# -*- 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 json
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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, ONNXRuntimeError,
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OrtInferSession, TokenIDConverter, get_logger,
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read_yaml)
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from .utils.postprocess_utils import (sentence_postprocess,
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sentence_postprocess_sentencepiece)
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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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from .utils.utils import pad_list
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logging = get_logger()
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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__(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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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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):
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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(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)
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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(
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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.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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outputs = self.infer(feats, feats_len)
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am_scores, valid_token_lens = outputs[0], outputs[1]
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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 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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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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if self.language == "en-bpe":
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pred = sentence_postprocess_sentencepiece(pred)
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else:
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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(us_peaks_, copy.copy(raw_tokens))
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text_proc, timestamp_proc, _ = sentence_postprocess(raw_tokens, timestamp_raw)
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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(waveform_list[0], timestamp, self.plot_timestamp_to)
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asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens})
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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("font", family='Alibaba PuHuiTi') # 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,
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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: np.ndarray,
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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 [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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def decode_one(self,
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am_score: np.ndarray,
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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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class ContextualParaformer(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__(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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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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):
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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" \
|
"\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(model_dir)
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if quantize:
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model_bb_file = os.path.join(model_dir, 'model_quant.onnx')
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model_eb_file = os.path.join(model_dir, 'model_eb_quant.onnx')
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else:
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model_bb_file = os.path.join(model_dir, 'model.onnx')
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model_eb_file = os.path.join(model_dir, 'model_eb.onnx')
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token_list_file = os.path.join(model_dir, 'tokens.txt')
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self.vocab = {}
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with open(Path(token_list_file), 'r') as fin:
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for i, line in enumerate(fin.readlines()):
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self.vocab[line.strip()] = i
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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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# logging.error(".onnx model not exist, please export first.")
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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.converter = TokenIDConverter(config['token_list'])
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self.tokenizer = CharTokenizer()
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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_bb = OrtInferSession(model_bb_file, device_id, intra_op_num_threads=intra_op_num_threads)
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self.ort_infer_eb = OrtInferSession(model_eb_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.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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def __call__(self,
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wav_content: Union[str, np.ndarray, List[str]],
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hotwords: str,
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**kwargs) -> List:
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# make hotword list
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hotwords, hotwords_length = self.proc_hotword(hotwords)
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# import pdb; pdb.set_trace()
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[bias_embed] = self.eb_infer(hotwords, hotwords_length)
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# index from bias_embed
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bias_embed = bias_embed.transpose(1, 0, 2)
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_ind = np.arange(0, len(hotwords)).tolist()
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bias_embed = bias_embed[_ind, hotwords_length.tolist()]
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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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bias_embed = np.expand_dims(bias_embed, axis=0)
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bias_embed = np.repeat(bias_embed, feats.shape[0], axis=0)
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try:
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outputs = self.bb_infer(feats, feats_len, bias_embed)
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am_scores, valid_token_lens = outputs[0], outputs[1]
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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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preds = ['']
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else:
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preds = self.decode(am_scores, valid_token_lens)
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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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return asr_res
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def proc_hotword(self, hotwords):
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hotwords = hotwords.split(" ")
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hotwords_length = [len(i) - 1 for i in hotwords]
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hotwords_length.append(0)
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hotwords_length = np.array(hotwords_length)
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# hotwords.append('<s>')
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def word_map(word):
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hotwords = []
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for c in word:
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if c not in self.vocab.keys():
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hotwords.append(8403)
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logging.warning("oov character {} found in hotword {}, replaced by <unk>".format(c, word))
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else:
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hotwords.append(self.vocab[c])
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return np.array(hotwords)
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hotword_int = [word_map(i) for i in hotwords]
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# import pdb; pdb.set_trace()
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hotword_int.append(np.array([1]))
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hotwords = pad_list(hotword_int, pad_value=0, max_len=10)
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# import pdb; pdb.set_trace()
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return hotwords, hotwords_length
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def bb_infer(self, feats: np.ndarray,
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feats_len: np.ndarray, bias_embed) -> Tuple[np.ndarray, np.ndarray]:
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outputs = self.ort_infer_bb([feats, feats_len, bias_embed])
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return outputs
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def eb_infer(self, hotwords, hotwords_length):
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outputs = self.ort_infer_eb([hotwords.astype(np.int32), hotwords_length.astype(np.int32)])
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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 [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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def decode_one(self,
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am_score: np.ndarray,
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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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