From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py | 34 ++++++++++++++++++++++++----------
1 files changed, 24 insertions(+), 10 deletions(-)
diff --git a/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py b/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py
index 71cf434..7afd083 100644
--- a/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py
+++ b/runtime/python/onnxruntime/funasr_onnx/paraformer_bin.py
@@ -7,17 +7,17 @@
from typing import List, Union, Tuple
import copy
-import torch
import librosa
import numpy as np
from .utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError,
OrtInferSession, TokenIDConverter, get_logger,
read_yaml)
-from .utils.postprocess_utils import sentence_postprocess
+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()
@@ -36,7 +36,6 @@
intra_op_num_threads: int = 4,
cache_dir: str = None
):
-
if not Path(model_dir).exists():
try:
from modelscope.hub.snapshot_download import snapshot_download
@@ -87,6 +86,10 @@
self.pred_bias = config['model_conf']['predictor_bias']
else:
self.pred_bias = 0
+ if "lang" in config:
+ self.language = config['lang']
+ else:
+ self.language = None
def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
@@ -112,7 +115,10 @@
preds = self.decode(am_scores, valid_token_lens)
if us_peaks is None:
for pred in preds:
- pred = sentence_postprocess(pred)
+ if self.language == "en-bpe":
+ pred = sentence_postprocess_sentencepiece(pred)
+ else:
+ pred = sentence_postprocess(pred)
asr_res.append({'preds': pred})
else:
for pred, us_peaks_ in zip(preds, us_peaks):
@@ -242,6 +248,13 @@
if not Path(model_dir).exists():
try:
+ from modelscope.hub.snapshot_download import snapshot_download
+ except:
+ 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"
+ try:
model_dir = snapshot_download(model_dir, cache_dir=cache_dir)
except:
raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(model_dir)
@@ -295,7 +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 = []
@@ -322,7 +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 = []
@@ -332,11 +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,
@@ -345,7 +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]:
--
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