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/demo_contextual_paraformer.py | 2 +-
runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py | 14 +++++++-------
runtime/python/onnxruntime/funasr_onnx/utils/utils.py | 12 ++++++------
3 files changed, 14 insertions(+), 14 deletions(-)
diff --git a/runtime/python/onnxruntime/demo_contextual_paraformer.py b/runtime/python/onnxruntime/demo_contextual_paraformer.py
index 3b698e6..4f8fdbd 100644
--- a/runtime/python/onnxruntime/demo_contextual_paraformer.py
+++ b/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())]
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]:
diff --git a/runtime/python/onnxruntime/funasr_onnx/utils/utils.py b/runtime/python/onnxruntime/funasr_onnx/utils/utils.py
index cf74200..1e7f880 100644
--- a/runtime/python/onnxruntime/funasr_onnx/utils/utils.py
+++ b/runtime/python/onnxruntime/funasr_onnx/utils/utils.py
@@ -7,7 +7,6 @@
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 @@
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 @@
)
mask = mask[ind].expand_as(xs).to(xs.device)
return mask
-
+'''
class TokenIDConverter():
def __init__(self, token_list: Union[List, str],
--
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