From ec3ccbea9ff1d869becaa2b13255d0da1e4bf3ca Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 02 三月 2023 20:23:39 +0800
Subject: [PATCH] torchscripts
---
funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py | 47 ++++++++++++++++++++++++++++-------------------
1 files changed, 28 insertions(+), 19 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
index e0c622c..8af3474 100644
--- a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
+++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
@@ -2,28 +2,29 @@
# @Author: SWHL
# @Contact: liekkaskono@163.com
import os.path
-import traceback
from pathlib import Path
from typing import List, Union, Tuple
+import copy
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.frontend import WavFrontend
+from .utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError,
+ OrtInferSession, TokenIDConverter, get_logger,
+ read_yaml)
+from .utils.postprocess_utils import sentence_postprocess
+from .utils.frontend import WavFrontend
+from funasr.utils.timestamp_tools import time_stamp_lfr6_pl
logging = get_logger()
class Paraformer():
- def __init__(self, model_dir: Union[str, Path]=None,
+ def __init__(self, model_dir: Union[str, Path] = None,
batch_size: int = 1,
- device_id: Union[str, int]="-1",
+ device_id: Union[str, int] = "-1",
):
-
+
if not Path(model_dir).exists():
raise FileNotFoundError(f'{model_dir} does not exist.')
@@ -47,20 +48,29 @@
asr_res = []
for beg_idx in range(0, waveform_nums, self.batch_size):
+ res = {}
end_idx = min(waveform_nums, beg_idx + self.batch_size)
-
feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
try:
- am_scores, valid_token_lens = self.infer(feats, feats_len)
+ outputs = self.infer(feats, feats_len)
+ 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]
+ else:
+ us_alphas, us_cif_peak = None, None
except ONNXRuntimeError:
#logging.warning(traceback.format_exc())
logging.warning("input wav is silence or noise")
preds = ['']
else:
- preds = self.decode(am_scores, valid_token_lens)
-
- asr_res.extend(preds)
+ preds, raw_token = self.decode(am_scores, valid_token_lens)[0]
+ res['preds'] = preds
+ if us_cif_peak is not None:
+ timestamp = time_stamp_lfr6_pl(us_alphas, us_cif_peak, copy.copy(raw_token), log=False)
+ res['timestamp'] = timestamp
+ asr_res.append(res)
return asr_res
def load_data(self,
@@ -107,8 +117,8 @@
def infer(self, feats: np.ndarray,
feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
- am_scores, token_nums = self.ort_infer([feats, feats_len])
- return am_scores, token_nums
+ outputs = self.ort_infer([feats, feats_len])
+ return outputs
def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]:
return [self.decode_one(am_score, token_num)
@@ -135,10 +145,9 @@
# Change integer-ids to tokens
token = self.converter.ids2tokens(token_int)
- token = token[:valid_token_num-1]
+ # token = token[:valid_token_num-1]
texts = sentence_postprocess(token)
text = texts[0]
# text = self.tokenizer.tokens2text(token)
- return text
-
+ return text, token
--
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