mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
98 lines
2.8 KiB
Python
98 lines
2.8 KiB
Python
from funasr.bin.diar_inference_launch import inference_launch
|
|
import os
|
|
|
|
|
|
def test_fbank_cpu_infer():
|
|
diar_config_path = "config_fbank.yaml"
|
|
diar_model_path = "sond.pb"
|
|
output_dir = "./outputs"
|
|
data_path_and_name_and_type = [
|
|
("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
|
|
("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
|
|
]
|
|
pipeline = inference_launch(
|
|
mode="sond",
|
|
diar_train_config=diar_config_path,
|
|
diar_model_file=diar_model_path,
|
|
output_dir=output_dir,
|
|
num_workers=1,
|
|
log_level="WARNING",
|
|
)
|
|
results = pipeline(data_path_and_name_and_type)
|
|
print(results)
|
|
|
|
|
|
def test_fbank_gpu_infer():
|
|
diar_config_path = "config_fbank.yaml"
|
|
diar_model_path = "sond.pb"
|
|
output_dir = "./outputs"
|
|
data_path_and_name_and_type = [
|
|
("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
|
|
("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
|
|
]
|
|
pipeline = inference_launch(
|
|
mode="sond",
|
|
diar_train_config=diar_config_path,
|
|
diar_model_file=diar_model_path,
|
|
output_dir=output_dir,
|
|
ngpu=1,
|
|
num_workers=1,
|
|
log_level="WARNING",
|
|
)
|
|
results = pipeline(data_path_and_name_and_type)
|
|
print(results)
|
|
|
|
|
|
def test_wav_gpu_infer():
|
|
diar_config_path = "config.yaml"
|
|
diar_model_path = "sond.pb"
|
|
output_dir = "./outputs"
|
|
data_path_and_name_and_type = [
|
|
("data/unit_test/test_wav.scp", "speech", "sound"),
|
|
("data/unit_test/test_profile.scp", "profile", "kaldi_ark"),
|
|
]
|
|
pipeline = inference_launch(
|
|
mode="sond",
|
|
diar_train_config=diar_config_path,
|
|
diar_model_file=diar_model_path,
|
|
output_dir=output_dir,
|
|
ngpu=1,
|
|
num_workers=1,
|
|
log_level="WARNING",
|
|
)
|
|
results = pipeline(data_path_and_name_and_type)
|
|
print(results)
|
|
|
|
|
|
def test_without_profile_gpu_infer():
|
|
diar_config_path = "config.yaml"
|
|
diar_model_path = "sond.pb"
|
|
output_dir = "./outputs"
|
|
raw_inputs = [[
|
|
"data/unit_test/raw_inputs/record.wav",
|
|
"data/unit_test/raw_inputs/spk1.wav",
|
|
"data/unit_test/raw_inputs/spk2.wav",
|
|
"data/unit_test/raw_inputs/spk3.wav",
|
|
"data/unit_test/raw_inputs/spk4.wav"
|
|
]]
|
|
pipeline = inference_launch(
|
|
mode="sond_demo",
|
|
diar_train_config=diar_config_path,
|
|
diar_model_file=diar_model_path,
|
|
output_dir=output_dir,
|
|
ngpu=1,
|
|
num_workers=1,
|
|
log_level="WARNING",
|
|
param_dict={},
|
|
)
|
|
results = pipeline(raw_inputs=raw_inputs)
|
|
print(results)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
|
|
test_fbank_cpu_infer()
|
|
test_fbank_gpu_infer()
|
|
test_wav_gpu_infer()
|
|
test_without_profile_gpu_infer()
|