From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/datasets/sense_voice_datasets/datasets.py | 359 ++++++++++++++++++++++++++++++++++++++++++++++++++---------
1 files changed, 304 insertions(+), 55 deletions(-)
diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
index 6d9b035..2e0266e 100644
--- a/funasr/datasets/sense_voice_datasets/datasets.py
+++ b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -1,8 +1,9 @@
import logging
+import re
import torch
import random
-
+import traceback
from funasr.register import tables
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
@@ -51,6 +52,13 @@
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)
+
+ self.permute = False
+ from funasr.frontends.whisper_frontend import WhisperFrontend
+
+ if isinstance(self.frontend, WhisperFrontend):
+ self.permute = True
def get_source_len(self, index):
item = self.index_ds[index]
@@ -64,59 +72,85 @@
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:
+ index_cur = torch.randint(0, len(self.index_ds), ()).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"]
+ try:
+ data_src = load_audio_text_image_video(source, fs=self.fs)
+ except Exception as e:
+ logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
+ continue
- 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 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]
- eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
+ if speech_lengths > self.batch_size:
+ continue
+ if self.permute:
+ speech = speech.permute(0, 2, 1)
+ target = item["target"]
+ if self.preprocessor_text:
+ target = self.preprocessor_text(target)
- ids = prompt_ids + target_ids + eos
- ids_lengths = len(ids)
+ task = item.get("prompt", "<|ASR|>")
+ text_language = item.get("text_language", "<|zh|>")
- text = torch.tensor(ids, dtype=torch.int64)
- text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
+ if isinstance(self.sos, str):
+ prompt = f"{self.sos}{task}{text_language}"
+ prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
+ else:
+ prompt = f"{task}{text_language}"
+ prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
+ prompt_ids = [self.sos] + prompt_ids
- 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,
- }
+ 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:
+ continue
+
+ if isinstance(self.eos, str):
+ eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
+ else:
+ eos = [self.eos]
+
+ ids = prompt_ids + target_ids + eos # [sos, task, lid, text, 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 +163,30 @@
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
@@ -152,14 +203,14 @@
)
if self.batch_type != "example":
- for i in range(3):
+ for i in range(10):
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 * 1.25:
beg = torch.randint(0, 2, ()).item()
if b < 2:
@@ -170,7 +221,6 @@
for key, data_list in outputs.items():
outputs[key] = outputs[key][beg : beg + b : 2]
-
speech_lengths_max = outputs["speech_lengths"].max().item()
outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
text_lengths_max = outputs["text_lengths"].max().item()
@@ -179,3 +229,202 @@
outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
return outputs
+
+
+@tables.register("dataset_classes", "SenseVoiceCTCDataset")
+class SenseVoiceCTCDataset(torch.utils.data.Dataset):
+ """
+ SenseVoiceCTCDataset
+ """
+
+ 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")
+ )
+ 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"
+ self.tokenizer = tokenizer
+
+ self.int_pad_value = int_pad_value
+ self.float_pad_value = float_pad_value
+ self.sos = kwargs.get("sos", "<|startoftranscript|>")
+ 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
+ self.retry = kwargs.get("retry", 5)
+
+ self.permute = False
+ from funasr.frontends.whisper_frontend import WhisperFrontend
+
+ if isinstance(self.frontend, WhisperFrontend):
+ self.permute = True
+
+ 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):
+
+ output = None
+ for idx in range(self.retry):
+ if idx == 0:
+ index_cur = index
+ else:
+ index_cur = torch.randint(0, len(self.index_ds), ()).item()
+
+ item = self.index_ds[index_cur]
+
+ source = item["source"]
+ try:
+ data_src = load_audio_text_image_video(source, fs=self.fs)
+ except Exception as e:
+ logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
+ continue
+
+ 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:
+ continue
+ if self.permute:
+ speech = speech.permute(0, 2, 1)
+ asr_target = item["target"]
+ if self.preprocessor_text:
+ asr_target = self.preprocessor_text(asr_target)
+ emo_target = item.get("emo_target", "<|NEUTRAL|>")
+ event_target = item.get("event_target", "<|Speech|>")
+ text_language = item.get("text_language", "<|zh|>")
+ punc_itn_bottom = item.get("with_or_wo_itn", "<|woitn|>")
+
+ target_ids = self.tokenizer.encode(asr_target, allowed_special="all")
+ target_ids_len = len(target_ids) # [text]
+ if target_ids_len > 200:
+ continue
+
+ lid_ids = self.tokenizer.encode(text_language, allowed_special="all")
+ emo_ids = self.tokenizer.encode(emo_target, allowed_special="all")
+ event_ids = self.tokenizer.encode(event_target, allowed_special="all")
+ punc_itn_bottom_ids = self.tokenizer.encode(punc_itn_bottom, allowed_special="all")
+
+ ids = lid_ids + emo_ids + event_ids + punc_itn_bottom_ids + target_ids # [lid, emo, lid, itn, text]
+ ids_lengths = len(ids)
+
+ text = torch.tensor(ids, dtype=torch.int64)
+ text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
+
+ output = {
+ "speech": speech[0, :, :],
+ "speech_lengths": speech_lengths,
+ "text": text,
+ "text_lengths": text_lengths,
+ }
+ break
+
+ return output
+
+ 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.error(f"ERROR: data is empty!")
+ outputs = {
+ "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],
+ }
+ 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:
+
+ 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
+ )
+
+ if self.batch_type != "example":
+ for i in range(10):
+ 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 * 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().item()
+ outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
+ text_lengths_max = outputs["text_lengths"].max().item()
+ outputs["text"] = outputs["text"][:, :text_lengths_max]
+
+ return outputs
--
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