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Merge pull request #80 from alibaba-damo-academy/dev

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add speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-offline &…
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hnluo 2023-02-09 14:15:18 +08:00 committed by GitHub
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25 changed files with 394 additions and 9 deletions
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35
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-offline/finetune.py Normal file
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@ -0,0 +1,35 @@
import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-offline"
params["model_revision"] = None
modelscope_finetune(params)

13
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-offline/infer.py Normal file
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@ -0,0 +1,13 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == "__main__":
audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_de.wav"
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-offline",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)
print(rec_result)

35
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/finetune.py Normal file
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@ -0,0 +1,35 @@
import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online"
params["model_revision"] = None
modelscope_finetune(params)

13
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online/infer.py Normal file
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@ -0,0 +1,13 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == "__main__":
audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_de.wav"
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-de-16k-common-vocab3690-tensorflow1-online",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)
print(rec_result)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline/finetune.py
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@ -30,6 +30,6 @@ if __name__ == '__main__':
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online"
params["model"] = "damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline"
params["model_revision"] = None
modelscope_finetune(params)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline/infer.py
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@ -6,7 +6,7 @@ if __name__ == "__main__":
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-online",
model="damo/speech_UniASR_asr_2pass-en-16k-common-vocab1080-tensorflow1-offline",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline/finetune.py
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@ -30,6 +30,6 @@ if __name__ == '__main__':
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online"
params["model"] = "damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline"
params["model_revision"] = None
modelscope_finetune(params)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline/infer.py
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@ -6,7 +6,7 @@ if __name__ == "__main__":
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-online",
model="damo/speech_UniASR_asr_2pass-es-16k-common-vocab3445-tensorflow1-offline",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)

35
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-offline/finetune.py Normal file
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import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-offline"
params["model_revision"] = None
modelscope_finetune(params)

13
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-offline/infer.py Normal file
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@ -0,0 +1,13 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == "__main__":
audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_fa.wav"
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-offline",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)
print(rec_result)

35
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/finetune.py Normal file
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@ -0,0 +1,35 @@
import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online"
params["model_revision"] = None
modelscope_finetune(params)

13
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/infer.py Normal file
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@ -0,0 +1,13 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == "__main__":
audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_fa.wav"
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)
print(rec_result)

35
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-offline/finetune.py Normal file
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@ -0,0 +1,35 @@
import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-offline"
params["model_revision"] = None
modelscope_finetune(params)

13
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-offline/infer.py Normal file
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@ -0,0 +1,13 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == "__main__":
audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_fr.wav"
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-offline",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)
print(rec_result)

35
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/finetune.py Normal file
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@ -0,0 +1,35 @@
import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online"
params["model_revision"] = None
modelscope_finetune(params)

13
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online/infer.py Normal file
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@ -0,0 +1,13 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == "__main__":
audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_fr.wav"
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-fr-16k-common-vocab3472-tensorflow1-online",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)
print(rec_result)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline/finetune.py
View File

@ -30,6 +30,6 @@ if __name__ == '__main__':
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online"
params["model"] = "damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline"
params["model_revision"] = None
modelscope_finetune(params)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline/infer.py
View File

@ -6,7 +6,7 @@ if __name__ == "__main__":
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-online",
model="damo/speech_UniASR_asr_2pass-ko-16k-common-vocab6400-tensorflow1-offline",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline/finetune.py
View File

@ -30,6 +30,6 @@ if __name__ == '__main__':
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online"
params["model"] = "damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline"
params["model_revision"] = None
modelscope_finetune(params)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline/infer.py
View File

@ -6,7 +6,7 @@ if __name__ == "__main__":
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-online",
model="damo/speech_UniASR_asr_2pass-ru-16k-common-vocab1664-tensorflow1-offline",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)

35
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-offline/finetune.py Normal file
View File

@ -0,0 +1,35 @@
import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-offline"
params["model_revision"] = None
modelscope_finetune(params)

13
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-offline/infer.py Normal file
View File

@ -0,0 +1,13 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == "__main__":
audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_vi.wav"
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-offline",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)
print(rec_result)

35
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/finetune.py Normal file
View File

@ -0,0 +1,35 @@
import os
from modelscope.metainfo import Trainers
from modelscope.trainers import build_trainer
from funasr.datasets.ms_dataset import MsDataset
def modelscope_finetune(params):
if not os.path.exists(params["output_dir"]):
os.makedirs(params["output_dir"], exist_ok=True)
# dataset split ["train", "validation"]
ds_dict = MsDataset.load(params["data_dir"])
kwargs = dict(
model=params["model"],
model_revision=params["model_revision"],
data_dir=ds_dict,
dataset_type=params["dataset_type"],
work_dir=params["output_dir"],
batch_bins=params["batch_bins"],
max_epoch=params["max_epoch"],
lr=params["lr"])
trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
trainer.train()
if __name__ == '__main__':
params = {}
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data"
params["batch_bins"] = 2000
params["dataset_type"] = "small"
params["max_epoch"] = 50
params["lr"] = 0.00005
params["model"] = "damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online"
params["model_revision"] = None
modelscope_finetune(params)

13
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online/infer.py Normal file
View File

@ -0,0 +1,13 @@
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
if __name__ == "__main__":
audio_in = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_vi.wav"
output_dir = "./results"
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_UniASR_asr_2pass-vi-16k-common-vocab1001-pytorch-online",
output_dir=output_dir,
)
rec_result = inference_pipline(audio_in=audio_in)
print(rec_result)

3
funasr/bin/build_trainer.py
View File

@ -49,7 +49,8 @@ def build_trainer(modelscope_dict,
scheduler_conf=None,
specaug=None,
specaug_conf=None,
param_dict=None):
param_dict=None,
**kwargs):
mode = modelscope_dict['mode']
args, ASRTask = parse_args(mode=mode)
# ddp related