From 4dc3a1b011e1e72eb737417b8e0e0bec7a7e3a6e Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期二, 21 三月 2023 15:12:21 +0800
Subject: [PATCH] resolve conflict
---
funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py | 28 +++++++++++++++++-----------
1 files changed, 17 insertions(+), 11 deletions(-)
diff --git a/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py b/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py
index 3545ccf..3c0606d 100644
--- a/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py
+++ b/funasr/runtime/python/libtorch/torch_paraformer/paraformer_bin.py
@@ -24,12 +24,16 @@
device_id: Union[str, int] = "-1",
plot_timestamp_to: str = "",
pred_bias: int = 1,
+ quantize: bool = False,
+ intra_op_num_threads: int = 1,
):
if not Path(model_dir).exists():
raise FileNotFoundError(f'{model_dir} does not exist.')
model_file = os.path.join(model_dir, 'model.torchscripts')
+ if quantize:
+ model_file = os.path.join(model_dir, 'model_quant.torchscripts')
config_file = os.path.join(model_dir, 'config.yaml')
cmvn_file = os.path.join(model_dir, 'am.mvn')
config = read_yaml(config_file)
@@ -58,26 +62,28 @@
am_scores, valid_token_lens = outputs[0], outputs[1]
if len(outputs) == 4:
# for BiCifParaformer Inference
- us_alphas, us_cif_peak = outputs[2], outputs[3]
+ us_alphas, us_peaks = outputs[2], outputs[3]
else:
- us_alphas, us_cif_peak = None, None
+ us_alphas, us_peaks = None, None
except:
#logging.warning(traceback.format_exc())
logging.warning("input wav is silence or noise")
preds = ['']
else:
- am_scores, valid_token_lens = am_scores.detach().cpu().numpy(), valid_token_lens.detach().cpu().numpy()
preds = self.decode(am_scores, valid_token_lens)
- if us_cif_peak is None:
+ if us_peaks is None:
for pred in preds:
+ pred = sentence_postprocess(pred)
asr_res.append({'preds': pred})
else:
- for pred, us_cif_peak_ in zip(preds, us_cif_peak):
- text, tokens = pred
- timestamp, timestamp_total = time_stamp_lfr6_onnx(us_cif_peak_, copy.copy(tokens))
+ for pred, us_peaks_ in zip(preds, us_peaks):
+ raw_tokens = pred
+ timestamp, timestamp_raw = time_stamp_lfr6_onnx(us_peaks_, copy.copy(raw_tokens))
+ text_proc, timestamp_proc, _ = sentence_postprocess(raw_tokens, timestamp_raw)
+ # logging.warning(timestamp)
if len(self.plot_timestamp_to):
- self.plot_wave_timestamp(waveform_list[0], timestamp_total, self.plot_timestamp_to)
- asr_res.append({'preds': text, 'timestamp': timestamp})
+ self.plot_wave_timestamp(waveform_list[0], timestamp, self.plot_timestamp_to)
+ asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens})
return asr_res
def plot_wave_timestamp(self, wav, text_timestamp, dest):
@@ -178,6 +184,6 @@
# Change integer-ids to tokens
token = self.converter.ids2tokens(token_int)
token = token[:valid_token_num-self.pred_bias]
- texts = sentence_postprocess(token)
- return texts
+ # texts = sentence_postprocess(token)
+ return token
--
Gitblit v1.9.1