update timestamp_onnx

shixian.shi

2023-03-02 a757387b729bee28cfccac55b5814fd17a3e64b3

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 @@
        )
        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 @@
                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 @@
    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 @@
            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