def convert_wav2vec2_checkpoint(
    checkpoint_path,
    pytorch_dump_folder_path,
    dict_path,
    encoder_config_path,
    decoder_config_path,
    vocab_size,
    num_decoder_layers,
):
    """
    Copy/paste/tweak model's weights to transformers design.
    """
    encoder_config = Wav2Vec2Config.from_pretrained(encoder_config_path)
    decoder_config = Speech2Text2Config.from_pretrained(
        decoder_config_path, vocab_size=vocab_size, decoder_layers=num_decoder_layers, do_stable_layer_norm=True
    )

    feature_extractor = Wav2Vec2FeatureExtractor(
        feature_size=1,
        sampling_rate=16000,
        padding_value=0,
        do_normalize=True,
        return_attention_mask=True,
    )

    model, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])}
    )
    model = model[0].eval()

    # set weights for wav2vec2 encoder
    hf_encoder = Wav2Vec2Model(encoder_config)
    projection_layer = recursively_load_weights_wav2vec2(model.encoder, hf_encoder)

    hf_decoder = Speech2Text2ForCausalLM(decoder_config)
    missing_keys, unexpected_keys = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(), strict=False)

    # set output linear layer
    unexpected_keys.remove("embed_out")
    hf_decoder.lm_head.weight = nn.Parameter(model.decoder.embed_out.detach())

    # layer norm is init to identity matrix so leaving it is fine
    logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}")
    logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}")

    hf_wav2vec = SpeechEncoderDecoderModel(encoder=hf_encoder, decoder=hf_decoder)
    hf_wav2vec.config.tie_word_embeddings = False

    # add projection layer
    hf_wav2vec.enc_to_dec_proj.weight = nn.Parameter(projection_layer.weight)
    hf_wav2vec.enc_to_dec_proj.bias = nn.Parameter(projection_layer.bias)

    vocab_dict = create_vocab_dict(dict_path)

    with open(os.path.join(pytorch_dump_folder_path, "vocab.json"), "w") as fp:
        json.dump(vocab_dict, fp)

    tokenizer = Speech2Text2Tokenizer(os.path.join(pytorch_dump_folder_path, "vocab.json"))
    tokenizer.save_pretrained(pytorch_dump_folder_path)

    config = hf_wav2vec.config.to_dict()
    config["pad_token_id"] = tokenizer.pad_token_id
    config["bos_token_id"] = tokenizer.bos_token_id
    config["eos_token_id"] = tokenizer.eos_token_id
    config["tokenizer_class"] = "speech_to_text_2"
    config["feature_extractor_type"] = "wav2vec2"

    hf_wav2vec.config = SpeechEncoderDecoderConfig.from_dict(config)

    hf_wav2vec.save_pretrained(pytorch_dump_folder_path)
    feature_extractor.save_pretrained(pytorch_dump_folder_path)

    Copy/paste/tweak model's weights to transformers design.
    
dpkd/transformers/src/transformers/models/speech_encoder_decoder/convert_speech_to_text_wav2vec2_seq2seq_original_to_pytorch.py