From 861147c7308b91068ffa02724fdf74ee623a909e Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 24 四月 2024 16:03:38 +0800
Subject: [PATCH] Dev gzf exp (#1654)
---
funasr/datasets/sense_voice_datasets/datasets.py | 83 +++++++++++++++++++++++------------------
1 files changed, 46 insertions(+), 37 deletions(-)
diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
index 956cf79..5468ea6 100644
--- a/funasr/datasets/sense_voice_datasets/datasets.py
+++ b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -10,28 +10,33 @@
"""
SenseVoiceDataset
"""
- def __init__(self,
- path,
- index_ds: str = None,
- frontend=None,
- tokenizer=None,
- int_pad_value: int = -1,
- float_pad_value: float = 0.0,
- **kwargs):
+
+ def __init__(
+ self,
+ path,
+ index_ds: str = None,
+ frontend=None,
+ tokenizer=None,
+ int_pad_value: int = -1,
+ float_pad_value: float = 0.0,
+ **kwargs,
+ ):
super().__init__()
index_ds_class = tables.index_ds_classes.get(index_ds)
self.index_ds = index_ds_class(path, **kwargs)
preprocessor_speech = kwargs.get("preprocessor_speech", None)
if preprocessor_speech:
preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
- preprocessor_speech = preprocessor_speech_class(**kwargs.get("preprocessor_speech_conf"))
+ preprocessor_speech = preprocessor_speech_class(
+ **kwargs.get("preprocessor_speech_conf")
+ )
self.preprocessor_speech = preprocessor_speech
preprocessor_text = kwargs.get("preprocessor_text", None)
if preprocessor_text:
preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
self.preprocessor_text = preprocessor_text
-
+
self.frontend = frontend
self.fs = 16000 if frontend is None else frontend.fs
self.data_type = "sound"
@@ -41,18 +46,18 @@
self.float_pad_value = float_pad_value
self.sos = kwargs.get("sos", "<|startoftranscript|>")
self.eos = kwargs.get("eos", "<|endoftext|>")
-
+
def get_source_len(self, index):
item = self.index_ds[index]
return self.index_ds.get_source_len(item)
-
+
def get_target_len(self, index):
item = self.index_ds[index]
return self.index_ds.get_target_len(item)
-
+
def __len__(self):
return len(self.index_ds)
-
+
def __getitem__(self, index):
item = self.index_ds[index]
# import pdb;
@@ -61,42 +66,46 @@
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]
+ speech, speech_lengths = extract_fbank(
+ data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
+ ) # speech: [b, T, d]
speech = speech.permute(0, 2, 1)
target = item["target"]
if self.preprocessor_text:
target = self.preprocessor_text(target)
-
+
task = item.get("prompt", "<|ASR|>")
text_language = item.get("text_language", "<|zh|>")
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]
+ prompt_ids_len = len(prompt_ids) - 1 # [sos, task]
target_ids = self.tokenizer.encode(target, allowed_special="all")
- target_ids_len = len(target_ids) + 1 # [lid, text]
-
- eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
-
+ target_ids_len = len(target_ids) + 1 # [lid, text]
+
+ 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 = (
+ [0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
+ ) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
target_mask = torch.tensor(target_mask, dtype=torch.float32)
- return {"speech": speech[0, :, :],
- "speech_lengths": speech_lengths,
- "text": text,
- "text_lengths": text_lengths,
- "target_mask": target_mask,
- }
-
-
- def collator(self, samples: list=None):
+ return {
+ "speech": speech[0, :, :],
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ "target_mask": target_mask,
+ }
+
+ def collator(self, samples: list = None):
outputs = {}
for sample in samples:
for key in sample.keys():
@@ -107,12 +116,12 @@
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:
-
+
pad_value = self.int_pad_value
else:
pad_value = self.float_pad_value
-
- outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
+
+ outputs[key] = torch.nn.utils.rnn.pad_sequence(
+ data_list, batch_first=True, padding_value=pad_value
+ )
return outputs
-
-
--
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