From ae49b2a8e1bc676e6014d8a12ebeec947b655e3e Mon Sep 17 00:00:00 2001
From: 莫拉古 <61447879+yechaoying@users.noreply.github.com>
Date: 星期五, 29 十一月 2024 09:55:43 +0800
Subject: [PATCH] 变量名写错了 (#2249)
---
runtime/python/libtorch/funasr_torch/sensevoice_bin.py | 164 +++++++++++++++++++++++++++++++++++++++++++-----------
1 files changed, 131 insertions(+), 33 deletions(-)
diff --git a/runtime/python/libtorch/funasr_torch/sensevoice_bin.py b/runtime/python/libtorch/funasr_torch/sensevoice_bin.py
index d2e3cde..d4444e7 100644
--- a/runtime/python/libtorch/funasr_torch/sensevoice_bin.py
+++ b/runtime/python/libtorch/funasr_torch/sensevoice_bin.py
@@ -17,11 +17,12 @@
read_yaml,
)
from .utils.frontend import WavFrontend
+from .utils.sentencepiece_tokenizer import SentencepiecesTokenizer
logging = get_logger()
-class SenseVoiceSmallTorchScript:
+class SenseVoiceSmall:
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
@@ -32,62 +33,160 @@
self,
model_dir: Union[str, Path] = None,
batch_size: int = 1,
- device_id: Union[str, int] = "-1",
plot_timestamp_to: str = "",
quantize: bool = False,
intra_op_num_threads: int = 4,
cache_dir: str = None,
**kwargs,
):
+ self.device = kwargs.get("device", "cpu")
+ 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
+ )
+
+ model_file = os.path.join(model_dir, "model.torchscript")
if quantize:
model_file = os.path.join(model_dir, "model_quant.torchscript")
- else:
- model_file = os.path.join(model_dir, "model.torchscript")
+ if not os.path.exists(model_file):
+ print(".torchscripts does not exist, begin to export torchscript")
+ try:
+ from funasr import AutoModel
+ except:
+ raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" "\npip3 install -U funasr\n" "For the users in China, you could install with the command:\n" "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
+
+ model = AutoModel(model=model_dir)
+ model_dir = model.export(type="torchscript", quantize=quantize, **kwargs)
config_file = os.path.join(model_dir, "config.yaml")
cmvn_file = os.path.join(model_dir, "am.mvn")
config = read_yaml(config_file)
- # token_list = os.path.join(model_dir, "tokens.json")
- # with open(token_list, "r", encoding="utf-8") as f:
- # token_list = json.load(f)
- # self.converter = TokenIDConverter(token_list)
- self.tokenizer = CharTokenizer()
- config["frontend_conf"]['cmvn_file'] = cmvn_file
+ self.tokenizer = SentencepiecesTokenizer(
+ bpemodel=os.path.join(model_dir, "chn_jpn_yue_eng_ko_spectok.bpe.model")
+ )
+ config["frontend_conf"]["cmvn_file"] = cmvn_file
self.frontend = WavFrontend(**config["frontend_conf"])
self.ort_infer = torch.jit.load(model_file)
self.batch_size = batch_size
self.blank_id = 0
+ self.lid_dict = {"auto": 0, "zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
+ 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]],
- language: List,
- textnorm: List,
- tokenizer=None,
- **kwargs) -> List:
+ 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])
- ctc_logits, encoder_out_lens = self.ort_infer(torch.Tensor(feats),
- torch.Tensor(feats_len),
- torch.tensor(language),
- torch.tensor(textnorm)
- )
- # support batch_size=1 only currently
- x = ctc_logits[0, : encoder_out_lens[0].item(), :]
- yseq = x.argmax(dim=-1)
- yseq = torch.unique_consecutive(yseq, dim=-1)
-
- mask = yseq != self.blank_id
- token_int = yseq[mask].tolist()
+ _language_list = language_list[beg_idx:end_idx]
+ _textnorm_list = textnorm_list[beg_idx:end_idx]
+ if not len(_language_list):
+ _language_list = [language_list[0]]
+ _textnorm_list = [textnorm_list[0]]
+ 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
- if tokenizer is not None:
- asr_res.append(tokenizer.tokens2text(token_int))
- else:
- asr_res.append(token_int)
+ ctc_logits, encoder_out_lens = self.ort_infer(
+ torch.Tensor(feats).to(self.device),
+ torch.Tensor(feats_len).to(self.device),
+ torch.tensor(_language_list).to(self.device),
+ torch.tensor(_textnorm_list).to(self.device),
+ )
+ for b in range(feats.shape[0]):
+ # back to torch.Tensor
+ if isinstance(ctc_logits, np.ndarray):
+ ctc_logits = torch.from_numpy(ctc_logits).float()
+ # support batch_size=1 only currently
+ x = ctc_logits[b, : encoder_out_lens[b].item(), :]
+ yseq = x.argmax(dim=-1)
+ yseq = torch.unique_consecutive(yseq, dim=-1)
+
+ mask = yseq != self.blank_id
+ token_int = yseq[mask].tolist()
+
+ asr_res.append(self.tokenizer.decode(token_int))
+
return asr_res
def load_data(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
@@ -127,4 +226,3 @@
feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
feats = np.array(feat_res).astype(np.float32)
return feats
-
--
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