From f79d31d86010a95ad45efe6cac5e5d0f95e4f35a Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期三, 10 一月 2024 11:21:03 +0800
Subject: [PATCH] update funasr-onnx
---
runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py | 14 +++++++-------
1 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py b/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py
index c4c558e..7afd083 100644
--- a/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py
+++ b/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py
@@ -7,7 +7,6 @@
from typing import List, Union, Tuple
import copy
-import torch
import librosa
import numpy as np
@@ -18,7 +17,7 @@
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 @@
# 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 @@
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 @@
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 @@
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]:
--
Gitblit v1.9.1