funasr1.0 fsmn-vad streaming

游雁

2024-01-12 247c763286c780e3f1a849148528b12e15506721

funasr1.0 fsmn-vad streaming

 examples/industrial_data_pretraining/paraformer/demo.py

@@ -5,7 +5,7 @@

from funasr import AutoModel

model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revison="v2.0.0")
model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.0")

res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(res)
@@ -13,7 +13,7 @@

from funasr import AutoFrontend

frontend = AutoFrontend(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revison="v2.0.0")
frontend = AutoFrontend(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.0")

fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2)


 examples/industrial_data_pretraining/paraformer_streaming/demo.py

@@ -9,7 +9,7 @@
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention

model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revison="v2.0.0")
model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="v2.0.0")
cache = {}
res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
            chunk_size=chunk_size,

 funasr/bin/inference.py

@@ -83,7 +83,7 @@
    
    return key_list, data_list

@hydra.main(config_name=None, version_base=None)
@hydra.main(config_name=None)
def main_hydra(cfg: DictConfig):
    def to_plain_list(cfg_item):
        if isinstance(cfg_item, ListConfig):

 funasr/bin/train.py

@@ -23,7 +23,7 @@
from funasr.download.download_from_hub import download_model
from funasr.register import tables

@hydra.main(config_name=None, version_base=None)
@hydra.main(config_name=None)
def main_hydra(kwargs: DictConfig):
    if kwargs.get("debug", False):
        import pdb; pdb.set_trace()

 setup.py

@@ -40,7 +40,7 @@
        "hdbscan",
        "umap",
        "jaconv",
        "hydra-core",
        "hydra-core>=1.3.2",
    ],
    # train: The modules invoked when training only.
    "train": [