# -*- encoding: utf-8 -*-
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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 CharTokenizer, Hypothesis, TokenIDConverter, get_logger, read_yaml
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from .utils.postprocess_utils import sentence_postprocess
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from .utils.frontend import WavFrontend
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from .utils.timestamp_utils import time_stamp_lfr6_onnx
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logging = get_logger()
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import torch
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import json
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class Paraformer:
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2206.08317
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"""
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def __init__(
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self,
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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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plot_timestamp_to: str = "",
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quantize: bool = False,
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intra_op_num_threads: int = 4,
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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" "\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"
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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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)
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model_file = os.path.join(model_dir, "model.torchscripts")
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if quantize:
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model_file = os.path.join(model_dir, "model_quant.torchscripts")
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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" "\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"
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model = AutoModel(model=model_dir)
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model_dir = model.export(type="torchscript", 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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token_list = os.path.join(model_dir, "tokens.json")
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with open(token_list, "r", encoding="utf-8") as f:
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token_list = json.load(f)
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self.converter = TokenIDConverter(token_list)
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self.tokenizer = CharTokenizer()
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self.frontend = WavFrontend(cmvn_file=cmvn_file, **config["frontend_conf"])
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self.ort_infer = torch.jit.load(model_file)
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self.batch_size = batch_size
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self.device_id = device_id
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self.plot_timestamp_to = plot_timestamp_to
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if "predictor_bias" in config["model_conf"].keys():
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self.pred_bias = config["model_conf"]["predictor_bias"]
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else:
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self.pred_bias = 0
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if "lang" in config:
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self.language = config["lang"]
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else:
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self.language = None
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def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
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waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
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waveform_nums = len(waveform_list)
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asr_res = []
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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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feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
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try:
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with torch.no_grad():
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if int(self.device_id) == -1:
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outputs = self.ort_infer(feats, feats_len)
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am_scores, valid_token_lens = outputs[0], outputs[1]
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else:
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outputs = self.ort_infer(feats.cuda(), feats_len.cuda())
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am_scores, valid_token_lens = outputs[0].cpu(), outputs[1].cpu()
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if len(outputs) == 4:
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# for BiCifParaformer Inference
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us_alphas, us_peaks = outputs[2], outputs[3]
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else:
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us_alphas, us_peaks = None, None
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except:
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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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preds = [""]
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else:
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preds = self.decode(am_scores, valid_token_lens)
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if us_peaks is None:
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for pred in preds:
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pred = sentence_postprocess(pred)
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asr_res.append({"preds": pred})
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else:
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for pred, us_peaks_ in zip(preds, us_peaks):
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raw_tokens = pred
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timestamp, timestamp_raw = time_stamp_lfr6_onnx(
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us_peaks_, copy.copy(raw_tokens)
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)
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text_proc, timestamp_proc, _ = sentence_postprocess(
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raw_tokens, timestamp_raw
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)
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# logging.warning(timestamp)
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if len(self.plot_timestamp_to):
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self.plot_wave_timestamp(
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waveform_list[0], timestamp, self.plot_timestamp_to
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)
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asr_res.append(
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{
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"preds": text_proc,
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"timestamp": timestamp_proc,
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"raw_tokens": raw_tokens,
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}
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)
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return asr_res
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def plot_wave_timestamp(self, wav, text_timestamp, dest):
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# TODO: Plot the wav and timestamp results with matplotlib
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import matplotlib
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matplotlib.use("Agg")
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matplotlib.rc(
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"font", family="Alibaba PuHuiTi"
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) # set it to a font that your system supports
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import matplotlib.pyplot as plt
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fig, ax1 = plt.subplots(figsize=(11, 3.5), dpi=320)
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ax2 = ax1.twinx()
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ax2.set_ylim([0, 2.0])
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# plot waveform
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ax1.set_ylim([-0.3, 0.3])
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time = np.arange(wav.shape[0]) / 16000
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ax1.plot(time, wav / wav.max() * 0.3, color="gray", alpha=0.4)
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# plot lines and text
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for char, start, end in text_timestamp:
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ax1.vlines(start, -0.3, 0.3, ls="--")
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ax1.vlines(end, -0.3, 0.3, ls="--")
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x_adj = 0.045 if char != "<sil>" else 0.12
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ax1.text((start + end) * 0.5 - x_adj, 0, char)
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# plt.legend()
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plotname = "{}/timestamp.png".format(dest)
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plt.savefig(plotname, bbox_inches="tight")
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def load_data(self, 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(f"The type of {wav_content} is not in [str, np.ndarray, list]")
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def extract_feat(self, waveform_list: List[np.ndarray]) -> 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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feats = torch.from_numpy(feats).type(torch.float32)
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feats_len = torch.from_numpy(feats_len).type(torch.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: np.ndarray, feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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outputs = self.ort_infer([feats, feats_len])
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return outputs
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def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:
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return [
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self.decode_one(am_score, token_num)
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for am_score, token_num in zip(am_scores, token_nums)
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]
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def decode_one(self, am_score: np.ndarray, valid_token_num: int) -> List[str]:
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yseq = am_score.argmax(axis=-1)
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score = am_score.max(axis=-1)
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score = np.sum(score, axis=-1)
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# pad with mask tokens to ensure compatibility with sos/eos tokens
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# asr_model.sos:1 asr_model.eos:2
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yseq = np.array([1] + yseq.tolist() + [2])
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hyp = Hypothesis(yseq=yseq, score=score)
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# remove sos/eos and get results
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last_pos = -1
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token_int = hyp.yseq[1:last_pos].tolist()
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# remove blank symbol id, which is assumed to be 0
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token_int = list(filter(lambda x: x not in (0, 2), token_int))
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# Change integer-ids to tokens
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token = self.converter.ids2tokens(token_int)
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token = token[: valid_token_num - self.pred_bias]
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# texts = sentence_postprocess(token)
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return token
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