optimize paraformer_onnx

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']
 

funasr/runtime/python/onnxruntime/rapid_paraformer/paraformer_onnx.py

@@ -48,20 +48,29 @@ class Paraformer():
 
         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 @@ class Paraformer():
 
     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 @@ class Paraformer():
         texts = sentence_postprocess(token)
         text = texts[0]
         # text = self.tokenizer.tokens2text(token)
-        return text
-
-
-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 text, token
 
-        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