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kongdeqiang

5 天以前 28ccfbfc51068a663a80764e14074df5edf2b5ba

 funasr/auto/auto_model.py

@@ -14,13 +14,14 @@
import numpy as np
from tqdm import tqdm

from omegaconf import DictConfig, ListConfig
from funasr.utils.misc import deep_update
from funasr.register import tables
from funasr.utils.load_utils import load_bytes
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
@@ -92,7 +93,8 @@
                if isinstance(data_i, str) and os.path.exists(data_i):
                    key = misc.extract_filename_without_extension(data_i)
                else:
                    key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
                    if key is None:
                        key = "rand_key_" + "".join(random.choice(chars) for _ in range(13))
                key_list.append(key)

    else:  # raw text; audio sample point, fbank; bytes
@@ -110,11 +112,15 @@

    def __init__(self, **kwargs):

        try:
            from funasr.utils.version_checker import check_for_update

            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)

@@ -141,13 +147,16 @@
        # if spk_model is not None, build spk model else None
        spk_model = kwargs.get("spk_model", None)
        spk_kwargs = {} if kwargs.get("spk_kwargs", {}) is None else kwargs.get("spk_kwargs", {})
        cb_kwargs = (
            {} if spk_kwargs.get("cb_kwargs", {}) is None else spk_kwargs.get("cb_kwargs", {})
        )
        if spk_model is not None:
            logging.info("Building SPK model.")
            spk_kwargs["model"] = spk_model
            spk_kwargs["model_revision"] = kwargs.get("spk_model_revision", "master")
            spk_kwargs["device"] = kwargs["device"]
            spk_model, spk_kwargs = self.build_model(**spk_kwargs)
            self.cb_model = ClusterBackend().to(kwargs["device"])
            self.cb_model = ClusterBackend(**cb_kwargs).to(kwargs["device"])
            spk_mode = kwargs.get("spk_mode", "punc_segment")
            if spk_mode not in ["default", "vad_segment", "punc_segment"]:
                logging.error("spk_mode should be one of default, vad_segment and punc_segment.")
@@ -163,7 +172,8 @@
        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")))
@@ -172,7 +182,10 @@
        set_all_random_seed(kwargs.get("seed", 0))

        device = kwargs.get("device", "cuda")
        if not torch.cuda.is_available() or kwargs.get("ngpu", 1) == 0:
        if ((device =="cuda" and not torch.cuda.is_available())
            or (device == "xpu" and not torch.xpu.is_available())
            or (device == "mps" and not torch.backends.mps.is_available())
            or kwargs.get("ngpu", 1) == 0):
            device = "cpu"
            kwargs["batch_size"] = 1
        kwargs["device"] = device
@@ -181,21 +194,60 @@

        # build tokenizer
        tokenizer = kwargs.get("tokenizer", None)
        if tokenizer is not None:
            tokenizer_class = tables.tokenizer_classes.get(tokenizer)
            tokenizer = tokenizer_class(**kwargs.get("tokenizer_conf", {}))
            kwargs["token_list"] = (
                tokenizer.token_list if hasattr(tokenizer, "token_list") else None
            )
            kwargs["token_list"] = (
                tokenizer.get_vocab() if hasattr(tokenizer, "get_vocab") else kwargs["token_list"]
            )
            vocab_size = len(kwargs["token_list"]) if kwargs["token_list"] is not None else -1
            if vocab_size == -1 and hasattr(tokenizer, "get_vocab_size"):
                vocab_size = tokenizer.get_vocab_size()
        else:
            vocab_size = -1
        kwargs["tokenizer"] = tokenizer
        kwargs["vocab_size"] = -1

        if tokenizer is not None:
            tokenizers = (
                tokenizer.split(",") if isinstance(tokenizer, str) else tokenizer
            )  # type of tokenizers is list!!!
            tokenizers_conf = kwargs.get("tokenizer_conf", {})
            tokenizers_build = []
            vocab_sizes = []
            token_lists = []

            ### === only for kws ===
            token_list_files = kwargs.get("token_lists", [])
            seg_dicts = kwargs.get("seg_dicts", [])
            ### === only for kws ===

            if not isinstance(tokenizers_conf, (list, tuple, ListConfig)):
                tokenizers_conf = [tokenizers_conf] * len(tokenizers)

            for i, tokenizer in enumerate(tokenizers):
                tokenizer_class = tables.tokenizer_classes.get(tokenizer)
                tokenizer_conf = tokenizers_conf[i]

                ### === only for kws ===
                if len(token_list_files) > 1:
                    tokenizer_conf["token_list"] = token_list_files[i]
                if len(seg_dicts) > 1:
                    tokenizer_conf["seg_dict"] = seg_dicts[i]
                ### === only for kws ===

                tokenizer = tokenizer_class(**tokenizer_conf)
                tokenizers_build.append(tokenizer)
                token_list = tokenizer.token_list if hasattr(tokenizer, "token_list") else None
                token_list = (
                    tokenizer.get_vocab() if hasattr(tokenizer, "get_vocab") else token_list
                )
                vocab_size = -1
                if token_list is not None:
                    vocab_size = len(token_list)

                if vocab_size == -1 and hasattr(tokenizer, "get_vocab_size"):
                    vocab_size = tokenizer.get_vocab_size()
                token_lists.append(token_list)
                vocab_sizes.append(vocab_size)

            if len(tokenizers_build) <= 1:
                tokenizers_build = tokenizers_build[0]
                token_lists = token_lists[0]
                vocab_sizes = vocab_sizes[0]

            kwargs["tokenizer"] = tokenizers_build
            kwargs["vocab_size"] = vocab_sizes
            kwargs["token_list"] = token_lists

        # build frontend
        frontend = kwargs.get("frontend", None)
@@ -209,10 +261,11 @@
        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)
        model = model_class(**model_conf, vocab_size=vocab_size)
        model = model_class(**model_conf)

        # init_param
        init_param = kwargs.get("init_param", None)
@@ -236,6 +289,10 @@
        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):
@@ -244,15 +301,30 @@
        res = self.model(*args, kwargs)
        return res

    def generate(self, input, input_len=None, **cfg):
    def generate(self, input, input_len=None, progress_callback=None, **cfg):
        if self.vad_model is None:
            return self.inference(input, input_len=input_len, **cfg)
            return self.inference(
                input, input_len=input_len, progress_callback=progress_callback, **cfg
            )

        else:
            return self.inference_with_vad(input, input_len=input_len, **cfg)
            return self.inference_with_vad(
                input, input_len=input_len, progress_callback=progress_callback, **cfg
            )

    def inference(self, input, input_len=None, model=None, kwargs=None, key=None, **cfg):
    def inference(
        self,
        input,
        input_len=None,
        model=None,
        kwargs=None,
        key=None,
        progress_callback=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()
@@ -304,15 +376,24 @@
            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)
            if progress_callback:
                try:
                    progress_callback(end_idx, num_samples)
                except Exception as e:
                    logging.error(f"progress_callback error: {e}")
            time_speech_total += batch_data_time
            time_escape_total += time_escape

        if pbar:
            # pbar.update(1)
            pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
        torch.cuda.empty_cache()

        device = next(model.parameters()).device
        if device.type == "cuda":
            with torch.cuda.device(device):
                torch.cuda.empty_cache()
        return asr_result_list

    def inference_with_vad(self, input, input_len=None, **cfg):
@@ -326,9 +407,11 @@
        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
@@ -368,6 +451,9 @@

            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()
@@ -495,8 +581,8 @@
                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'\
@@ -506,8 +592,8 @@
                            {
                                "start": vadsegment[0],
                                "end": vadsegment[1],
                                "sentence": res["text"],
                                "timestamp": res["timestamp"],
                                "sentence": rest["text"],
                                "timestamp": rest["timestamp"],
                            }
                        )
                elif self.spk_mode == "punc_segment":