python/FunASR-XL.git

			@@ -482,13 +482,17 @@

			return frame_state

			def forward(self, feats: torch.Tensor, waveform: torch.tensor, cache: dict = {},
			is_final: bool = False
			def forward(self, feats: torch.Tensor,
			waveform: torch.tensor,
			cache: dict = {},
			is_final: bool = False,
			**kwargs,
			):
			# if len(cache) == 0:
			# self.AllResetDetection()
			# self.waveform = waveform # compute decibel for each frame
			cache["stats"].waveform = waveform
			is_streaming_input = kwargs.get("is_streaming_input", True)
			self.ComputeDecibel(cache=cache)
			self.ComputeScores(feats, cache=cache)
			if not is_final:
			@@ -500,13 +504,32 @@
			segment_batch = []
			if len(cache["stats"].output_data_buf) > 0:
			for i in range(cache["stats"].output_data_buf_offset, len(cache["stats"].output_data_buf)):
			if not is_final and (not cache["stats"].output_data_buf[i].contain_seg_start_point or not
			cache["stats"].output_data_buf[
			i].contain_seg_end_point):
			continue
			segment = [cache["stats"].output_data_buf[i].start_ms, cache["stats"].output_data_buf[i].end_ms]
			if is_streaming_input: # in this case, return [beg, -1], [], [-1, end], [beg, end]
			if not cache["stats"].output_data_buf[i].contain_seg_start_point:
			continue
			if not cache["stats"].next_seg and not cache["stats"].output_data_buf[i].contain_seg_end_point:
			continue
			start_ms = cache["stats"].output_data_buf[i].start_ms if cache["stats"].next_seg else -1
			if cache["stats"].output_data_buf[i].contain_seg_end_point:
			end_ms = cache["stats"].output_data_buf[i].end_ms
			cache["stats"].next_seg = True
			cache["stats"].output_data_buf_offset += 1
			else:
			end_ms = -1
			cache["stats"].next_seg = False
			segment = [start_ms, end_ms]

			else: # in this case, return [beg, end]

			if not is_final and (not cache["stats"].output_data_buf[i].contain_seg_start_point or not
			cache["stats"].output_data_buf[
			i].contain_seg_end_point):
			continue
			segment = [cache["stats"].output_data_buf[i].start_ms, cache["stats"].output_data_buf[i].end_ms]
			cache["stats"].output_data_buf_offset += 1 # need update this parameter

			segment_batch.append(segment)
			cache["stats"].output_data_buf_offset += 1 # need update this parameter

			if segment_batch:
			segments.append(segment_batch)
			# if is_final:
			@@ -551,7 +574,9 @@
			chunk_stride_samples = int(chunk_size * frontend.fs / 1000)

			time1 = time.perf_counter()
			cfg = {"is_final": kwargs.get("is_final", False)}
			is_streaming_input = kwargs.get("is_streaming_input", False) if chunk_size >= 15000 else kwargs.get("is_streaming_input", True)
			is_final = kwargs.get("is_final", False) if is_streaming_input else kwargs.get("is_final", True)
			cfg = {"is_final": is_final, "is_streaming_input": is_streaming_input}
			audio_sample_list = load_audio_text_image_video(data_in,
			fs=frontend.fs,
			audio_fs=kwargs.get("fs", 16000),
			@@ -560,7 +585,7 @@
			cache=cfg,
			)
			_is_final = cfg["is_final"] # if data_in is a file or url, set is_final=True

			is_streaming_input = cfg["is_streaming_input"]
			time2 = time.perf_counter()
			meta_data["load_data"] = f"{time2 - time1:0.3f}"
			assert len(audio_sample_list) == 1, "batch_size must be set 1"
			@@ -588,7 +613,8 @@
			"feats": speech,
			"waveform": cache["frontend"]["waveforms"],
			"is_final": kwargs["is_final"],
			"cache": cache
			"cache": cache,
			"is_streaming_input": is_streaming_input
			}
			segments_i = self.forward(**batch)
			if len(segments_i) > 0: