hf hub

游雁

2024-05-15 fb45c9a6ef4c5f94d8b36abafca072f62aff9b5f

hf hub

 funasr/download/download_from_hub.py

@@ -10,7 +10,7 @@
    if hub == "ms":
        kwargs = download_from_ms(**kwargs)
    elif hub == "hf":
        pass
        kwargs = download_from_hf(**kwargs)
    elif hub == "openai":
        model_or_path = kwargs.get("model")
        if os.path.exists(model_or_path):
@@ -34,6 +34,67 @@
    if not os.path.exists(model_or_path) and "model_path" not in kwargs:
        try:
            model_or_path = get_or_download_model_dir(
                model_or_path,
                model_revision,
                is_training=kwargs.get("is_training"),
                check_latest=kwargs.get("check_latest", True),
            )
        except Exception as e:
            print(f"Download: {model_or_path} failed!: {e}")

    kwargs["model_path"] = model_or_path if "model_path" not in kwargs else kwargs["model_path"]

    if os.path.exists(os.path.join(model_or_path, "configuration.json")):
        with open(os.path.join(model_or_path, "configuration.json"), "r", encoding="utf-8") as f:
            conf_json = json.load(f)

            cfg = {}
            if "file_path_metas" in conf_json:
                add_file_root_path(model_or_path, conf_json["file_path_metas"], cfg)
            cfg.update(kwargs)
            if "config" in cfg:
                config = OmegaConf.load(cfg["config"])
                kwargs = OmegaConf.merge(config, cfg)
                kwargs["model"] = config["model"]
    elif os.path.exists(os.path.join(model_or_path, "config.yaml")) and os.path.exists(
        os.path.join(model_or_path, "model.pt")
    ):
        config = OmegaConf.load(os.path.join(model_or_path, "config.yaml"))
        kwargs = OmegaConf.merge(config, kwargs)
        init_param = os.path.join(model_or_path, "model.pb")
        kwargs["init_param"] = init_param
        if os.path.exists(os.path.join(model_or_path, "tokens.txt")):
            kwargs["tokenizer_conf"]["token_list"] = os.path.join(model_or_path, "tokens.txt")
        if os.path.exists(os.path.join(model_or_path, "tokens.json")):
            kwargs["tokenizer_conf"]["token_list"] = os.path.join(model_or_path, "tokens.json")
        if os.path.exists(os.path.join(model_or_path, "seg_dict")):
            kwargs["tokenizer_conf"]["seg_dict"] = os.path.join(model_or_path, "seg_dict")
        if os.path.exists(os.path.join(model_or_path, "bpe.model")):
            kwargs["tokenizer_conf"]["bpemodel"] = os.path.join(model_or_path, "bpe.model")
        kwargs["model"] = config["model"]
        if os.path.exists(os.path.join(model_or_path, "am.mvn")):
            kwargs["frontend_conf"]["cmvn_file"] = os.path.join(model_or_path, "am.mvn")
        if os.path.exists(os.path.join(model_or_path, "jieba_usr_dict")):
            kwargs["jieba_usr_dict"] = os.path.join(model_or_path, "jieba_usr_dict")
    if isinstance(kwargs, DictConfig):
        kwargs = OmegaConf.to_container(kwargs, resolve=True)
    if os.path.exists(os.path.join(model_or_path, "requirements.txt")):
        requirements = os.path.join(model_or_path, "requirements.txt")
        print(f"Detect model requirements, begin to install it: {requirements}")
        from funasr.utils.install_model_requirements import install_requirements

        install_requirements(requirements)
    return kwargs


def download_from_hf(**kwargs):
    model_or_path = kwargs.get("model")
    if model_or_path in name_maps_hf:
        model_or_path = name_maps_hf[model_or_path]
    model_revision = kwargs.get("model_revision", "master")
    if not os.path.exists(model_or_path) and "model_path" not in kwargs:
        try:
            model_or_path = get_or_download_model_dir_hf(
                model_or_path,
                model_revision,
                is_training=kwargs.get("is_training"),
@@ -136,3 +197,22 @@
            model, revision=model_revision, user_agent={Invoke.KEY: key, ThirdParty.KEY: "funasr"}
        )
    return model_cache_dir


def get_or_download_model_dir_hf(
    model,
    model_revision=None,
    is_training=False,
    check_latest=True,
):
    """Get local model directory or download model if necessary.

    Args:
        model (str): model id or path to local model directory.
        model_revision  (str, optional): model version number.
        :param is_training:
    """
    from huggingface_hub import snapshot_download

    model_cache_dir = snapshot_download(model)
    return model_cache_dir

 funasr/download/name_maps_from_hub.py

@@ -14,7 +14,9 @@
    "Qwen-Audio": "Qwen/Qwen-Audio",
}

name_maps_hf = {}
name_maps_hf = {
    "": "",
}

name_maps_openai = {
    "Whisper-tiny.en": "tiny.en",