"转换失败:{e}") |
|---|
| | | |
|---|
| | | def load_wav(path: str) -> np.ndarray: |
|---|
| | | if not path.lower().endswith('.wav'): |
|---|
| | | import os |
|---|
| | | input_path = path |
|---|
| | | path = os.path.splitext(path)[0]+'.wav' |
|---|
| | | convert_to_wav(input_path,path) #将mp3格式转换成wav格式 |
|---|
| | | |
|---|
| | | 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, |
|---|
| | | cache_dir: str = None |
|---|
| | | ): |
|---|
| | | if not Path(model_dir).exists(): |
|---|
| | | try: |
|---|
| | | from modelscope.hub.snapshot_download import snapshot_download |
|---|
| | | except: |
|---|
| | | raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" \ |
|---|
| | | "\npip3 install -U modelscope\n" \ |
|---|
| | | "For the users in China, you could install with the command:\n" \ |
|---|
| | | "\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
|---|
| | | try: |
|---|
| | | model_dir = snapshot_download(model_dir, cache_dir=cache_dir) |
|---|
| | | except: |
|---|
| | | raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format( |
|---|
| | | model_dir) |
|---|
| | | |
|---|
| | | model_file = os.path.join(model_dir, 'model.onnx') |
|---|
| | | if quantize: |
|---|
| | | model_file = os.path.join(model_dir, 'model_quant.onnx') |
|---|
| | | if not os.path.exists(model_file): |
|---|
| | | print(".onnx is not exist, begin to export onnx") |
|---|
| | | try: |
|---|
| | | from funasr.export.export_model import ModelExport |
|---|
| | | except: |
|---|
| | | raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \ |
|---|
| | | "\npip3 install -U funasr\n" \ |
|---|
| | | "For the users in China, you could install with the command:\n" \ |
|---|
| | | "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
|---|
| | | export_model = ModelExport( |
|---|
| | | cache_dir=cache_dir, |
|---|
| | | onnx=True, |
|---|
| | | device="cpu", |
|---|
| | | quant=quantize, |
|---|
| | | ) |
|---|
| | | export_model.export(model_dir) |
|---|
| | | 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 |
|---|
| | | 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, |
|---|
| | | cache_dir: str = None, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | if not Path(model_dir).exists(): |
|---|
| | | try: |
|---|
| | | from modelscope.hub.snapshot_download import snapshot_download |
|---|
| | | except: |
|---|
| | | raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" "\npip3 install -U modelscope\n" "For the users in China, you could install with the command:\n" "\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
|---|
| | | try: |
|---|
| | | model_dir = snapshot_download(model_dir, cache_dir=cache_dir) |
|---|
| | | except: |
|---|
| | | raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format( |
|---|
| | | model_dir |
|---|
| | | ) |
|---|
| | | |
|---|
| | | model_file = os.path.join(model_dir, "model.onnx") |
|---|
| | | if quantize: |
|---|
| | | model_file = os.path.join(model_dir, "model_quant.onnx") |
|---|
| | | if not os.path.exists(model_file): |
|---|
| | | print(".onnx does not exist, begin to export onnx") |
|---|
| | | try: |
|---|
| | | from funasr import AutoModel |
|---|
| | | except: |
|---|
| | | raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" "\npip3 install -U funasr\n" "For the users in China, you could install with the command:\n" "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
|---|
| | | |
|---|
| | | model = AutoModel(model=model_dir) |
|---|
| | | model_dir = model.export(type="onnx", quantize=quantize, **kwargs) |
|---|
| | | |
|---|
| | | config_file = os.path.join(model_dir, "config.yaml") |
|---|
| | | self.cmvn_file = os.path.join(model_dir, "am.mvn") |
|---|
| | | self.config = read_yaml(config_file) |
|---|
| | | |
|---|
| | | self.ort_infer = OrtInferSession( |
|---|
| | | model_file, device_id, intra_op_num_threads=intra_op_num_threads |
|---|
| | | ) |
|---|
| | | self.batch_size = batch_size |
|---|
| | | self.max_end_sil = ( |
|---|
| | | max_end_sil if max_end_sil is not None else self.config["model_conf"]["max_end_silence_time"] |
|---|
| | | ) |
|---|
| | | self.encoder_conf = self.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: Dict = kwargs.get("param_dict", dict()) |
|---|
| | | is_final = param_dict.get("is_final", False) |
|---|
| | | frontend: WavFrontendOnline = param_dict.get("frontend", WavFrontendOnline(cmvn_file=self.cmvn_file, **self.config["frontend_conf"])) |
|---|
| | | feats, feats_len = self.extract_feat(frontend=frontend, waveforms=waveforms, is_final=is_final) |
|---|
| | | segments = [] |
|---|
| | | if feats.size != 0: |
|---|
| | | in_cache = param_dict.get("in_cache", list()) |
|---|
| | | in_cache = self.prepare_cache(in_cache) |
|---|
| | | vad_scorer = param_dict.get("vad_scorer", E2EVadModel(self.config["model_conf"])) |
|---|
| | | try: |
|---|
| | | inputs = [feats] |
|---|
| | | inputs.extend(in_cache) |
|---|
| | | scores, out_caches = self.infer(inputs) |
|---|
| | | param_dict["in_cache"] = out_caches |
|---|
| | | waveforms = frontend.get_waveforms() |
|---|
| | | segments = 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 = [] |
|---|
| | | param_dict.update({"frontend": frontend, "vad_scorer": vad_scorer}) |
|---|
| | | 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, frontend: WavFrontendOnline, 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 = 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 |
|---|
