install requirements automatically

游雁

2024-03-25 447222c00e545906879364c641846f399290dcee

install requirements automatically

 examples/common_voice/whisper_lid/demo_funasr.py

@@ -12,7 +12,7 @@
    "example_ko.mp3",
]

model = AutoModel(model="iic/speech_whisper-large_lid_multilingual_pytorch", model_revision="v2.0.4")
model = AutoModel(model="iic/speech_whisper-large_lid_multilingual_pytorch", model_revision="master")
for wav_id in multilingual_wavs:
    wav_file = f"{model.model_path}/examples/{wav_id}"
    res = model.generate(input=wav_file, data_type="sound", inference_clip_length=250)

 examples/common_voice/whisper_lid/demo_modelscope.py

@@ -15,7 +15,7 @@

inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='iic/speech_whisper-large_lid_multilingual_pytorch', model_revision="v2.0.4")
    model='iic/speech_whisper-large_lid_multilingual_pytorch', model_revision="master")

for wav in multilingual_wavs:
    rec_result = inference_pipeline(input=wav, inference_clip_length=250)

 examples/industrial_data_pretraining/bicif_paraformer/demo.py

@@ -6,11 +6,11 @@
from funasr import AutoModel

model = AutoModel(model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                  model_revision="v2.0.4",
                  model_revision="master",
                  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  vad_model_revision="v2.0.4",
                  vad_model_revision="master",
                  punc_model="iic/punc_ct-transformer_cn-en-common-vocab471067-large",
                  punc_model_revision="v2.0.4",
                  punc_model_revision="master",
                  # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
                  # spk_model_revision="v2.0.2",
                  )

 examples/industrial_data_pretraining/bicif_paraformer/demo.sh

@@ -1,11 +1,11 @@

model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"
vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
vad_model_revision="v2.0.4"
vad_model_revision="master"
#punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
punc_model="iic/punc_ct-transformer_cn-en-common-vocab471067-large"
punc_model_revision="v2.0.4"
punc_model_revision="master"
spk_model="iic/speech_campplus_sv_zh-cn_16k-common"
spk_model_revision="v2.0.2"


 examples/industrial_data_pretraining/bicif_paraformer/export.py

@@ -8,7 +8,7 @@
from funasr import AutoModel

model = AutoModel(model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                  model_revision="v2.0.4", device="cpu")
                  model_revision="master", device="cpu")

res = model.export(type="onnx", quantize=False)
print(res)

 examples/industrial_data_pretraining/bicif_paraformer/export.sh

@@ -6,7 +6,7 @@


model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"

python -m funasr.bin.export \
++model=${model} \

 examples/industrial_data_pretraining/bicif_paraformer/finetune.sh

@@ -11,7 +11,7 @@

## option 1, download model automatically
model_name_or_model_dir="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"

## option 2, download model by git
#local_path_root=${workspace}/modelscope_models

 examples/industrial_data_pretraining/conformer/demo.py

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

from funasr import AutoModel

model = AutoModel(model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch", model_revision="v2.0.4",
model = AutoModel(model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch", model_revision="master",
                  )

res = model.generate(input="https://modelscope.oss-cn-beijing.aliyuncs.com/test/audios/asr_example.wav")

 examples/industrial_data_pretraining/conformer/demo.sh

@@ -1,6 +1,6 @@

model="iic/speech_conformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch"
model_revision="v2.0.4"
model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/contextual_paraformer/demo.py

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

from funasr import AutoModel

model = AutoModel(model="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="v2.0.4")
model = AutoModel(model="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="master")

res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
            hotword='达摩院 魔搭')

 examples/industrial_data_pretraining/contextual_paraformer/demo.sh

@@ -1,6 +1,6 @@

model="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404"
model_revision="v2.0.4"
model_revision="master"

python ../../../funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/contextual_paraformer/finetune.sh

@@ -11,7 +11,7 @@

## option 1, download model automatically
model_name_or_model_dir="iic/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404"
model_revision="v2.0.4"
model_revision="master"

## option 2, download model by git
#local_path_root=${workspace}/modelscope_models

 examples/industrial_data_pretraining/ct_transformer/demo.py

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

from funasr import AutoModel

model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.4")
model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="master")

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

from funasr import AutoModel

model = AutoModel(model="iic/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="v2.0.4")
model = AutoModel(model="iic/punc_ct-transformer_cn-en-common-vocab471067-large", model_revision="master")

res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
print(res)

 examples/industrial_data_pretraining/ct_transformer/demo.sh

@@ -1,9 +1,9 @@

#model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
#model_revision="v2.0.4"
#model_revision="master"

model="iic/punc_ct-transformer_cn-en-common-vocab471067-large"
model_revision="v2.0.4"
model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/ct_transformer/export.py

@@ -8,7 +8,7 @@
from funasr import AutoModel

model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
                  model_revision="v2.0.4")
                  model_revision="master")

res = model.export(type="onnx", quantize=False)
print(res)

 examples/industrial_data_pretraining/ct_transformer/export.sh

@@ -6,7 +6,7 @@


model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
model_revision="v2.0.4"
model_revision="master"

python -m funasr.bin.export \
++model=${model} \

 examples/industrial_data_pretraining/ct_transformer_streaming/demo.py

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

from funasr import AutoModel

model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727", model_revision="v2.0.4")
model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727", model_revision="master")

inputs = "跨境河流是养育沿岸|人民的生命之源长期以来为帮助下游地区防灾减灾中方技术人员|在上游地区极为恶劣的自然条件下克服巨大困难甚至冒着生命危险|向印方提供汛期水文资料处理紧急事件中方重视印方在跨境河流问题上的关切|愿意进一步完善双方联合工作机制|凡是|中方能做的我们|都会去做而且会做得更好我请印度朋友们放心中国在上游的|任何开发利用都会经过科学|规划和论证兼顾上下游的利益"
vads = inputs.split("|")

 examples/industrial_data_pretraining/ct_transformer_streaming/demo.sh

@@ -1,6 +1,6 @@

model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727"
model_revision="v2.0.4"
model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/ct_transformer_streaming/export.py

@@ -8,7 +8,7 @@
from funasr import AutoModel

model = AutoModel(model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727",
                  model_revision="v2.0.4")
                  model_revision="master")

res = model.export(type="onnx", quantize=False)
print(res)

 examples/industrial_data_pretraining/ct_transformer_streaming/export.sh

@@ -6,7 +6,7 @@


model="iic/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727"
model_revision="v2.0.4"
model_revision="master"

python -m funasr.bin.export \
++model=${model} \

 examples/industrial_data_pretraining/emotion2vec/demo.py

@@ -6,9 +6,9 @@
from funasr import AutoModel

# model="iic/emotion2vec_base"
model = AutoModel(model="iic/emotion2vec_base_finetuned", model_revision="v2.0.4",
model = AutoModel(model="iic/emotion2vec_base_finetuned", model_revision="master",
                  # vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  # vad_model_revision="v2.0.4",
                  # vad_model_revision="master",
                  # vad_kwargs={"max_single_segment_time": 2000},
                  )


 examples/industrial_data_pretraining/fsmn_vad_streaming/demo.py

@@ -6,7 +6,7 @@
from funasr import AutoModel
wav_file = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav"

model = AutoModel(model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.4")
model = AutoModel(model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="master")

res = model.generate(input=wav_file)
print(res)

 examples/industrial_data_pretraining/fsmn_vad_streaming/demo.sh

@@ -1,7 +1,7 @@


model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
model_revision="v2.0.4"
model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/fsmn_vad_streaming/export.py

@@ -8,7 +8,7 @@

from funasr import AutoModel

model = AutoModel(model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.4")
model = AutoModel(model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="master")

res = model.export(type="onnx", quantize=False)
print(res)

 examples/industrial_data_pretraining/fsmn_vad_streaming/export.sh

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


model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
model_revision="v2.0.4"
model_revision="master"

python -m funasr.bin.export \
++model=${model} \

 examples/industrial_data_pretraining/monotonic_aligner/demo.py

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

from funasr import AutoModel

model = AutoModel(model="iic/speech_timestamp_prediction-v1-16k-offline", model_revision="v2.0.4")
model = AutoModel(model="iic/speech_timestamp_prediction-v1-16k-offline", model_revision="master")

res = model.generate(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
                   "欢迎大家来到魔搭社区进行体验"),

 examples/industrial_data_pretraining/monotonic_aligner/demo.sh

@@ -1,6 +1,6 @@

model="iic/speech_timestamp_prediction-v1-16k-offline"
model_revision="v2.0.4"
model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/paraformer-zh-spk/demo.py

@@ -6,11 +6,11 @@
from funasr import AutoModel

model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                  model_revision="v2.0.4",
                  model_revision="master",
                  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  vad_model_revision="v2.0.4",
                  vad_model_revision="master",
                  punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
                  punc_model_revision="v2.0.4",
                  punc_model_revision="master",
                  # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
                  # spk_model_revision="v2.0.2"
                  )

 examples/industrial_data_pretraining/paraformer-zh-spk/demo.sh

@@ -1,10 +1,10 @@

model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"
vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
vad_model_revision="v2.0.4"
vad_model_revision="master"
punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
punc_model_revision="v2.0.4"
punc_model_revision="master"
spk_model="iic/speech_campplus_sv_zh-cn_16k-common"
spk_model_revision="v2.0.2"


 examples/industrial_data_pretraining/paraformer/demo.py

@@ -6,12 +6,12 @@
from funasr import AutoModel

model = AutoModel(model="iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch", 
                  model_revision="v2.0.4",
                  model_revision="master",
                  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  vad_model_revision="v2.0.4",
                  vad_model_revision="master",
                  vad_kwargs={"max_single_segment_time": 60000},
                  punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
                  punc_model_revision="v2.0.4",
                  punc_model_revision="master",
                  # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
                  # spk_model_revision="v2.0.2",
                  )
@@ -23,7 +23,7 @@
''' can not use currently
from funasr import AutoFrontend

frontend = AutoFrontend(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.4")
frontend = AutoFrontend(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="master")

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/export.py

@@ -10,7 +10,7 @@
from funasr import AutoModel

model = AutoModel(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                  model_revision="v2.0.4")
                  model_revision="master")

res = model.export(type="onnx", quantize=False)
print(res)

 examples/industrial_data_pretraining/paraformer/export.sh

@@ -6,7 +6,7 @@


model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"


python -m funasr.bin.export \

 examples/industrial_data_pretraining/paraformer/finetune.sh

@@ -10,7 +10,7 @@

## option 1, download model automatically
model_name_or_model_dir="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"

## option 2, download model by git
#local_path_root=${workspace}/modelscope_models

 examples/industrial_data_pretraining/paraformer/infer.sh

@@ -9,7 +9,7 @@
output_dir="./outputs/debug"

model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"

device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"


 examples/industrial_data_pretraining/paraformer_streaming/demo.py

@@ -10,7 +10,7 @@
chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
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="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="v2.0.4")
model = AutoModel(model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online", model_revision="master")

wav_file = os.path.join(model.model_path, "example/asr_example.wav")
res = model.generate(input=wav_file,

 examples/industrial_data_pretraining/paraformer_streaming/demo.sh

@@ -1,6 +1,6 @@

model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
model_revision="v2.0.4"
model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/paraformer_streaming/export.py

@@ -10,7 +10,7 @@
from funasr import AutoModel

model = AutoModel(model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online",
                  model_revision="v2.0.4")
                  model_revision="master")

res = model.export(type="onnx", quantize=False)
print(res)

 examples/industrial_data_pretraining/paraformer_streaming/export.sh

@@ -6,7 +6,7 @@


model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
model_revision="v2.0.4"
model_revision="master"


python -m funasr.bin.export \

 examples/industrial_data_pretraining/paraformer_streaming/finetune.sh

@@ -10,7 +10,7 @@

## option 1, download model automatically
model_name_or_model_dir="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
model_revision="v2.0.4"
model_revision="master"

## option 2, download model by git
#local_path_root=${workspace}/modelscope_models

 examples/industrial_data_pretraining/scama/demo.py

@@ -9,7 +9,7 @@
encoder_chunk_look_back = 0 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 0 #number of encoder chunks to lookback for decoder cross-attention

model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_SCAMA_asr-zh-cn-16k-common-vocab8358-streaming", model_revision="v2.0.4")
model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_SCAMA_asr-zh-cn-16k-common-vocab8358-streaming", model_revision="master")
cache = {}
res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
            chunk_size=chunk_size,

 examples/industrial_data_pretraining/scama/demo.sh

@@ -1,6 +1,6 @@

model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online"
model_revision="v2.0.4"
model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/seaco_paraformer/demo.py

@@ -6,11 +6,11 @@
from funasr import AutoModel

model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
                  model_revision="v2.0.4",
                  model_revision="master",
                  # vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  # vad_model_revision="v2.0.4",
                  # vad_model_revision="master",
                  # punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
                  # punc_model_revision="v2.0.4",
                  # punc_model_revision="master",
                  # spk_model="iic/speech_campplus_sv_zh-cn_16k-common",
                  # spk_model_revision="v2.0.2",
                  )

 examples/industrial_data_pretraining/seaco_paraformer/demo.sh

@@ -1,10 +1,10 @@

model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"
vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
vad_model_revision="v2.0.4"
vad_model_revision="master"
punc_model="iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
punc_model_revision="v2.0.4"
punc_model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/uniasr/demo.py

@@ -6,7 +6,7 @@
from funasr import AutoModel


model = AutoModel(model="iic/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline", model_revision="v2.0.4",)
model = AutoModel(model="iic/speech_UniASR-large_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline", model_revision="master",)


res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
@@ -16,7 +16,7 @@
''' can not use currently
from funasr import AutoFrontend

frontend = AutoFrontend(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="v2.0.4")
frontend = AutoFrontend(model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revision="master")

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/uniasr/demo.sh

@@ -1,6 +1,6 @@

model="iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
model_revision="master"

python funasr/bin/inference.py \
+model=${model} \

 examples/industrial_data_pretraining/whisper/infer.sh

@@ -11,7 +11,7 @@
output_dir="./outputs/debug"

model="iic/speech_whisper-large_asr_multilingual"
model_revision="v2.0.4"
model_revision="master"

device="cuda:0" # "cuda:0" for gpu0, "cuda:1" for gpu1, "cpu"


 funasr/auto/auto_model.py

@@ -109,7 +109,7 @@
        if vad_model is not None:
            logging.info("Building VAD model.")
            vad_kwargs["model"] = vad_model
            vad_kwargs["model_revision"] = kwargs.get("vad_model_revision", None)
            vad_kwargs["model_revision"] = kwargs.get("vad_model_revision", "master")
            vad_kwargs["device"] = kwargs["device"]
            vad_model, vad_kwargs = self.build_model(**vad_kwargs)

@@ -119,7 +119,7 @@
        if punc_model is not None:
            logging.info("Building punc model.")
            punc_kwargs["model"] = punc_model
            punc_kwargs["model_revision"] = kwargs.get("punc_model_revision", None)
            punc_kwargs["model_revision"] = kwargs.get("punc_model_revision", "master")
            punc_kwargs["device"] = kwargs["device"]
            punc_model, punc_kwargs = self.build_model(**punc_kwargs)

@@ -129,7 +129,7 @@
        if spk_model is not None:
            logging.info("Building SPK model.")
            spk_kwargs["model"] = spk_model
            spk_kwargs["model_revision"] = kwargs.get("spk_model_revision", None)
            spk_kwargs["model_revision"] = kwargs.get("spk_model_revision", "master")
            spk_kwargs["device"] = kwargs["device"]
            spk_model, spk_kwargs = self.build_model(**spk_kwargs)
            self.cb_model = ClusterBackend().to(kwargs["device"])

 funasr/download/download_from_hub.py

@@ -29,7 +29,7 @@
    model_or_path = kwargs.get("model")
    if model_or_path in name_maps_ms:
        model_or_path = name_maps_ms[model_or_path]
    model_revision = kwargs.get("model_revision")
    model_revision = kwargs.get("model_revision", "master")
    if not os.path.exists(model_or_path) and "model_path" not in kwargs:
        try:
            model_or_path = get_or_download_model_dir(model_or_path, model_revision,

 funasr/models/llm_asr/model.py

@@ -73,7 +73,7 @@
        hub = audio_encoder_conf.get("hub", None)
        if hub == "ms":
            from funasr import AutoModel
            model = AutoModel(model=audio_encoder, model_revision="v2.0.4")
            model = AutoModel(model=audio_encoder, model_revision="master")
            # frontend = model.kwargs.get("frontend")
            audio_encoder_output_size = model.model.encoder_output_size


 funasr/models/llm_asr_nar/model.py

@@ -75,7 +75,7 @@
        if hub == "funasr":
            from funasr import AutoModel
            init_param_path = encoder_conf.get("init_param_path", "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
            model = AutoModel(model=init_param_path, model_revision="v2.0.4")
            model = AutoModel(model=init_param_path, model_revision="master")
            # frontend = model.kwargs.get("frontend")
            model.model.decoder = None
            
@@ -406,7 +406,7 @@
            from funasr import AutoModel
            init_param_path = encoder_conf.get("init_param_path",
                                               "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
            model = AutoModel(model=init_param_path, model_revision="v2.0.4")
            model = AutoModel(model=init_param_path, model_revision="master")
            # frontend = model.kwargs.get("frontend")
            model.model.decoder = None