update funasr-onnx

runtime/python/onnxruntime/demo_contextual_paraformer.py

@@ -1,7 +1,7 @@
 from funasr_onnx import ContextualParaformer
 from pathlib import Path
 
-model_dir = "./export/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404"
+model_dir = "../export/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404" # your export dir
 model = ContextualParaformer(model_dir, batch_size=1)
 
 wav_path = ['{}/.cache/modelscope/hub/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/example/asr_example.wav'.format(Path.home())]

runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py

@@ -7,7 +7,6 @@ from pathlib import Path
 from typing import List, Union, Tuple
 
 import copy
-import torch
 import librosa
 import numpy as np
 
@@ -18,7 +17,7 @@ from .utils.postprocess_utils import (sentence_postprocess,
                                       sentence_postprocess_sentencepiece)
 from .utils.frontend import WavFrontend
 from .utils.timestamp_utils import time_stamp_lfr6_onnx
-from .utils.utils import pad_list, make_pad_mask
+from .utils.utils import pad_list
 
 logging = get_logger()
 
@@ -309,7 +308,7 @@ class ContextualParaformer(Paraformer):
         # index from bias_embed
         bias_embed = bias_embed.transpose(1, 0, 2)
         _ind = np.arange(0, len(hotwords)).tolist()
-        bias_embed = bias_embed[_ind, hotwords_length.cpu().numpy().tolist()]
+        bias_embed = bias_embed[_ind, hotwords_length.tolist()]
         waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
         waveform_nums = len(waveform_list)
         asr_res = []
@@ -336,7 +335,7 @@ class ContextualParaformer(Paraformer):
         hotwords = hotwords.split(" ")
         hotwords_length = [len(i) - 1 for i in hotwords]
         hotwords_length.append(0)
-        hotwords_length = torch.Tensor(hotwords_length).to(torch.int32)
+        hotwords_length = np.array(hotwords_length)
         # hotwords.append('<s>')
         def word_map(word):
             hotwords = []
@@ -346,11 +345,12 @@ class ContextualParaformer(Paraformer):
                     logging.warning("oov character {} found in hotword {}, replaced by <unk>".format(c, word))
                 else:
                     hotwords.append(self.vocab[c])
-            return torch.tensor(hotwords)
+            return np.array(hotwords)
         hotword_int = [word_map(i) for i in hotwords]
         # import pdb; pdb.set_trace()
-        hotword_int.append(torch.tensor([1]))
+        hotword_int.append(np.array([1]))
         hotwords = pad_list(hotword_int, pad_value=0, max_len=10)
+        # import pdb; pdb.set_trace()
         return hotwords, hotwords_length
 
     def bb_infer(self, feats: np.ndarray,
@@ -359,7 +359,7 @@ class ContextualParaformer(Paraformer):
         return outputs
 
     def eb_infer(self, hotwords, hotwords_length):
-        outputs = self.ort_infer_eb([hotwords.to(torch.int32).numpy(), hotwords_length.to(torch.int32).numpy()])
+        outputs = self.ort_infer_eb([hotwords.astype(np.int32), hotwords_length.astype(np.int32)])
         return outputs
 
     def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:

runtime/python/onnxruntime/funasr_onnx/utils/utils.py

@@ -7,7 +7,6 @@ from pathlib import Path
 from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
 
 import re
-import torch
 import numpy as np
 import yaml
 try:
@@ -27,14 +26,15 @@ def pad_list(xs, pad_value, max_len=None):
     n_batch = len(xs)
     if max_len is None:
         max_len = max(x.size(0) for x in xs)
-    pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)
-
+    # pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)
+    # numpy format
+    pad = np.zeros((n_batch, max_len)).astype(np.int32)
     for i in range(n_batch):
-        pad[i, : xs[i].size(0)] = xs[i]
+        pad[i, : xs[i].shape[0]] = xs[i]
 
     return pad
 
-
+'''
 def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None):
     if length_dim == 0:
         raise ValueError("length_dim cannot be 0: {}".format(length_dim))
@@ -67,7 +67,7 @@ def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None):
         )
         mask = mask[ind].expand_as(xs).to(xs.device)
     return mask
-
+'''
 
 class TokenIDConverter():
     def __init__(self, token_list: Union[List, str],