dcos

游雁

2024-07-30 d238a5ab4450aad636bbbc60d67335ca59b3bd9c

 funasr/auto/auto_model.py

@@ -20,7 +20,7 @@
from funasr.download.file import download_from_url
from funasr.utils.timestamp_tools import timestamp_sentence
from funasr.utils.timestamp_tools import timestamp_sentence_en
from funasr.download.download_from_hub import download_model
from funasr.download.download_model_from_hub import download_model
from funasr.utils.vad_utils import slice_padding_audio_samples
from funasr.utils.vad_utils import merge_vad
from funasr.utils.load_utils import load_audio_text_image_video
@@ -114,15 +114,15 @@
        try:
            from funasr.utils.version_checker import check_for_update

            check_for_update()
            print(
                "Check update of funasr, and it would cost few times. You may disable it by set `disable_update=True` in AutoModel"
            )
            check_for_update(disable=kwargs.get("disable_update", False))
        except:
            pass

        log_level = getattr(logging, kwargs.get("log_level", "INFO").upper())
        logging.basicConfig(level=log_level)

        if not kwargs.get("disable_log", True):
            tables.print()

        model, kwargs = self.build_model(**kwargs)

@@ -171,7 +171,8 @@
        self.spk_kwargs = spk_kwargs
        self.model_path = kwargs.get("model_path")

    def build_model(self, **kwargs):
    @staticmethod
    def build_model(**kwargs):
        assert "model" in kwargs
        if "model_conf" not in kwargs:
            logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
@@ -217,6 +218,7 @@
        kwargs["frontend"] = frontend
        # build model
        model_class = tables.model_classes.get(kwargs["model"])
        assert model_class is not None, f'{kwargs["model"]} is not registered'
        model_conf = {}
        deep_update(model_conf, kwargs.get("model_conf", {}))
        deep_update(model_conf, kwargs)
@@ -244,6 +246,10 @@
        elif kwargs.get("bf16", False):
            model.to(torch.bfloat16)
        model.to(device)

        if not kwargs.get("disable_log", True):
            tables.print()

        return model, kwargs

    def __call__(self, *args, **cfg):
@@ -261,6 +267,8 @@

    def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
        kwargs = self.kwargs if kwargs is None else kwargs
        if "cache" in kwargs:
            kwargs.pop("cache")
        deep_update(kwargs, cfg)
        model = self.model if model is None else model
        model.eval()
@@ -312,7 +320,7 @@
            speed_stats["rtf"] = f"{(time_escape) / batch_data_time:0.3f}"
            description = f"{speed_stats}, "
            if pbar:
                pbar.update(1)
                pbar.update(end_idx - beg_idx)
                pbar.set_description(description)
            time_speech_total += batch_data_time
            time_escape_total += time_escape
@@ -334,9 +342,11 @@
        end_vad = time.time()

        #  FIX(gcf): concat the vad clips for sense vocie model for better aed
        if kwargs.get("merge_vad", False):
        if cfg.get("merge_vad", False):
            for i in range(len(res)):
                res[i]["value"] = merge_vad(res[i]["value"], kwargs.get("merge_length", 15000))
                res[i]["value"] = merge_vad(
                    res[i]["value"], kwargs.get("merge_length_s", 15) * 1000
                )

        # step.2 compute asr model
        model = self.model
@@ -376,6 +386,9 @@

            if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
                batch_size = max(batch_size, sorted_data[0][0][1] - sorted_data[0][0][0])

            if kwargs["device"] == "cpu":
                batch_size = 0

            beg_idx = 0
            beg_asr_total = time.time()
@@ -503,8 +516,8 @@
                sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
                if self.spk_mode == "vad_segment":  # recover sentence_list
                    sentence_list = []
                    for res, vadsegment in zip(restored_data, vadsegments):
                        if "timestamp" not in res:
                    for rest, vadsegment in zip(restored_data, vadsegments):
                        if "timestamp" not in rest:
                            logging.error(
                                "Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
                                           and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
@@ -514,8 +527,8 @@
                            {
                                "start": vadsegment[0],
                                "end": vadsegment[1],
                                "sentence": res["text"],
                                "timestamp": res["timestamp"],
                                "sentence": rest["text"],
                                "timestamp": rest["timestamp"],
                            }
                        )
                elif self.spk_mode == "punc_segment":