From aef5511794d81cdeaf138908e23296b41a6a3947 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 02 三月 2023 17:51:19 +0800
Subject: [PATCH] Merge pull request #174 from alibaba-damo-academy/dev_timestamp
---
funasr/runtime/python/onnxruntime/demo.py | 4 -
funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py | 83 ++++++++---------------------------------
2 files changed, 18 insertions(+), 69 deletions(-)
diff --git a/funasr/runtime/python/onnxruntime/demo.py b/funasr/runtime/python/onnxruntime/demo.py
index b4a03f3..517c7ef 100644
--- a/funasr/runtime/python/onnxruntime/demo.py
+++ b/funasr/runtime/python/onnxruntime/demo.py
@@ -1,10 +1,8 @@
from rapid_paraformer import Paraformer
-from rapid_paraformer import BiCifParaformer
model_dir = "/Users/shixian/code/funasr2/export/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-# model = Paraformer(model_dir, batch_size=1)
-model = BiCifParaformer(model_dir, batch_size=1)
+model = Paraformer(model_dir, batch_size=1)
wav_path = ['/Users/shixian/code/funasr2/export/damo/speech_paraformer-tiny-commandword_asr_nat-zh-cn-16k-vocab544-pytorch/example/asr_example.wav']
diff --git a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
index d77bcf7..8af3474 100644
--- a/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
+++ b/funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py
@@ -48,20 +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,
@@ -108,8 +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)
@@ -140,63 +149,5 @@
texts = sentence_postprocess(token)
text = texts[0]
# text = self.tokenizer.tokens2text(token)
- return text
+ return text, token
-
-class BiCifParaformer(Paraformer):
- def infer(self, feats: np.ndarray,
- feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
- am_scores, token_nums, us_alphas, us_cif_peak = self.ort_infer([feats, feats_len])
- return am_scores, token_nums, us_alphas, us_cif_peak
- def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List:
- 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):
- res = {}
- end_idx = min(waveform_nums, beg_idx + self.batch_size)
- feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
- am_scores, valid_token_lens, us_alphas, us_cif_peak = self.infer(feats, feats_len)
-
- try:
- am_scores, valid_token_lens, us_alphas, us_cif_peak = self.infer(feats, feats_len)
- except ONNXRuntimeError:
- #logging.warning(traceback.format_exc())
- logging.warning("input wav is silence or noise")
- preds = ['']
- else:
- token = self.decode(am_scores, valid_token_lens)
- timestamp = time_stamp_lfr6_pl(us_alphas, us_cif_peak, copy.copy(token[0]), log=False)
- texts = sentence_postprocess(token[0], timestamp)
- # texts = sentence_postprocess(token[0])
- text = texts[0]
- res['text'] = text
- res['timestamp'] = timestamp
- asr_res.append(res)
-
- return asr_res
-
- def decode_one(self,
- am_score: np.ndarray,
- valid_token_num: int) -> List[str]:
- yseq = am_score.argmax(axis=-1)
- score = am_score.max(axis=-1)
- score = np.sum(score, axis=-1)
-
- # pad with mask tokens to ensure compatibility with sos/eos tokens
- # asr_model.sos:1 asr_model.eos:2
- yseq = np.array([1] + yseq.tolist() + [2])
- hyp = Hypothesis(yseq=yseq, score=score)
-
- # remove sos/eos and get results
- last_pos = -1
- token_int = hyp.yseq[1:last_pos].tolist()
-
- # remove blank symbol id, which is assumed to be 0
- token_int = list(filter(lambda x: x not in (0, 2), token_int))
-
- # Change integer-ids to tokens
- token = self.converter.ids2tokens(token_int)
- # token = token[:valid_token_num-1]
- return token
\ No newline at end of file
--
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