From 925910c5995bac39e9c5907c40070a30edfabe60 Mon Sep 17 00:00:00 2001
From: 维石 <shixian.shi@alibaba-inc.com>
Date: 星期二, 23 七月 2024 17:48:48 +0800
Subject: [PATCH] update onnx batch inference
---
runtime/python/libtorch/funasr_torch/sensevoice_bin.py | 89 +++++++++++++++++++++++++++++++++++++++-----
1 files changed, 78 insertions(+), 11 deletions(-)
diff --git a/runtime/python/libtorch/funasr_torch/sensevoice_bin.py b/runtime/python/libtorch/funasr_torch/sensevoice_bin.py
index 11cd2c9..f103fea 100644
--- a/runtime/python/libtorch/funasr_torch/sensevoice_bin.py
+++ b/runtime/python/libtorch/funasr_torch/sensevoice_bin.py
@@ -81,27 +81,94 @@
self.lid_int_dict = {24884: 3, 24885: 4, 24888: 7, 24892: 11, 24896: 12, 24992: 13}
self.textnorm_dict = {"withitn": 14, "woitn": 15}
self.textnorm_int_dict = {25016: 14, 25017: 15}
+
+ def _get_lid(self, lid):
+ if lid in list(self.lid_dict.keys()):
+ return self.lid_dict[lid]
+ else:
+ raise ValueError(
+ f"The language {l} is not in {list(self.lid_dict.keys())}"
+ )
+
+ def _get_tnid(self, tnid):
+ if tnid in list(self.textnorm_dict.keys()):
+ return self.textnorm_dict[tnid]
+ else:
+ raise ValueError(
+ f"The textnorm {tnid} is not in {list(self.textnorm_dict.keys())}"
+ )
+
+ def read_tags(self, language_input, textnorm_input):
+ # handle language
+ if isinstance(language_input, list):
+ language_list = []
+ for l in language_input:
+ language_list.append(self._get_lid(l))
+ elif isinstance(language_input, str):
+ # if is existing file
+ if os.path.exists(language_input):
+ language_file = open(language_input, "r").readlines()
+ language_list = [
+ self._get_lid(l.strip())
+ for l in language_file
+ ]
+ else:
+ language_list = [self._get_lid(language_input)]
+ else:
+ raise ValueError(
+ f"Unsupported type {type(language_input)} for language_input"
+ )
+ # handle textnorm
+ if isinstance(textnorm_input, list):
+ textnorm_list = []
+ for tn in textnorm_input:
+ textnorm_list.append(self._get_tnid(tn))
+ elif isinstance(textnorm_input, str):
+ # if is existing file
+ if os.path.exists(textnorm_input):
+ textnorm_file = open(textnorm_input, "r").readlines()
+ textnorm_list = [
+ self._get_tnid(tn.strip())
+ for tn in textnorm_file
+ ]
+ else:
+ textnorm_list = [self._get_tnid(textnorm_input)]
+ else:
+ raise ValueError(
+ f"Unsupported type {type(textnorm_input)} for textnorm_input"
+ )
+ return language_list, textnorm_list
- def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
-
- language = self.lid_dict[kwargs.get("language", "auto")]
- use_itn = kwargs.get("use_itn", False)
- textnorm = kwargs.get("text_norm", None)
- if textnorm is None:
- textnorm = "withitn" if use_itn else "woitn"
- textnorm = self.textnorm_dict[textnorm]
-
+ def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs):
+ language_input = kwargs.get("language", "auto")
+ textnorm_input = kwargs.get("textnorm", "woitn")
+ language_list, textnorm_list = self.read_tags(language_input, textnorm_input)
+
waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
waveform_nums = len(waveform_list)
+
+ assert len(language_list) == 1 or len(language_list) == waveform_nums, \
+ "length of parsed language list should be 1 or equal to the number of waveforms"
+ assert len(textnorm_list) == 1 or len(textnorm_list) == waveform_nums, \
+ "length of parsed textnorm list should be 1 or equal to the number of waveforms"
+
asr_res = []
for beg_idx in range(0, waveform_nums, self.batch_size):
end_idx = min(waveform_nums, beg_idx + self.batch_size)
feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
+ _language_list = language_list[beg_idx:end_idx]
+ _textnorm_list = textnorm_list[beg_idx:end_idx]
+ B = feats.shape[0]
+ if len(_language_list) == 1 and B != 1:
+ _language_list = _language_list * B
+ if len(_textnorm_list) == 1 and B != 1:
+ _textnorm_list = _textnorm_list * B
+
ctc_logits, encoder_out_lens = self.ort_infer(
torch.Tensor(feats).to(self.device),
torch.Tensor(feats_len).to(self.device),
- torch.tensor([language]).to(self.device),
- torch.tensor([textnorm]).to(self.device),
+ torch.tensor([_language_list]).to(self.device),
+ torch.tensor([_textnorm_list]).to(self.device),
)
# support batch_size=1 only currently
x = ctc_logits[0, : encoder_out_lens[0].item(), :]
--
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