add paraformer online opt infer code

haoneng.lhn

2023-09-14 f41a1276ff18cb9fd3d837dcda84a7650637d579

 funasr/bin/asr_inference_launch.py

@@ -415,7 +415,7 @@
                        ibest_writer["rtf"][key] = rtf_cur

                    if text is not None:
                        if use_timestamp and timestamp is not None:
                        if use_timestamp and timestamp is not None and len(timestamp):
                            postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp)
                        else:
                            postprocessed_result = postprocess_utils.sentence_postprocess(token)
@@ -692,7 +692,7 @@
            text, token, token_int = result[0], result[1], result[2]
            time_stamp = result[4] if len(result[4]) > 0 else None

            if use_timestamp and time_stamp is not None:
            if use_timestamp and time_stamp is not None and len(time_stamp):
                postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
            else:
                postprocessed_result = postprocess_utils.sentence_postprocess(token)
@@ -840,37 +840,72 @@
            data = yaml.load(f, Loader=yaml.Loader)
        return data

    def _prepare_cache(cache: dict = {}, chunk_size=[5, 10, 5], batch_size=1):
    def _prepare_cache(cache: dict = {}, chunk_size=[5, 10, 5], encoder_chunk_look_back=0,
                       decoder_chunk_look_back=0, 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,
                    "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size,
                    "encoder_chunk_look_back": encoder_chunk_look_back, "last_chunk": False, "opt": None,
                    "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_de = {"decode_fsmn": None, "decoder_chunk_look_back": decoder_chunk_look_back, "opt": None}
        cache["decoder"] = cache_de

        return cache

    def _cache_reset(cache: dict = {}, chunk_size=[5, 10, 5], batch_size=1):
    def _cache_reset(cache: dict = {}, chunk_size=[5, 10, 5], encoder_chunk_look_back=0,
                     decoder_chunk_look_back=0, 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}
                        "cif_alphas": torch.zeros((batch_size, 1)), "chunk_size": chunk_size,
                        "encoder_chunk_look_back": encoder_chunk_look_back, "last_chunk": False, "opt": None,
                        "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_de = {"decode_fsmn": None, "decoder_chunk_look_back": decoder_chunk_look_back, "opt": None}
            cache["decoder"] = cache_de

        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,
@@ -899,24 +934,34 @@
        is_final = False
        cache = {}
        chunk_size = [5, 10, 5]
        encoder_chunk_look_back = 0
        decoder_chunk_look_back = 0
        if param_dict is not None and "cache" in param_dict:
            cache = param_dict["cache"]
        if param_dict is not None and "is_final" in param_dict:
            is_final = param_dict["is_final"]
        if param_dict is not None and "chunk_size" in param_dict:
            chunk_size = param_dict["chunk_size"]
        if param_dict is not None and "encoder_chunk_look_back" in param_dict:
            encoder_chunk_look_back = param_dict["encoder_chunk_look_back"]
            if encoder_chunk_look_back > 0:
                chunk_size[0] = 0
        if param_dict is not None and "decoder_chunk_look_back" in param_dict:
            decoder_chunk_look_back = param_dict["decoder_chunk_look_back"]

        # 7 .Start for-loop
        # FIXME(kamo): The output format should be discussed about
        raw_inputs = torch.unsqueeze(raw_inputs, axis=0)
        asr_result_list = []
        cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
        cache = _prepare_cache(cache, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, 
                               decoder_chunk_look_back=decoder_chunk_look_back, batch_size=1)
        item = {}
        if data_path_and_name_and_type is not None and data_path_and_name_and_type[2] == "sound":
            sample_offset = 0
            speech_length = raw_inputs.shape[1]
            stride_size = chunk_size[1] * 960
            cache = _prepare_cache(cache, chunk_size=chunk_size, batch_size=1)
            cache = _prepare_cache(cache, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, 
                                   decoder_chunk_look_back=decoder_chunk_look_back, batch_size=1)
            final_result = ""
            for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
                if sample_offset + stride_size >= speech_length - 1:
@@ -937,7 +982,8 @@

        asr_result_list.append(item)
        if is_final:
            cache = _cache_reset(cache, chunk_size=chunk_size, batch_size=1)
            cache = _cache_reset(cache, chunk_size=chunk_size, batch_size=1,
                                 encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
        return asr_result_list

    return _forward
@@ -1791,12 +1837,6 @@
        type=int,
        default=1,
        help="The batch size for inference",
    )
    group.add_argument(
        "--decoding_ind",
        type=int,
        default=0,
        help="chunk select for chunk encoder",
    )
    group.add_argument("--nbest", type=int, default=5, help="Output N-best hypotheses")
    group.add_argument("--beam_size", type=int, default=20, help="Beam size")