update timestamp_onnx

funasr/runtime/python/onnxruntime/demo.py

@@ -2,7 +2,7 @@
 from rapid_paraformer import Paraformer
 
 model_dir = "/Users/shixian/code/funasr2/export/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
-model_dir = "/Users/shixian/code/funasr2/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
+# model_dir = "/Users/shixian/code/funasr2/export/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
 
 model = Paraformer(model_dir, batch_size=1)
 

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

@@ -41,17 +41,16 @@ class Paraformer():
         )
         self.ort_infer = OrtInferSession(model_file, device_id)
         self.batch_size = batch_size
+        self.plot = True
 
     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])
-
             try:
                 outputs = self.infer(feats, feats_len)
                 am_scores, valid_token_lens = outputs[0], outputs[1]
@@ -68,11 +67,17 @@ class Paraformer():
                 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_onnx(us_cif_peak, copy.copy(raw_token))
+                    timestamp, timestamp_total = time_stamp_lfr6_onnx(us_cif_peak, copy.copy(raw_token))
                     res['timestamp'] = timestamp
+                    if self.plot:
+                        self.plot_wave_timestamp(waveform_list[0], timestamp_total)
             asr_res.append(res)
         return asr_res
 
+    def plot_wave_timestamp(self, wav, text_timestamp):
+        # TODO: Plot the wav and timestamp results with matplotlib
+        import pdb; pdb.set_trace()
+
     def load_data(self,
                   wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
         def load_wav(path: str) -> np.ndarray:

funasr/runtime/python/onnxruntime/rapid_paraformer/utils/timestamp_utils.py

@@ -9,7 +9,6 @@ def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0):
     TIME_RATE = 10.0 * 6 / 1000 / 3  #  3 times upsampled
     cif_peak = us_cif_peak.reshape(-1)
     num_frames = cif_peak.shape[-1]
-    import pdb; pdb.set_trace()
     if char_list[-1] == '</s>':
         char_list = char_list[:-1]
     # char_list = [i for i in text]
@@ -49,11 +48,11 @@ def time_stamp_lfr6_onnx(us_cif_peak, char_list, begin_time=0.0):
             timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
             timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
     assert len(new_char_list) == len(timestamp_list)
-    res_txt = ""
+    res_total = []
     for char, timestamp in zip(new_char_list, timestamp_list):
-        res_txt += "{} {} {};".format(char, timestamp[0], timestamp[1])
+        res_total.append([char, timestamp[0], timestamp[1]])  # += "{} {} {};".format(char, timestamp[0], timestamp[1])
     res = []
     for char, timestamp in zip(new_char_list, timestamp_list):
         if char != '<sil>':
             res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
-    return res
+    return res, res_total