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Update asr_inference_paraformer_streaming.py

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hnluo 2023-04-27 10:46:18 +08:00 committed by GitHub
parent 493dda8f98
commit 9624eba825
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1 changed files with 19 additions and 6 deletions
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funasr/bin/asr_inference_paraformer_streaming.py
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@ -8,6 +8,7 @@ import os
import codecs
import tempfile
import requests
import yaml
from pathlib import Path
from typing import Optional
from typing import Sequence
@ -462,13 +463,23 @@ def inference_modelscope(
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)), "tail_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}
@ -478,9 +489,12 @@ def inference_modelscope(
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)), "tail_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}
@ -720,4 +734,3 @@ if __name__ == "__main__":
#
# 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)