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This commit is contained in:
zhifu gao 2024-04-26 11:27:39 +08:00 committed by GitHub
parent 8fdc372c81
commit 1cdb3cc28d
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GPG Key ID: B5690EEEBB952194
2 changed files with 31 additions and 5 deletions
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33
funasr/datasets/sense_voice_datasets/datasets.py
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@ -50,6 +50,7 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
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 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
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 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
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]
@ -108,16 +115,30 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
"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):
if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
@ -132,25 +153,29 @@ class SenseVoiceDataset(torch.utils.data.Dataset):
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

3
funasr/models/sense_voice/model.py
View File

@ -309,7 +309,7 @@ class SenseVoiceRWKV(nn.Module):
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 @@ class SenseVoiceRWKV(nn.Module):
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: