From 1b978426040c836059cd0b941648e8515805b389 Mon Sep 17 00:00:00 2001
From: 北念 <lzr265946@alibaba-inc.com>
Date: 星期二, 23 七月 2024 11:45:43 +0800
Subject: [PATCH] add sensevoice scp2jsonl
---
runtime/python/libtorch/funasr_torch/sensevoice_bin.py | 78 +++++++++++++++++++++++++-------------
1 files changed, 51 insertions(+), 27 deletions(-)
diff --git a/runtime/python/libtorch/funasr_torch/sensevoice_bin.py b/runtime/python/libtorch/funasr_torch/sensevoice_bin.py
index d2e3cde..11cd2c9 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
@@ -32,50 +33,76 @@
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 __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) -> 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]
+
waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
waveform_nums = len(waveform_list)
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)
- )
+ 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),
+ )
# support batch_size=1 only currently
x = ctc_logits[0, : encoder_out_lens[0].item(), :]
yseq = x.argmax(dim=-1)
@@ -83,11 +110,9 @@
mask = yseq != self.blank_id
token_int = yseq[mask].tolist()
-
- if tokenizer is not None:
- asr_res.append(tokenizer.tokens2text(token_int))
- else:
- asr_res.append(token_int)
+
+ 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 +152,3 @@
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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