v0.8.0

游雁

2023-10-10 580b11b57ac4b62f7e2acda73813a4e10e8e4cd3

 funasr/bin/asr_inference_launch.py

@@ -55,6 +55,7 @@
                                        distribute_spk)
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.utils.cluster_backend import ClusterBackend
from funasr.utils.modelscope_utils import get_cache_dir
from tqdm import tqdm

def inference_asr(
@@ -498,6 +499,7 @@
):
    ncpu = kwargs.get("ncpu", 1)
    torch.set_num_threads(ncpu)
    language = kwargs.get("model_lang", None)

    if word_lm_train_config is not None:
        raise NotImplementedError("Word LM is not implemented")
@@ -704,10 +706,13 @@
            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 and len(time_stamp):
                postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
            if language == "en-bpe":
                postprocessed_result = postprocess_utils.sentence_postprocess_sentencepiece(token)
            else:
                postprocessed_result = postprocess_utils.sentence_postprocess(token)
                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)
            text_postprocessed = ""
            time_stamp_postprocessed = ""
            text_postprocessed_punc = postprocessed_result
@@ -787,7 +792,7 @@
        time_stamp_writer: bool = True,
        punc_infer_config: Optional[str] = None,
        punc_model_file: Optional[str] = None,
        sv_model_file: Optional[str] = None,
        sv_model_file: Optional[str] = None, 
        streaming: bool = False,
        embedding_node: str = "resnet1_dense",
        sv_threshold: float = 0.9465,
@@ -808,6 +813,9 @@
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    if sv_model_file is None:
        sv_model_file = "{}/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/campplus_cn_common.bin".format(get_cache_dir(None))

    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
@@ -1084,7 +1092,6 @@
            logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
        torch.cuda.empty_cache()
        distribute_spk(asr_result_list[0]['sentences'], sv_output)
        import pdb; pdb.set_trace()
        return asr_result_list

    return _forward
@@ -2030,7 +2037,7 @@
        return inference_paraformer(**kwargs)
    elif mode == "paraformer_streaming":
        return inference_paraformer_online(**kwargs)
    elif mode == "paraformer_vad_speaker":
    elif mode.startswith("paraformer_vad_speaker"):
        return inference_paraformer_vad_speaker(**kwargs)
    elif mode.startswith("paraformer_vad"):
        return inference_paraformer_vad_punc(**kwargs)