From b76af7be8cd7428f19ec0ba9a7fd811148fbc358 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期日, 28 四月 2024 21:18:45 +0800
Subject: [PATCH] Merge branch 'dev_gzf_exp' of github.com:alibaba-damo-academy/FunASR into dev_gzf_exp merge
---
funasr/datasets/sense_voice_datasets/datasets.py | 132 ++++++++++++++++++++------------
funasr/train_utils/trainer.py | 42 +++++++--
funasr/bin/train.py | 15 ++-
docs/images/wechat.png | 0
4 files changed, 123 insertions(+), 66 deletions(-)
diff --git a/docs/images/wechat.png b/docs/images/wechat.png
index 6d19842..ac8fa38 100644
--- a/docs/images/wechat.png
+++ b/docs/images/wechat.png
Binary files differ
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index 448e464..97516eb 100644
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -13,7 +13,7 @@
from contextlib import nullcontext
import torch.distributed as dist
-from collections.abc import Sequence
+
from omegaconf import DictConfig, OmegaConf
from torch.cuda.amp import autocast, GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
@@ -99,7 +99,7 @@
if freeze_param is not None:
if "," in freeze_param:
freeze_param = eval(freeze_param)
- if not isinstance(freeze_param, Sequence):
+ if not isinstance(freeze_param, (list, tuple)):
freeze_param = (freeze_param,)
logging.info("freeze_param is not None: %s", freeze_param)
for t in freeze_param:
@@ -193,7 +193,7 @@
try:
from tensorboardX import SummaryWriter
- writer = SummaryWriter(tensorboard_dir) if trainer.rank == 0 else None
+ writer = SummaryWriter(tensorboard_dir) # if trainer.rank == 0 else None
except:
writer = None
@@ -206,6 +206,7 @@
epoch, data_split_i=data_split_i, start_step=trainer.start_step
)
trainer.start_step = 0
+
trainer.train_epoch(
model=model,
optim=optim,
@@ -222,11 +223,13 @@
torch.cuda.empty_cache()
trainer.validate_epoch(
- model=model, dataloader_val=dataloader_val, epoch=epoch, writer=writer
+ model=model, dataloader_val=dataloader_val, epoch=epoch + 1, writer=writer
)
scheduler.step()
-
- trainer.save_checkpoint(epoch, model=model, optim=optim, scheduler=scheduler, scaler=scaler)
+ trainer.step_in_epoch = 0
+ trainer.save_checkpoint(
+ epoch + 1, model=model, optim=optim, scheduler=scheduler, scaler=scaler
+ )
time2 = time.perf_counter()
time_escaped = (time2 - time1) / 3600.0
diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
index 226342c..1d269dd 100644
--- a/funasr/datasets/sense_voice_datasets/datasets.py
+++ b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -51,6 +51,7 @@
self.batch_size = kwargs.get("batch_size")
self.batch_type = kwargs.get("batch_type")
self.prompt_ids_len = 0
+ self.retry = kwargs.get("retry", 5)
def get_source_len(self, index):
item = self.index_ds[index]
@@ -64,59 +65,75 @@
return len(self.index_ds)
def __getitem__(self, index):
- item = self.index_ds[index]
# import pdb;
# pdb.set_trace()
- source = item["source"]
- data_src = load_audio_text_image_video(source, fs=self.fs)
- if self.preprocessor_speech:
- data_src = self.preprocessor_speech(data_src, fs=self.fs)
- speech, speech_lengths = extract_fbank(
- data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
- ) # speech: [b, T, d]
- if speech_lengths > self.batch_size:
- return None
- speech = speech.permute(0, 2, 1)
- target = item["target"]
- if self.preprocessor_text:
- target = self.preprocessor_text(target)
+ output = None
+ for idx in range(self.retry):
+ if idx == 0:
+ index_cur = index
+ else:
+ if index <= self.retry:
+ index_cur = index + idx
+ else:
+ index_cur = torch.randint(0, index, ()).item()
- task = item.get("prompt", "<|ASR|>")
- text_language = item.get("text_language", "<|zh|>")
+ item = self.index_ds[index_cur]
- prompt = f"{self.sos}{task}{text_language}"
- prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
- prompt_ids_len = len(prompt_ids) - 1 # [sos, task]
- self.prompt_ids_len = prompt_ids_len
+ source = item["source"]
+ data_src = load_audio_text_image_video(source, fs=self.fs)
+ if self.preprocessor_speech:
+ data_src = self.preprocessor_speech(data_src, fs=self.fs)
+ speech, speech_lengths = extract_fbank(
+ data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
+ ) # speech: [b, T, d]
- target_ids = self.tokenizer.encode(target, allowed_special="all")
- target_ids_len = len(target_ids) + 1 # [lid, text]
- if target_ids_len > 200:
- return None
+ if speech_lengths > self.batch_size:
+ continue
+ speech = speech.permute(0, 2, 1)
+ target = item["target"]
+ if self.preprocessor_text:
+ target = self.preprocessor_text(target)
- eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
+ task = item.get("prompt", "<|ASR|>")
+ text_language = item.get("text_language", "<|zh|>")
- ids = prompt_ids + target_ids + eos
- ids_lengths = len(ids)
+ prompt = f"{self.sos}{task}{text_language}"
+ prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
+ prompt_ids_len = len(prompt_ids) - 1 # [sos, task]
+ self.prompt_ids_len = prompt_ids_len
- text = torch.tensor(ids, dtype=torch.int64)
- text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
+ target_ids = self.tokenizer.encode(target, allowed_special="all")
+ target_ids_len = len(target_ids) + 1 # [lid, text]
+ if target_ids_len > 200:
+ continue
- target_mask = (
- [0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
- ) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
- target_mask_lengths = len(target_mask)
- target_mask = torch.tensor(target_mask, dtype=torch.float32)
- target_mask_lengths = torch.tensor([target_mask_lengths], dtype=torch.int32)
- return {
- "speech": speech[0, :, :],
- "speech_lengths": speech_lengths,
- "text": text,
- "text_lengths": text_lengths,
- "target_mask": target_mask,
- "target_mask_lengths": target_mask_lengths,
- }
+ eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
+
+ ids = prompt_ids + target_ids + eos
+ ids_lengths = len(ids)
+
+ text = torch.tensor(ids, dtype=torch.int64)
+ text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
+
+ target_mask = (
+ [0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
+ ) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
+ target_mask_lengths = len(target_mask)
+ target_mask = torch.tensor(target_mask, dtype=torch.float32)
+ target_mask_lengths = torch.tensor([target_mask_lengths], dtype=torch.int32)
+
+ output = {
+ "speech": speech[0, :, :],
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ "target_mask": target_mask,
+ "target_mask_lengths": target_mask_lengths,
+ }
+ break
+
+ return output
def collator(self, samples: list = None):
outputs = {}
@@ -129,13 +146,30 @@
outputs[key].append(sample[key])
if len(outputs) < 1:
- logging.info(f"ERROR: data is empty!")
+ logging.error(f"ERROR: data is empty!")
outputs = {
- "speech": torch.rand((10, 128), dtype=torch.float32),
- "speech_lengths": torch.tensor([10], dtype=torch.int32),
- "text": torch.tensor([58836], dtype=torch.int32),
- "text_lengths": torch.tensor([1], dtype=torch.int32),
- "target_mask": torch.tensor([[0] * (self.prompt_ids_len) + [1] * (1) + [1]]),
+ "speech": torch.rand((10, 128), dtype=torch.float32)[None, :, :],
+ "speech_lengths": torch.tensor(
+ [
+ 10,
+ ],
+ dtype=torch.int32,
+ )[:, None],
+ "text": torch.tensor(
+ [
+ 58836,
+ ],
+ dtype=torch.int32,
+ )[None, :],
+ "text_lengths": torch.tensor(
+ [
+ 1,
+ ],
+ dtype=torch.int32,
+ )[:, None],
+ "target_mask": torch.tensor([[0] * (self.prompt_ids_len) + [1] * (1) + [1]])[
+ None, :
+ ],
}
return outputs
diff --git a/funasr/train_utils/trainer.py b/funasr/train_utils/trainer.py
index 66f8778..e86420c 100644
--- a/funasr/train_utils/trainer.py
+++ b/funasr/train_utils/trainer.py
@@ -116,6 +116,7 @@
self.reset_gpu_cache = kwargs.get("reset_gpu_cache", False)
self.start_data_split_i = 0
self.start_step = 0
+ self.step_in_epoch = 0
self.use_wandb = kwargs.get("use_wandb", False)
if self.use_wandb:
wandb.login(key=kwargs.get("wandb_token"))
@@ -137,6 +138,8 @@
optim=None,
scheduler=None,
scaler=None,
+ step_in_epoch=None,
+ **kwargs,
):
"""
Saves a checkpoint containing the model's state, the optimizer's state,
@@ -147,6 +150,7 @@
epoch (int): The epoch number at which the checkpoint is being saved.
"""
+ step_in_epoch = None if step is None else step_in_epoch
if self.rank == 0:
logging.info(f"Save checkpoint: {epoch}, rank: {self.local_rank}\n")
# self.step_or_epoch += 1
@@ -161,7 +165,12 @@
"best_step_or_epoch": self.best_step_or_epoch,
"avg_keep_nbest_models_type": self.avg_keep_nbest_models_type,
"step": step,
+ "step_in_epoch": step_in_epoch,
+ "data_split_i": kwargs.get("data_split_i", 0),
+ "data_split_num": kwargs.get("data_split_num", 1),
+ "batch_total": self.batch_total,
}
+ step = step_in_epoch
if hasattr(model, "module"):
state["state_dict"] = model.module.state_dict()
@@ -195,7 +204,7 @@
)
else:
logging.info(
- f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}"
+ f"No improvement in acc: {self.val_acc_step_or_eoch[ckpt_name]:.4f} < {self.val_acc_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
)
elif self.avg_keep_nbest_models_type == "loss":
if (
@@ -210,7 +219,7 @@
)
else:
logging.info(
- f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}"
+ f"No improvement in loss: {self.val_loss_step_or_eoch[ckpt_name]:.4f} > {self.val_loss_step_or_eoch[self.best_step_or_epoch]:.4f}, {os.path.join(self.output_dir, self.best_step_or_epoch)}"
)
else:
print("Undo")
@@ -251,7 +260,7 @@
ckpt = os.path.join(self.output_dir, "model.pt")
if os.path.isfile(ckpt):
checkpoint = torch.load(ckpt, map_location="cpu")
- self.start_epoch = checkpoint["epoch"] + 1
+ self.start_epoch = checkpoint["epoch"]
# self.model.load_state_dict(checkpoint['state_dict'])
src_state = checkpoint["state_dict"]
dst_state = model.state_dict()
@@ -288,11 +297,15 @@
checkpoint["best_step_or_epoch"] if "best_step_or_epoch" in checkpoint else ""
)
self.start_data_split_i = (
- checkpoint["start_data_split_i"] if "start_data_split_i" in checkpoint else 0
+ checkpoint["data_split_i"] if "data_split_i" in checkpoint else 0
)
self.batch_total = checkpoint["batch_total"] if "batch_total" in checkpoint else 0
self.start_step = checkpoint["step"] if "step" in checkpoint else 0
self.start_step = 0 if self.start_step is None else self.start_step
+ self.step_in_epoch = (
+ checkpoint["step_in_epoch"] if "step_in_epoch" in checkpoint else 0
+ )
+ self.step_in_epoch = 0 if self.step_in_epoch is None else self.step_in_epoch
model.to(self.device)
print(f"Checkpoint loaded successfully from '{ckpt}'")
@@ -321,7 +334,7 @@
"""
if self.use_ddp or self.use_fsdp:
dist.barrier()
- logging.info(f"Train epoch: {epoch}, rank: {self.local_rank}\n")
+ logging.info(f"Train epoch: {epoch}, rank: {self.rank}\n")
model.train()
# Set the number of steps for gradient accumulation
@@ -341,6 +354,7 @@
if iterator_stop > 0:
break
self.batch_total += 1
+ self.step_in_epoch += 1
time1 = time.perf_counter()
speed_stats["data_load"] = f"{time1-time_beg:0.3f}"
@@ -443,6 +457,7 @@
self.log(
epoch,
batch_idx,
+ step_in_epoch=self.step_in_epoch,
batch_num_epoch=batch_num_epoch,
lr=lr,
loss=loss.detach().cpu().item(),
@@ -454,16 +469,17 @@
data_split_num=kwargs.get("data_split_num", 1),
)
- if (batch_idx + 1) % self.validate_interval == 0:
+ if self.step_in_epoch % self.validate_interval == 0:
self.validate_epoch(
model=model,
dataloader_val=dataloader_val,
epoch=epoch,
writer=writer,
step=batch_idx + 1,
+ step_in_epoch=self.step_in_epoch,
)
- if (batch_idx + 1) % self.save_checkpoint_interval == 0:
+ if self.step_in_epoch % self.save_checkpoint_interval == 0:
self.save_checkpoint(
epoch,
model=model,
@@ -471,6 +487,9 @@
scheduler=scheduler,
scaler=scaler,
step=batch_idx + 1,
+ step_in_epoch=self.step_in_epoch,
+ data_split_i=kwargs.get("data_split_i", 0),
+ data_split_num=kwargs.get("data_split_num", 1),
)
time_beg = time.perf_counter()
@@ -500,7 +519,7 @@
"""
if self.use_ddp or self.use_fsdp:
dist.barrier()
- logging.info(f"Validate epoch: {epoch}, rank: {self.local_rank}\n")
+ logging.info(f"Validate epoch: {epoch}, rank: {self.rank}\n")
model.eval()
with torch.no_grad():
@@ -578,10 +597,10 @@
iterator_stop.fill_(1)
dist.all_reduce(iterator_stop, dist.ReduceOp.SUM)
- if kwargs.get("step", None) is None:
+ if kwargs.get("step_in_epoch", None) is None:
ckpt_name = f"model.pt.ep{epoch}"
else:
- ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step")}'
+ ckpt_name = f'model.pt.ep{epoch}.{kwargs.get("step_in_epoch")}'
self.val_acc_step_or_eoch[ckpt_name] = self.val_acc_avg
self.val_loss_step_or_eoch[ckpt_name] = self.val_loss_avg
model.train()
@@ -594,6 +613,7 @@
self,
epoch=0,
batch_idx=0,
+ step_in_epoch=0,
batch_num_epoch=-1,
lr=0.0,
loss=0.0,
@@ -627,7 +647,7 @@
f"rank: {self.rank}, "
f"epoch: {epoch}/{self.max_epoch}, "
f"data_slice: {data_split_i}/{data_split_num}, "
- f"step: {batch_idx + 1}/{batch_num_epoch}, total step: {self.batch_total}, "
+ f"step_in_slice: {batch_idx + 1}/{batch_num_epoch}, step_in_epoch: {step_in_epoch}, total step: {self.batch_total}, "
f"(loss_avg_rank: {loss:.3f}), "
f"(loss_avg_epoch: {loss_avg_epoch:.3f}), "
f"(ppl_avg_epoch: {math.exp(loss_avg_epoch):.3e}), "
--
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