Fsmn_vad支持多线程并发调用 (#2519)

王梦迪

2025-05-20 fe588bc508c0076bb007d6ed36c18ac8ecb341ac

Fsmn_vad支持多线程并发调用 (#2519)

* 修复WavFrontend.fbank多线程调用下共享fbank_fn导致的bug

* Fsmn_vad支持多线程并发调用

---------

Co-authored-by: wangmengdi06 <wangmengdi06@58.com>

 runtime/python/onnxruntime/funasr_onnx/utils/frontend.py

@@ -52,12 +52,12 @@

    def fbank(self, waveform: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        waveform = waveform * (1 << 15)
        self.fbank_fn = knf.OnlineFbank(self.opts)
        self.fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
        frames = self.fbank_fn.num_frames_ready
        fbank_fn = knf.OnlineFbank(self.opts)
        fbank_fn.accept_waveform(self.opts.frame_opts.samp_freq, waveform.tolist())
        frames = fbank_fn.num_frames_ready
        mat = np.empty([frames, self.opts.mel_opts.num_bins])
        for i in range(frames):
            mat[i, :] = self.fbank_fn.get_frame(i)
            mat[i, :] = fbank_fn.get_frame(i)
        feat = mat.astype(np.float32)
        feat_len = np.array(mat.shape[0]).astype(np.int32)
        return feat, feat_len

 runtime/python/onnxruntime/funasr_onnx/vad_bin.py

@@ -69,7 +69,7 @@
            model_file, device_id, intra_op_num_threads=intra_op_num_threads
        )
        self.batch_size = batch_size
        self.vad_scorer = E2EVadModel(config["model_conf"])
        self.vad_scorer_config = config["model_conf"]
        self.max_end_sil = (
            max_end_sil if max_end_sil is not None else config["model_conf"]["max_end_silence_time"]
        )
@@ -90,10 +90,9 @@
        waveform_list = self.load_data(audio_in, self.frontend.opts.frame_opts.samp_freq)
        waveform_nums = len(waveform_list)
        is_final = kwargs.get("kwargs", False)

        segments = [[]] * self.batch_size
        for beg_idx in range(0, waveform_nums, self.batch_size):

            vad_scorer = E2EVadModel(self.vad_scorer_config)
            end_idx = min(waveform_nums, beg_idx + self.batch_size)
            waveform = waveform_list[beg_idx:end_idx]
            feats, feats_len = self.extract_feat(waveform)
@@ -122,7 +121,7 @@
                    inputs.extend(in_cache)
                    scores, out_caches = self.infer(inputs)
                    in_cache = out_caches
                    segments_part = self.vad_scorer(
                    segments_part = vad_scorer(
                        scores,
                        waveform_package,
                        is_final=is_final,