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游雁

2023-04-27 30aa982bf29ceefaf52c0013c12c19adc57dea0e

 funasr/bin/asr_inference_paraformer_streaming.py

@@ -8,6 +8,7 @@
import codecs
import tempfile
import requests
import yaml
from pathlib import Path
from typing import Optional
from typing import Sequence
@@ -202,10 +203,12 @@
        assert check_argument_types()
        results = []
        cache_en = cache["encoder"]
        if speech.shape[1] < 16 * 60 and cache["is_final"]:
            cache["last_chunk"] = True
            feats = cache["feats"]
        if speech.shape[1] < 16 * 60 and cache_en["is_final"]:
            cache_en["tail_chunk"] = True
            feats = cache_en["feats"]
            feats_len = torch.tensor([feats.shape[1]])
            results = self.infer(feats, feats_len, cache)
            return results
        else:
            if self.frontend is not None:
                feats, feats_len = self.frontend.forward(speech, speech_lengths, cache_en["is_final"])
@@ -232,7 +235,7 @@
                        feats_len = torch.tensor([feats_chunk2.shape[1]])
                        results_chunk2 = self.infer(feats_chunk2, feats_len, cache)

                        return results_chunk1 + results_chunk2
                        return ["".join(results_chunk1 + results_chunk2)]

                results = self.infer(feats, feats_len, cache)

@@ -460,13 +463,23 @@
        array = np.frombuffer((middle_data.astype(dtype) - offset) / abs_max, dtype=np.float32)
        return array

    def _read_yaml(yaml_path: Union[str, Path]) -> Dict:
        if not Path(yaml_path).exists():
            raise FileExistsError(f'The {yaml_path} does not exist.')

        with open(str(yaml_path), 'rb') as f:
            data = yaml.load(f, Loader=yaml.Loader)
        return data

    def _prepare_cache(cache: dict = {}, chunk_size=[5,10,5], batch_size=1):
        if len(cache) > 0:
            return cache

        cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, 320)),
        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], 560))}
                    "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}
@@ -476,9 +489,12 @@

    def _cache_reset(cache: dict = {}, chunk_size=[5,10,5], batch_size=1):
        if len(cache) > 0:
            cache_en = {"start_idx": 0, "cif_hidden": torch.zeros((batch_size, 1, 320)),
            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], 560))}
                        "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}
@@ -718,4 +734,3 @@
    #
    # rec_result = inference_16k_pipline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
    # print(rec_result)