From 1cdb3cc28d4d89a576cc06e5cd8eb80da1f3a3aa Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 26 四月 2024 11:27:39 +0800
Subject: [PATCH] Dev gzf exp (#1665)
---
funasr/datasets/sense_voice_datasets/datasets.py | 33 +++++++++++++++++++++++++++++----
funasr/models/sense_voice/model.py | 3 ++-
2 files changed, 31 insertions(+), 5 deletions(-)
diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
index 6a79e75..6d9b035 100644
--- a/funasr/datasets/sense_voice_datasets/datasets.py
+++ b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -50,6 +50,7 @@
self.eos = kwargs.get("eos", "<|endoftext|>")
self.batch_size = kwargs.get("batch_size")
self.batch_type = kwargs.get("batch_type")
+ self.prompt_ids_len = 0
def get_source_len(self, index):
item = self.index_ds[index]
@@ -73,6 +74,9 @@
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:
@@ -84,9 +88,12 @@
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
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
eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
@@ -108,15 +115,29 @@
"text": text,
"text_lengths": text_lengths,
"target_mask": target_mask,
+ "target_mask_lengths": target_mask_lengths,
}
def collator(self, samples: list = None):
outputs = {}
for sample in samples:
+ if sample is None:
+ continue
for key in sample.keys():
if key not in outputs:
outputs[key] = []
outputs[key].append(sample[key])
+
+ if len(outputs) < 1:
+ logging.info(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]]),
+ }
+ return outputs
for key, data_list in outputs.items():
if isinstance(data_list[0], torch.Tensor):
@@ -132,25 +153,29 @@
if self.batch_type != "example":
for i in range(3):
- outputs = self._filter_badcase(outputs)
+ outputs = self._filter_badcase(outputs, i=i)
return outputs
def _filter_badcase(self, outputs, i=0):
b, t, _ = outputs["speech"].shape
- if b * t > self.batch_size:
+
+ if b * t > self.batch_size * 1.25:
beg = torch.randint(0, 2, ()).item()
+ if b < 2:
+ beg = 0
logging.info(
f"Warning, b * t: {b * t} > {self.batch_size}, drop half data {i}th, beg:{beg}"
)
for key, data_list in outputs.items():
outputs[key] = outputs[key][beg : beg + b : 2]
- speech_lengths_max = outputs["speech_lengths_max"].max().item()
+
+ speech_lengths_max = outputs["speech_lengths"].max().item()
outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
text_lengths_max = outputs["text_lengths"].max().item()
outputs["text"] = outputs["text"][:, :text_lengths_max]
- target_mask_lengths_max = outputs["target_mask_lengths_max"].max().item()
+ target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
return outputs
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index b731bb6..07fb4eb 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -309,7 +309,7 @@
if len(speech_lengths.size()) > 1:
speech_lengths = speech_lengths[:, 0]
- batch_size = speech.shape[0]
+ batch_size, frames, _ = speech.shape
if self.activation_checkpoint:
from torch.utils.checkpoint import checkpoint
@@ -328,6 +328,7 @@
stats["acc"] = acc_att
stats["loss"] = torch.clone(loss.detach())
stats["batch_size"] = batch_size
+ stats["batch_size_x_frames"] = frames * batch_size
# force_gatherable: to-device and to-tensor if scalar for DataParallel
if self.length_normalized_loss:
--
Gitblit v1.9.1