src/transformers/models/audio_spectrogram_transformer/convert_audio_spectrogram_transformer_original_to_pytorch.py/convert_audio_spectrogram_transformer_checkpoint
def convert_audio_spectrogram_transformer_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False):
    """
    Copy/paste/tweak model's weights to our Audio Spectrogram Transformer structure.
    """
    config = get_audio_spectrogram_transformer_config(model_name)

    model_name_to_url = {
        "ast-finetuned-audioset-10-10-0.4593": (
            "https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"
        ),
        "ast-finetuned-audioset-10-10-0.450": (
            "https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"
        ),
        "ast-finetuned-audioset-10-10-0.448": (
            "https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"
        ),
        "ast-finetuned-audioset-10-10-0.448-v2": (
            "https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"
        ),
        "ast-finetuned-audioset-12-12-0.447": (
            "https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"
        ),
        "ast-finetuned-audioset-14-14-0.443": (
            "https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"
        ),
        "ast-finetuned-audioset-16-16-0.442": (
            "https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"
        ),
        "ast-finetuned-speech-commands-v2": (
            "https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"
        ),
    }

    # load original state_dict
    checkpoint_url = model_name_to_url[model_name]
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
    # remove some keys
    remove_keys(state_dict)
    # rename some keys
    new_state_dict = convert_state_dict(state_dict, config)

    # load 🤗 model
    model = ASTForAudioClassification(config)
    model.eval()

    model.load_state_dict(new_state_dict)

    # verify outputs on dummy input
    # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
    mean = -4.2677393 if "speech-commands" not in model_name else -6.845978
    std = 4.5689974 if "speech-commands" not in model_name else 5.5654526
    max_length = 1024 if "speech-commands" not in model_name else 128
    feature_extractor = ASTFeatureExtractor(mean=mean, std=std, max_length=max_length)

    if "speech-commands" in model_name:
        dataset = load_dataset("speech_commands", "v0.02", split="validation")
        waveform = dataset[0]["audio"]["array"]
    else:
        filepath = hf_hub_download(
            repo_id="nielsr/audio-spectogram-transformer-checkpoint",
            filename="sample_audio.flac",
            repo_type="dataset",
        )

        waveform, _ = torchaudio.load(filepath)
        waveform = waveform.squeeze().numpy()

    inputs = feature_extractor(waveform, sampling_rate=16000, return_tensors="pt")

    # forward pass
    outputs = model(**inputs)
    logits = outputs.logits

    if model_name == "ast-finetuned-audioset-10-10-0.4593":
        expected_slice = torch.tensor([-0.8760, -7.0042, -8.6602])
    elif model_name == "ast-finetuned-audioset-10-10-0.450":
        expected_slice = torch.tensor([-1.1986, -7.0903, -8.2718])
    elif model_name == "ast-finetuned-audioset-10-10-0.448":
        expected_slice = torch.tensor([-2.6128, -8.0080, -9.4344])
    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
        expected_slice = torch.tensor([-1.5080, -7.4534, -8.8917])
    elif model_name == "ast-finetuned-audioset-12-12-0.447":
        expected_slice = torch.tensor([-0.5050, -6.5833, -8.0843])
    elif model_name == "ast-finetuned-audioset-14-14-0.443":
        expected_slice = torch.tensor([-0.3826, -7.0336, -8.2413])
    elif model_name == "ast-finetuned-audioset-16-16-0.442":
        expected_slice = torch.tensor([-1.2113, -6.9101, -8.3470])
    elif model_name == "ast-finetuned-speech-commands-v2":
        expected_slice = torch.tensor([6.1589, -8.0566, -8.7984])
    else:
        raise ValueError("Unknown model name")
    if not torch.allclose(logits[0, :3], expected_slice, atol=1e-4):
        raise ValueError("Logits don't match")
    print("Looks ok!")

    if pytorch_dump_folder_path is not None:
        Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
        print(f"Saving model {model_name} to {pytorch_dump_folder_path}")
        model.save_pretrained(pytorch_dump_folder_path)
        print(f"Saving feature extractor to {pytorch_dump_folder_path}")
        feature_extractor.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        print("Pushing model and feature extractor to the hub...")
        model.push_to_hub(f"MIT/{model_name}")
        feature_extractor.push_to_hub(f"MIT/{model_name}")