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update egs_modelscope recipe

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北念 2023-03-16 16:47:30 +08:00
parent d1cde93914
commit ba7d7a3628
10 changed files with 149 additions and 123 deletions
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31
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch/infer.py
View File

@ -8,9 +8,14 @@ from modelscope.utils.constant import Tasks
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_core(output_dir, split_dir, njob, idx):
def modelscope_infer_core(output_dir, split_dir, njob, idx, batch_size, ngpu, model):
output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
gpu_id = (int(idx) - 1) // njob
if ngpu > 0:
use_gpu = 1
gpu_id = int(idx) - 1
else:
use_gpu = 0
gpu_id = -1
if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
@ -18,9 +23,10 @@ def modelscope_infer_core(output_dir, split_dir, njob, idx):
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch",
model=model,
output_dir=output_dir_job,
batch_size=64
batch_size=batch_size,
ngpu=use_gpu,
)
audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
inference_pipline(audio_in=audio_in)
@ -30,13 +36,18 @@ def modelscope_infer(params):
# prepare for multi-GPU decoding
ngpu = params["ngpu"]
njob = params["njob"]
batch_size = params["batch_size"]
output_dir = params["output_dir"]
model = params["model"]
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.mkdir(output_dir)
split_dir = os.path.join(output_dir, "split")
os.mkdir(split_dir)
nj = ngpu * njob
if ngpu > 0:
nj = ngpu
elif ngpu == 0:
nj = njob
wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
with open(wav_scp_file) as f:
lines = f.readlines()
@ -56,7 +67,7 @@ def modelscope_infer(params):
p = Pool(nj)
for i in range(nj):
p.apply_async(modelscope_infer_core,
args=(output_dir, split_dir, njob, str(i + 1)))
args=(output_dir, split_dir, njob, str(i + 1), batch_size, ngpu, model))
p.close()
p.join()
@ -81,8 +92,10 @@ def modelscope_infer(params):
if __name__ == "__main__":
params = {}
params["model"] = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch"
params["data_dir"] = "./data/test"
params["output_dir"] = "./results"
params["ngpu"] = 1
params["njob"] = 1
modelscope_infer(params)
params["ngpu"] = 1 # if ngpu > 0, will use gpu decoding
params["njob"] = 1 # if ngpu = 0, will use cpu decoding
params["batch_size"] = 64
modelscope_infer(params)

27
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch/infer_after_finetune.py
View File

@ -4,23 +4,18 @@ import shutil
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.hub.snapshot_download import snapshot_download
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_after_finetune(params):
# prepare for decoding
pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
for file_name in params["required_files"]:
if file_name == "configuration.json":
with open(os.path.join(pretrained_model_path, file_name)) as f:
config_dict = json.load(f)
config_dict["model"]["am_model_name"] = params["decoding_model_name"]
with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
json.dump(config_dict, f, indent=4, separators=(',', ': '))
else:
shutil.copy(os.path.join(pretrained_model_path, file_name),
os.path.join(params["output_dir"], file_name))
try:
pretrained_model_path = snapshot_download(params["modelscope_model_name"], cache_dir=params["output_dir"])
except BaseException:
raise BaseException(f"Please download pretrain model from ModelScope firstly.")
shutil.copy(os.path.join(params["output_dir"], params["decoding_model_name"]), os.path.join(pretrained_model_path, "model.pb"))
decoding_path = os.path.join(params["output_dir"], "decode_results")
if os.path.exists(decoding_path):
shutil.rmtree(decoding_path)
@ -29,9 +24,9 @@ def modelscope_infer_after_finetune(params):
# decoding
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model=params["output_dir"],
model=pretrained_model_path,
output_dir=decoding_path,
batch_size=64
batch_size=params["batch_size"]
)
audio_in = os.path.join(params["data_dir"], "wav.scp")
inference_pipeline(audio_in=audio_in)
@ -46,8 +41,8 @@ def modelscope_infer_after_finetune(params):
if __name__ == '__main__':
params = {}
params["modelscope_model_name"] = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch"
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)
params["batch_size"] = 64
modelscope_infer_after_finetune(params)

31
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch/infer.py
View File

@ -8,9 +8,14 @@ from modelscope.utils.constant import Tasks
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_core(output_dir, split_dir, njob, idx):
def modelscope_infer_core(output_dir, split_dir, njob, idx, batch_size, ngpu, model):
output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
gpu_id = (int(idx) - 1) // njob
if ngpu > 0:
use_gpu = 1
gpu_id = int(idx) - 1
else:
use_gpu = 0
gpu_id = -1
if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
@ -18,9 +23,10 @@ def modelscope_infer_core(output_dir, split_dir, njob, idx):
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch",
model=model,
output_dir=output_dir_job,
batch_size=64
batch_size=batch_size,
ngpu=use_gpu,
)
audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
inference_pipline(audio_in=audio_in)
@ -30,13 +36,18 @@ def modelscope_infer(params):
# prepare for multi-GPU decoding
ngpu = params["ngpu"]
njob = params["njob"]
batch_size = params["batch_size"]
output_dir = params["output_dir"]
model = params["model"]
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.mkdir(output_dir)
split_dir = os.path.join(output_dir, "split")
os.mkdir(split_dir)
nj = ngpu * njob
if ngpu > 0:
nj = ngpu
elif ngpu == 0:
nj = njob
wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
with open(wav_scp_file) as f:
lines = f.readlines()
@ -56,7 +67,7 @@ def modelscope_infer(params):
p = Pool(nj)
for i in range(nj):
p.apply_async(modelscope_infer_core,
args=(output_dir, split_dir, njob, str(i + 1)))
args=(output_dir, split_dir, njob, str(i + 1), batch_size, ngpu, model))
p.close()
p.join()
@ -81,8 +92,10 @@ def modelscope_infer(params):
if __name__ == "__main__":
params = {}
params["model"] = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch"
params["data_dir"] = "./data/test"
params["output_dir"] = "./results"
params["ngpu"] = 1
params["njob"] = 1
modelscope_infer(params)
params["ngpu"] = 1 # if ngpu > 0, will use gpu decoding
params["njob"] = 1 # if ngpu = 0, will use cpu decoding
params["batch_size"] = 64
modelscope_infer(params)

27
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch/infer_after_finetune.py
View File

@ -4,23 +4,18 @@ import shutil
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.hub.snapshot_download import snapshot_download
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_after_finetune(params):
# prepare for decoding
pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
for file_name in params["required_files"]:
if file_name == "configuration.json":
with open(os.path.join(pretrained_model_path, file_name)) as f:
config_dict = json.load(f)
config_dict["model"]["am_model_name"] = params["decoding_model_name"]
with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
json.dump(config_dict, f, indent=4, separators=(',', ': '))
else:
shutil.copy(os.path.join(pretrained_model_path, file_name),
os.path.join(params["output_dir"], file_name))
try:
pretrained_model_path = snapshot_download(params["modelscope_model_name"], cache_dir=params["output_dir"])
except BaseException:
raise BaseException(f"Please download pretrain model from ModelScope firstly.")
shutil.copy(os.path.join(params["output_dir"], params["decoding_model_name"]), os.path.join(pretrained_model_path, "model.pb"))
decoding_path = os.path.join(params["output_dir"], "decode_results")
if os.path.exists(decoding_path):
shutil.rmtree(decoding_path)
@ -29,9 +24,9 @@ def modelscope_infer_after_finetune(params):
# decoding
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model=params["output_dir"],
model=pretrained_model_path,
output_dir=decoding_path,
batch_size=64
batch_size=params["batch_size"]
)
audio_in = os.path.join(params["data_dir"], "wav.scp")
inference_pipeline(audio_in=audio_in)
@ -46,8 +41,8 @@ def modelscope_infer_after_finetune(params):
if __name__ == '__main__':
params = {}
params["modelscope_model_name"] = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch"
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)
params["batch_size"] = 64
modelscope_infer_after_finetune(params)

8
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
View File

@ -22,10 +22,12 @@
Or you can use the finetuned model for inference directly.
- Setting parameters in `infer.py`
- <strong>model:</strong> # model name on ModelScope
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>output_dir:</strong> # result dir
- <strong>ngpu:</strong> # the number of GPUs for decoding
- <strong>njob:</strong> # the number of jobs for each GPU
- <strong>ngpu:</strong> # the number of GPUs for decoding, if `ngpu` > 0, use GPU decoding
- <strong>njob:</strong> # the number of jobs for CPU decoding, if `ngpu` = 0, use CPU decoding, please set `njob`
- <strong>batch_size:</strong> # batchsize of inference
- Then you can run the pipeline to infer with:
```python
@ -39,9 +41,11 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
### Inference using local finetuned model
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>modelscope_model_name: </strong> # model name on ModelScope
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- <strong>batch_size:</strong> # batchsize of inference
- Then you can run the pipeline to finetune with:
```python

31
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.py
View File

@ -8,9 +8,14 @@ from modelscope.utils.constant import Tasks
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_core(output_dir, split_dir, njob, idx):
def modelscope_infer_core(output_dir, split_dir, njob, idx, batch_size, ngpu, model):
output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
gpu_id = (int(idx) - 1) // njob
if ngpu > 0:
use_gpu = 1
gpu_id = int(idx) - 1
else:
use_gpu = 0
gpu_id = -1
if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
@ -18,9 +23,10 @@ def modelscope_infer_core(output_dir, split_dir, njob, idx):
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
model=model,
output_dir=output_dir_job,
batch_size=64
batch_size=batch_size,
ngpu=use_gpu,
)
audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
inference_pipline(audio_in=audio_in)
@ -30,13 +36,18 @@ def modelscope_infer(params):
# prepare for multi-GPU decoding
ngpu = params["ngpu"]
njob = params["njob"]
batch_size = params["batch_size"]
output_dir = params["output_dir"]
model = params["model"]
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.mkdir(output_dir)
split_dir = os.path.join(output_dir, "split")
os.mkdir(split_dir)
nj = ngpu * njob
if ngpu > 0:
nj = ngpu
elif ngpu == 0:
nj = njob
wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
with open(wav_scp_file) as f:
lines = f.readlines()
@ -56,7 +67,7 @@ def modelscope_infer(params):
p = Pool(nj)
for i in range(nj):
p.apply_async(modelscope_infer_core,
args=(output_dir, split_dir, njob, str(i + 1)))
args=(output_dir, split_dir, njob, str(i + 1), batch_size, ngpu, model))
p.close()
p.join()
@ -81,8 +92,10 @@ def modelscope_infer(params):
if __name__ == "__main__":
params = {}
params["model"] = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
params["data_dir"] = "./data/test"
params["output_dir"] = "./results"
params["ngpu"] = 1
params["njob"] = 1
modelscope_infer(params)
params["ngpu"] = 1 # if ngpu > 0, will use gpu decoding
params["njob"] = 1 # if ngpu = 0, will use cpu decoding
params["batch_size"] = 64
modelscope_infer(params)

27
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py
View File

@ -4,23 +4,18 @@ import shutil
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.hub.snapshot_download import snapshot_download
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_after_finetune(params):
# prepare for decoding
pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
for file_name in params["required_files"]:
if file_name == "configuration.json":
with open(os.path.join(pretrained_model_path, file_name)) as f:
config_dict = json.load(f)
config_dict["model"]["am_model_name"] = params["decoding_model_name"]
with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
json.dump(config_dict, f, indent=4, separators=(',', ': '))
else:
shutil.copy(os.path.join(pretrained_model_path, file_name),
os.path.join(params["output_dir"], file_name))
try:
pretrained_model_path = snapshot_download(params["modelscope_model_name"], cache_dir=params["output_dir"])
except BaseException:
raise BaseException(f"Please download pretrain model from ModelScope firstly.")
shutil.copy(os.path.join(params["output_dir"], params["decoding_model_name"]), os.path.join(pretrained_model_path, "model.pb"))
decoding_path = os.path.join(params["output_dir"], "decode_results")
if os.path.exists(decoding_path):
shutil.rmtree(decoding_path)
@ -29,9 +24,9 @@ def modelscope_infer_after_finetune(params):
# decoding
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model=params["output_dir"],
model=pretrained_model_path,
output_dir=decoding_path,
batch_size=64
batch_size=params["batch_size"]
)
audio_in = os.path.join(params["data_dir"], "wav.scp")
inference_pipeline(audio_in=audio_in)
@ -46,8 +41,8 @@ def modelscope_infer_after_finetune(params):
if __name__ == '__main__':
params = {}
params["modelscope_model_name"] = "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)
params["batch_size"] = 64
modelscope_infer_after_finetune(params)

31
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer.py
View File

@ -8,9 +8,14 @@ from modelscope.utils.constant import Tasks
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_core(output_dir, split_dir, njob, idx):
def modelscope_infer_core(output_dir, split_dir, njob, idx, batch_size, ngpu, model):
output_dir_job = os.path.join(output_dir, "output.{}".format(idx))
gpu_id = (int(idx) - 1) // njob
if ngpu > 0:
use_gpu = 1
gpu_id = int(idx) - 1
else:
use_gpu = 0
gpu_id = -1
if "CUDA_VISIBLE_DEVICES" in os.environ.keys():
gpu_list = os.environ['CUDA_VISIBLE_DEVICES'].split(",")
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_list[gpu_id])
@ -18,9 +23,10 @@ def modelscope_infer_core(output_dir, split_dir, njob, idx):
os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_id)
inference_pipline = pipeline(
task=Tasks.auto_speech_recognition,
model="damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1",
model=model,
output_dir=output_dir_job,
batch_size=64
batch_size=batch_size,
ngpu=use_gpu,
)
audio_in = os.path.join(split_dir, "wav.{}.scp".format(idx))
inference_pipline(audio_in=audio_in)
@ -30,13 +36,18 @@ def modelscope_infer(params):
# prepare for multi-GPU decoding
ngpu = params["ngpu"]
njob = params["njob"]
batch_size = params["batch_size"]
output_dir = params["output_dir"]
model = params["model"]
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.mkdir(output_dir)
split_dir = os.path.join(output_dir, "split")
os.mkdir(split_dir)
nj = ngpu * njob
if ngpu > 0:
nj = ngpu
elif ngpu == 0:
nj = njob
wav_scp_file = os.path.join(params["data_dir"], "wav.scp")
with open(wav_scp_file) as f:
lines = f.readlines()
@ -56,7 +67,7 @@ def modelscope_infer(params):
p = Pool(nj)
for i in range(nj):
p.apply_async(modelscope_infer_core,
args=(output_dir, split_dir, njob, str(i + 1)))
args=(output_dir, split_dir, njob, str(i + 1), batch_size, ngpu, model))
p.close()
p.join()
@ -81,8 +92,10 @@ def modelscope_infer(params):
if __name__ == "__main__":
params = {}
params["model"] = "damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1"
params["data_dir"] = "./data/test"
params["output_dir"] = "./results"
params["ngpu"] = 1
params["njob"] = 1
modelscope_infer(params)
params["ngpu"] = 1 # if ngpu > 0, will use gpu decoding
params["njob"] = 1 # if ngpu = 0, will use cpu decoding
params["batch_size"] = 64
modelscope_infer(params)

27
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer_after_finetune.py
View File

@ -4,23 +4,18 @@ import shutil
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.hub.snapshot_download import snapshot_download
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_after_finetune(params):
# prepare for decoding
pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
for file_name in params["required_files"]:
if file_name == "configuration.json":
with open(os.path.join(pretrained_model_path, file_name)) as f:
config_dict = json.load(f)
config_dict["model"]["am_model_name"] = params["decoding_model_name"]
with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
json.dump(config_dict, f, indent=4, separators=(',', ': '))
else:
shutil.copy(os.path.join(pretrained_model_path, file_name),
os.path.join(params["output_dir"], file_name))
try:
pretrained_model_path = snapshot_download(params["modelscope_model_name"], cache_dir=params["output_dir"])
except BaseException:
raise BaseException(f"Please download pretrain model from ModelScope firstly.")
shutil.copy(os.path.join(params["output_dir"], params["decoding_model_name"]), os.path.join(pretrained_model_path, "model.pb"))
decoding_path = os.path.join(params["output_dir"], "decode_results")
if os.path.exists(decoding_path):
shutil.rmtree(decoding_path)
@ -29,9 +24,9 @@ def modelscope_infer_after_finetune(params):
# decoding
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model=params["output_dir"],
model=pretrained_model_path,
output_dir=decoding_path,
batch_size=64
batch_size=params["batch_size"]
)
audio_in = os.path.join(params["data_dir"], "wav.scp")
inference_pipeline(audio_in=audio_in)
@ -46,8 +41,8 @@ def modelscope_infer_after_finetune(params):
if __name__ == '__main__':
params = {}
params["modelscope_model_name"] = "damo/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1"
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)
params["batch_size"] = 64
modelscope_infer_after_finetune(params)

32
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py
View File

@ -4,27 +4,17 @@ import shutil
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.hub.snapshot_download import snapshot_download
from funasr.utils.compute_wer import compute_wer
def modelscope_infer_after_finetune(params):
# prepare for decoding
if not os.path.exists(os.path.join(params["output_dir"], "punc")):
os.makedirs(os.path.join(params["output_dir"], "punc"))
if not os.path.exists(os.path.join(params["output_dir"], "vad")):
os.makedirs(os.path.join(params["output_dir"], "vad"))
pretrained_model_path = os.path.join(os.environ["HOME"], ".cache/modelscope/hub", params["modelscope_model_name"])
for file_name in params["required_files"]:
if file_name == "configuration.json":
with open(os.path.join(pretrained_model_path, file_name)) as f:
config_dict = json.load(f)
config_dict["model"]["am_model_name"] = params["decoding_model_name"]
with open(os.path.join(params["output_dir"], "configuration.json"), "w") as f:
json.dump(config_dict, f, indent=4, separators=(',', ': '))
else:
shutil.copy(os.path.join(pretrained_model_path, file_name),
os.path.join(params["output_dir"], file_name))
try:
pretrained_model_path = snapshot_download(params["modelscope_model_name"], cache_dir=params["output_dir"])
except BaseException:
raise BaseException(f"Please download pretrain model from ModelScope firstly.")shutil.copy(os.path.join(params["output_dir"], params["decoding_model_name"]), os.path.join(pretrained_model_path, "model.pb"))
decoding_path = os.path.join(params["output_dir"], "decode_results")
if os.path.exists(decoding_path):
shutil.rmtree(decoding_path)
@ -33,16 +23,16 @@ def modelscope_infer_after_finetune(params):
# decoding
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model=params["output_dir"],
model=pretrained_model_path,
output_dir=decoding_path,
batch_size=64
batch_size=params["batch_size"]
)
audio_in = os.path.join(params["data_dir"], "wav.scp")
inference_pipeline(audio_in=audio_in)
# computer CER if GT text is set
text_in = os.path.join(params["data_dir"], "text")
if text_in is not None:
if os.path.exists(text_in):
text_proc_file = os.path.join(decoding_path, "1best_recog/token")
compute_wer(text_in, text_proc_file, os.path.join(decoding_path, "text.cer"))
@ -50,8 +40,8 @@ def modelscope_infer_after_finetune(params):
if __name__ == '__main__':
params = {}
params["modelscope_model_name"] = "damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json", "punc/punc.pb", "punc/punc.yaml", "vad/vad.mvn", "vad/vad.pb", "vad/vad.yaml"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)
params["batch_size"] = 64
modelscope_infer_after_finetune(params)