From 1596f6f414f6f41da66506debb1dff19fffeb3ec Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 24 六月 2024 11:55:17 +0800
Subject: [PATCH] fixbug hotwords
---
funasr/datasets/sense_voice_datasets/datasets.py | 180 +++++++++++++++++++++++++++++++++++++++++++++++------------
1 files changed, 142 insertions(+), 38 deletions(-)
diff --git a/funasr/datasets/sense_voice_datasets/datasets.py b/funasr/datasets/sense_voice_datasets/datasets.py
index 5468ea6..d4e14f2 100644
--- a/funasr/datasets/sense_voice_datasets/datasets.py
+++ b/funasr/datasets/sense_voice_datasets/datasets.py
@@ -1,6 +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
@@ -46,6 +49,16 @@
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]
@@ -59,59 +72,123 @@
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]
- 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|>")
+ 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()
- 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]
+ item = self.index_ds[index_cur]
- target_ids = self.tokenizer.encode(target, allowed_special="all")
- target_ids_len = len(target_ids) + 1 # [lid, text]
+ 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
- eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
+ 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]
- ids = prompt_ids + target_ids + eos
- ids_lengths = len(ids)
+ 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)
- text = torch.tensor(ids, dtype=torch.int64)
- text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
+ task = item.get("prompt", "<|ASR|>")
+ text_language = item.get("text_language", "<|zh|>")
- 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)
+ 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
- return {
- "speech": speech[0, :, :],
- "speech_lengths": speech_lengths,
- "text": text,
- "text_lengths": text_lengths,
- "target_mask": target_mask,
- }
+ 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 = {}
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],
+ "target_mask": torch.tensor([[0] * (self.prompt_ids_len) + [1] * (1) + [1]])[
+ None, :
+ ],
+ }
+ return outputs
for key, data_list in outputs.items():
if isinstance(data_list[0], torch.Tensor):
@@ -124,4 +201,31 @@
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]
+ target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
+ outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
+
return outputs
--
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