speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

funasr2

Browse Source
This commit is contained in:
游雁 2023-12-06 19:42:02 +08:00
parent 27f31cd42b
commit e98e10639d
3 changed files with 29 additions and 29 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
funasr/cli/models/paraformer.py
View File

@ -594,7 +594,7 @@ class Paraformer(nn.Module):
for li in range(bsz):
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
if target_num > 0:
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].cuda(), value=0)
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0)
input_mask = input_mask.eq(1)
input_mask = input_mask.masked_fill(~nonpad_positions, False)
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
@ -624,7 +624,7 @@ class Paraformer(nn.Module):
for li in range(bsz):
target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
if target_num > 0:
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num], value=0)
input_mask[li].scatter_(dim=0, index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device), value=0)
input_mask = input_mask.eq(1)
input_mask = input_mask.masked_fill(~nonpad_positions, False)
input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)

45
funasr/cli/train_cli.py
View File

@ -50,7 +50,7 @@ def main(kwargs: DictConfig):
use_fsdp = kwargs.get("use_fsdp", None)
if use_ddp or use_fsdp:
dist.init_process_group(backend=kwargs.get("backend", "nccl"), init_method='env://')
device= torch.cuda.set_device(local_rank)
torch.cuda.set_device(local_rank)
# build_tokenizer
@ -72,9 +72,24 @@ def main(kwargs: DictConfig):
# model_class = load_class_from_path(kwargs.get("model").split(":"))
model_class = dynamic_import(kwargs.get("model"))
model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list))
# model = model.to(device=kwargs.get("device", "cpu"))
frontend = model.frontend
# init_param
init_param = kwargs.get("init_param", None)
if init_param is not None:
init_param = eval(init_param)
if isinstance(init_param, Sequence):
init_param = (init_param,)
logging.info("init_param is not None: ", init_param)
for p in init_param:
logging.info(f"Loading pretrained params from {p}")
load_pretrained_model(
model=model,
init_param=p,
ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
oss_bucket=kwargs.get("oss_bucket", None),
)
else:
initialize(model, kwargs.get("init", "kaiming_normal"))
# import pdb;
# pdb.set_trace()
@ -97,6 +112,8 @@ def main(kwargs: DictConfig):
model = DDP(model, device_ids=[local_rank])
elif use_fsdp:
model = FSDP(model).cuda(local_rank)
else:
model = model.to(device=kwargs.get("device", "cuda"))
# optim
@ -111,27 +128,9 @@ def main(kwargs: DictConfig):
scheduler_class = scheduler_choices.get(scheduler)
scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
# init_param
init_param = kwargs.get("init_param", None)
if init_param is not None:
init_param = eval(init_param)
if isinstance(init_param, Sequence):
init_param = (init_param,)
logging.info("init_param is not None: ", freeze_param)
for p in init_param:
logging.info(f"Loading pretrained params from {p}")
load_pretrained_model(
model=model,
init_param=p,
ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
oss_bucket=kwargs.get("oss_bucket", None),
)
else:
initialize(model, kwargs.get("init", "kaiming_normal"))
# dataset
dataset_tr = AudioDataset(kwargs.get("train_data_set_list"), frontend=model.frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf"))
dataset_tr = AudioDataset(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf"))
# dataloader
batch_sampler = BatchSampler(dataset_tr, **kwargs.get("dataset_conf"), **kwargs.get("dataset_conf").get("batch_conf"))

9
funasr/cli/trainer.py
View File

@ -131,7 +131,7 @@ class Trainer:
for batch_idx, batch in enumerate(self.dataloader_train):
batch = to_device(batch, self.device)
my_context = model.no_sync if batch_idx % accumulation_steps != 0 else nullcontext
my_context = self.model.no_sync if batch_idx % accumulation_steps != 0 else nullcontext
with my_context():
retval = self.model(**batch)
loss, stats, weight = retval
@ -163,9 +163,10 @@ class Trainer:
self.optim.zero_grad()
pbar.update(1)
pbar.set_description(
f"Training Epoch: {epoch + 1}/{self.max_epoch}, step {batch_idx}/{len(self.dataloader_train)} (loss: {loss.detach().float()})")
if self.local_rank == 0:
pbar.set_description(
f"Training Epoch: {epoch + 1}/{self.max_epoch}, step {batch_idx}/{len(self.dataloader_train)} (loss: {loss.detach().float()})")
pbar.close()
# def _train_epoch(self, epoch):