bugfix

游雁

2024-07-22 e42f539eb8acd106463a5bc466a39497691fb4ca

bugfix

 examples/industrial_data_pretraining/paraformer/export.py

@@ -20,10 +20,10 @@

# # method2, inference from local path
# from funasr import AutoModel

#
# model = AutoModel(
#     model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
# )

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

 examples/industrial_data_pretraining/paraformer/export.sh

@@ -15,10 +15,10 @@
++quantize=false


# method2, inference from local path
model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"

python -m funasr.bin.export \
++model=${model} \
++type="onnx" \
++quantize=false
## method2, inference from local path
#model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
#
#python -m funasr.bin.export \
#++model=${model} \
#++type="onnx" \
#++quantize=false

 examples/industrial_data_pretraining/paraformer_streaming/export.py

@@ -17,13 +17,13 @@
print(res)


# method2, inference from local path
from funasr import AutoModel


model = AutoModel(
    model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
)

res = model.export(type="onnx", quantize=False)
print(res)
# # method2, inference from local path
# from funasr import AutoModel
#
#
# model = AutoModel(
#     model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
# )
#
# res = model.export(type="onnx", quantize=False)
# print(res)

 examples/industrial_data_pretraining/paraformer_streaming/export.sh

@@ -4,10 +4,7 @@
# method1, inference from model hub
export HYDRA_FULL_ERROR=1


model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"



python -m funasr.bin.export \
++model=${model} \
@@ -16,12 +13,12 @@
++device="cpu"


# method2, inference from local path
model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"

python -m funasr.bin.export \
++model=${model} \
++type="onnx" \
++quantize=false \
++device="cpu" \
++debug=false
## method2, inference from local path
#model="/Users/zhifu/.cache/modelscope/hub/iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online"
#
#python -m funasr.bin.export \
#++model=${model} \
#++type="onnx" \
#++quantize=false \
#++device="cpu" \
#++debug=false

 examples/industrial_data_pretraining/sense_voice/demo.py

@@ -7,7 +7,7 @@
from funasr import AutoModel
from funasr.utils.postprocess_utils import rich_transcription_postprocess

model_dir = "iic/SenseVoiceSmall"
model_dir = "/Users/zhifu/Downloads/modelscope_models/SenseVoiceSmall"  # "iic/SenseVoiceSmall"


model = AutoModel(
@@ -19,17 +19,30 @@

# en
res = model.generate(
    input=f"{model.model_path}/example/en.mp3",
    input="/Users/zhifu/Downloads/8_output.wav",
    cache={},
    language="auto",  # "zn", "en", "yue", "ja", "ko", "nospeech"
    use_itn=True,
    batch_size_s=60,
    merge_vad=True,  #
    merge_length_s=15,
    merge_length_s=0.1,
)
text = rich_transcription_postprocess(res[0]["text"])
print(text)

# en
res = model.generate(
    input="/Users/zhifu/Downloads/8_output.wav",
    cache={},
    language="auto",  # "zn", "en", "yue", "ja", "ko", "nospeech"
    use_itn=True,
    batch_size_s=60,
    merge_vad=False,  #
    merge_length_s=15,
)
text = rich_transcription_postprocess(res[0]["text"])
print(text)
raise "exit"
# zh
res = model.generate(
    input=f"{model.model_path}/example/zh.mp3",

 funasr/models/paraformer/export_meta.py

@@ -31,7 +31,7 @@
    model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
    model.export_name = types.MethodType(export_name, model)

    model.export_name = 'model'
    model.export_name = "model"
    return model


@@ -83,4 +83,4 @@
def export_name(
    self,
):
    return "model.onnx"
    return "model"

 funasr/models/paraformer_streaming/export_meta.py

@@ -9,32 +9,6 @@


def export_rebuild_model(model, **kwargs):
    model.device = kwargs.get("device")
    is_onnx = kwargs.get("type", "onnx") == "onnx"
    encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
    model.encoder = encoder_class(model.encoder, onnx=is_onnx)

    predictor_class = tables.predictor_classes.get(kwargs["predictor"] + "Export")
    model.predictor = predictor_class(model.predictor, onnx=is_onnx)

    decoder_class = tables.decoder_classes.get(kwargs["decoder"] + "Export")
    model.decoder = decoder_class(model.decoder, onnx=is_onnx)

    from funasr.utils.torch_function import sequence_mask

    model.make_pad_mask = sequence_mask(kwargs["max_seq_len"], flip=False)

    model.forward = types.MethodType(export_forward, model)
    model.export_dummy_inputs = types.MethodType(export_dummy_inputs, model)
    model.export_input_names = types.MethodType(export_input_names, model)
    model.export_output_names = types.MethodType(export_output_names, model)
    model.export_dynamic_axes = types.MethodType(export_dynamic_axes, model)
    model.export_name = types.MethodType(export_name, model)

    return model


def export_rebuild_model(model, **kwargs):
    # self.device = kwargs.get("device")
    is_onnx = kwargs.get("type", "onnx") == "onnx"
    encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
@@ -64,7 +38,7 @@
    encoder_model.export_input_names = types.MethodType(export_encoder_input_names, encoder_model)
    encoder_model.export_output_names = types.MethodType(export_encoder_output_names, encoder_model)
    encoder_model.export_dynamic_axes = types.MethodType(export_encoder_dynamic_axes, encoder_model)
    encoder_model.export_name = types.MethodType(export_encoder_name, encoder_model)
    encoder_model.export_name = "model"  # types.MethodType(export_encoder_name, encoder_model)

    # decoder
    decoder_model.forward = types.MethodType(export_decoder_forward, decoder_model)
@@ -72,7 +46,7 @@
    decoder_model.export_input_names = types.MethodType(export_decoder_input_names, decoder_model)
    decoder_model.export_output_names = types.MethodType(export_decoder_output_names, decoder_model)
    decoder_model.export_dynamic_axes = types.MethodType(export_decoder_dynamic_axes, decoder_model)
    decoder_model.export_name = types.MethodType(export_decoder_name, decoder_model)
    decoder_model.export_name = "decoder"  # types.MethodType(export_decoder_name, decoder_model)

    return encoder_model, decoder_model