funasr1.0 finetune

游雁

2024-01-16 2ed3f46f40cd5da19cad76a97b52c46b2869d5ed

funasr1.0 finetune

 examples/industrial_data_pretraining/paraformer/finetune.sh

@@ -1,14 +1,17 @@

# download model
local_path_root=../modelscope_models
mkdir -p ${local_path_root}
local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
## download model
#local_path_root=../modelscope_models
#mkdir -p ${local_path_root}
#local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
#git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}


python funasr/bin/train.py \
+model="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
+token_list="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/tokens.txt" \
+train_data_set_list="data/list/audio_datasets.jsonl" \
+model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
+model_revision="v2.0.2" \
+train_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len.jsonl" \
++dataset_conf.batch_size=2 \
++dataset_conf.batch_type="example" \
+output_dir="outputs/debug/ckpt/funasr2/exp2" \
+device="cpu"
+device="cpu" \
+debug="true"

 funasr/auto/auto_model.py

@@ -146,7 +146,7 @@
        device = kwargs.get("device", "cuda")
        if not torch.cuda.is_available() or kwargs.get("ngpu", 0):
            device = "cpu"
            # kwargs["batch_size"] = 1
            kwargs["batch_size"] = 1
        kwargs["device"] = device
        
        if kwargs.get("ncpu", None):

 funasr/bin/train.py

@@ -40,8 +40,7 @@


def main(**kwargs):
    # preprocess_config(kwargs)
    # import pdb; pdb.set_trace()
    
    # set random seed
    tables.print()
    set_all_random_seed(kwargs.get("seed", 0))
@@ -169,6 +168,8 @@
        local_rank=local_rank,
        use_ddp=use_ddp,
        use_fsdp=use_fsdp,
        output_dir=kwargs.get("output_dir", "./exp"),
        resume=kwargs.get("resume", True),
        **kwargs.get("train_conf"),
    )
    trainer.run()

 funasr/datasets/audio_datasets/samplers.py

@@ -20,7 +20,7 @@
        self.dataset = dataset
        self.total_samples = len(dataset)
        self.batch_type = batch_type
        self.batch_size = batch_size
        self.batch_size = int(batch_size)
        self.buffer_size = buffer_size
        self.max_token_length = kwargs.get("max_token_length", 5000)
        self.shuffle_idx = np.arange(self.total_samples)

 funasr/train_utils/trainer.py

@@ -5,6 +5,8 @@
from tqdm import tqdm
import torch.distributed as dist
from contextlib import nullcontext
# from torch.utils.tensorboard import SummaryWriter
from tensorboardX import SummaryWriter

from funasr.train_utils.device_funcs import to_device
from funasr.train_utils.recursive_op import recursive_average
@@ -34,6 +36,7 @@
                 local_rank,
                 use_ddp=False,
                 use_fsdp=False,
                 output_dir: str="./",
                 **kwargs):
        """
        Initializes the Trainer class with the model, optimizer, scheduler, dataloaders, and other settings.
@@ -55,7 +58,7 @@
        self.scheduler = scheduler
        self.dataloader_train = dataloader_train
        self.dataloader_val = dataloader_val
        self.output_dir = kwargs.get('output_dir', './')
        self.output_dir = output_dir
        self.resume = kwargs.get('resume', True)
        self.start_epoch = 0
        self.max_epoch = kwargs.get('max_epoch', 100)
@@ -77,6 +80,10 @@
            logging.warning("distributed is not initialized, only single shard")
        self.rank = rank
        self.world_size = world_size
        
        os.makedirs(os.path.join(self.output_dir, "tensorboard"), exist_ok=True)
        self.writer = SummaryWriter(os.path.join(self.output_dir, "tensorboard")) if rank == 0 else None
        
    
    def _save_checkpoint(self, epoch):
        """
@@ -128,6 +135,8 @@
            if self.rank == 0:
                self._save_checkpoint(epoch)
            self.scheduler.step()
            
        self.writer.close()
    
    def _train_epoch(self, epoch):
        """
@@ -215,7 +224,16 @@
                    f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
                )
                pbar.set_description(description)
            
                if self.writer:
                    self.writer.add_scalar('Loss/train', loss.item(),
                                           epoch*len(self.dataloader_train) + batch_idx)
                    for key, var in stats.items():
                        self.writer.add_scalar(f'{key}/train', var.item(),
                                               epoch * len(self.dataloader_train) + batch_idx)
                    for key, var in speed_stats.items():
                        self.writer.add_scalar(f'{key}/train', eval(var),
                                               epoch * len(self.dataloader_train) + batch_idx)
                    
            # if batch_idx == 2:
            #     break
        pbar.close()

 setup.py

@@ -46,6 +46,7 @@
    "train": [
        "editdistance",
        "wandb",
        "pip install tensorboardX",
    ],
    # all: The modules should be optionally installled due to some reason.
    #      Please consider moving them to "install" occasionally