funasr2 paraformer biciparaformer contextuaparaformer

游雁

2023-12-13 806a03609df033d61f824f1ab8527eb88fe837ad

 funasr/bin/asr_inference_launch.py

@@ -1254,37 +1254,6 @@

        return cache

    #def _prepare_cache(cache: dict = {}, chunk_size=[5, 10, 5], batch_size=1):
    #    if len(cache) > 0:
    #        return cache
    #    config = _read_yaml(asr_train_config)
    #    enc_output_size = config["encoder_conf"]["output_size"]
    #    feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
    #    cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
    #                "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
    #                "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)), "tail_chunk": False}
    #    cache["encoder"] = cache_en

    #    cache_de = {"decode_fsmn": None}
    #    cache["decoder"] = cache_de

    #    return cache

    #def _cache_reset(cache: dict = {}, chunk_size=[5, 10, 5], batch_size=1):
    #    if len(cache) > 0:
    #        config = _read_yaml(asr_train_config)
    #        enc_output_size = config["encoder_conf"]["output_size"]
    #        feats_dims = config["frontend_conf"]["n_mels"] * config["frontend_conf"]["lfr_m"]
    #        cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, enc_output_size)),
    #                    "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size, "last_chunk": False,
    #                    "feats": torch.zeros((batch_size, chunk_size[0] + chunk_size[2], feats_dims)),
    #                    "tail_chunk": False}
    #        cache["encoder"] = cache_en

    #        cache_de = {"decode_fsmn": None}
    #        cache["decoder"] = cache_de

    #    return cache

    def _forward(
            data_path_and_name_and_type,