python/FunASR-XL.git

			@@ -1,5 +1,4 @@
			import os
			<<<<<<< HEAD

			from modelscope.metainfo import Trainers
			from modelscope.trainers import build_trainer
			@@ -21,50 +20,17 @@
			batch_bins=params.batch_bins,
			max_epoch=params.max_epoch,
			lr=params.lr)
			=======
			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"])
			>>>>>>> main
			trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
			trainer.train()


			if __name__ == '__main__':
			<<<<<<< HEAD
			params = modelscope_args(model="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch", data_path="./data")
			params.output_dir = "./checkpoint" # m模型保存路径
			params.data_path = "./example_data/" # 数据路径
			params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large
			params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒，
			params.max_epoch = 50 # 最大训练轮数
			params.max_epoch = 20 # 最大训练轮数
			params.lr = 0.00005 # 设置学习率

			=======
			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-tr-16k-common-vocab1582-pytorch"
			params["model_revision"] = None
			>>>>>>> main
			modelscope_finetune(params)
			modelscope_finetune(params)

			@@ -1,33 +1,3 @@
			<<<<<<< HEAD
			import os
			import shutil
			import argparse
			from modelscope.pipelines import pipeline
			from modelscope.utils.constant import Tasks

			def modelscope_infer(args):
			os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
			inference_pipeline = pipeline(
			task=Tasks.auto_speech_recognition,
			model=args.model,
			output_dir=args.output_dir,
			batch_size=args.batch_size,
			param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
			)
			inference_pipeline(audio_in=args.audio_in)

			if __name__ == "__main__":
			parser = argparse.ArgumentParser()
			parser.add_argument('--model', type=str, default="damo/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch")
			parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
			parser.add_argument('--output_dir', type=str, default="./results/")
			parser.add_argument('--decoding_mode', type=str, default="normal")
			parser.add_argument('--hotword_txt', type=str, default=None)
			parser.add_argument('--batch_size', type=int, default=64)
			parser.add_argument('--gpuid', type=str, default="0")
			args = parser.parse_args()
			modelscope_infer(args)
			=======
			from modelscope.pipelines import pipeline
			from modelscope.utils.constant import Tasks

			@@ -40,5 +10,4 @@
			output_dir=output_dir,
			)
			rec_result = inference_pipeline(audio_in=audio_in, param_dict={"decoding_model":"offline"})
			print(rec_result)
			>>>>>>> main
			print(rec_result)

	egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/finetune.py	38 ●●●●● 补丁 \| 查看 \| 原始文档 \| blame \| 历史
	egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-tr-16k-common-vocab1582-pytorch/infer.py	33 ●●●●● 补丁 \| 查看 \| 原始文档 \| blame \| 历史