import os
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from omegaconf import OmegaConf
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import torch
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from funasr.utils.name_maps_from_hub import name_maps_ms, name_maps_hf
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def download_model(**kwargs):
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model_hub = kwargs.get("model_hub", "ms")
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if model_hub == "ms":
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kwargs = download_fr_ms(**kwargs)
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return kwargs
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def download_fr_ms(**kwargs):
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model_or_path = kwargs.get("model_pretrain")
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model_revision = kwargs.get("model_pretrain_revision")
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if not os.path.exists(model_or_path):
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model_or_path = get_or_download_model_dir(model_or_path, model_revision, third_party="funasr")
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config = os.path.join(model_or_path, "config.yaml")
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assert os.path.exists(config), "{} is not exist!".format(config)
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cfg = OmegaConf.load(config)
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kwargs = OmegaConf.merge(cfg, kwargs)
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init_param = os.path.join(model_or_path, "model.pb")
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kwargs["init_param"] = init_param
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kwargs["token_list"] = os.path.join(model_or_path, "tokens.txt")
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return kwargs
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def get_or_download_model_dir(
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model,
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model_revision=None,
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third_party=None):
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""" Get local model directory or download model if necessary.
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|
Args:
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model (str): model id or path to local model directory.
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model_revision (str, optional): model version number.
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third_party (str, optional): in which third party library
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this function is called.
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"""
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from modelscope.hub.check_model import check_local_model_is_latest
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from modelscope.hub.snapshot_download import snapshot_download
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from modelscope.utils.constant import Invoke, ThirdParty
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if os.path.exists(model):
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model_cache_dir = model if os.path.isdir(
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model) else os.path.dirname(model)
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check_local_model_is_latest(
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model_cache_dir,
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user_agent={
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Invoke.KEY: Invoke.LOCAL_TRAINER,
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ThirdParty.KEY: third_party
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})
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else:
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model_cache_dir = snapshot_download(
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model,
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revision=model_revision,
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user_agent={
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Invoke.KEY: Invoke.TRAINER,
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ThirdParty.KEY: third_party
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})
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return model_cache_dir
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