fixbug hotwords

游雁

2024-06-24 1596f6f414f6f41da66506debb1dff19fffeb3ec

 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

@@ -53,6 +54,12 @@
        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)
@@ -65,23 +72,23 @@
        return len(self.index_ds)

    def __getitem__(self, index):
        # import pdb;
        # pdb.set_trace()

        output = None
        for idx in range(self.retry):
            if idx == 0:
                index_cur = index
            else:
                if index <= self.retry:
                    index_cur = index + idx
                else:
                    index_cur = torch.randint(0, index, ()).item()
                index_cur = torch.randint(0, len(self.index_ds), ()).item()

            item = self.index_ds[index_cur]

            source = item["source"]
            data_src = load_audio_text_image_video(source, fs=self.fs)
            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(
@@ -90,7 +97,8 @@

            if speech_lengths > self.batch_size:
                continue
            speech = speech.permute(0, 2, 1)
            if self.permute:
                speech = speech.permute(0, 2, 1)
            target = item["target"]
            if self.preprocessor_text:
                target = self.preprocessor_text(target)
@@ -98,8 +106,14 @@
            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")
            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

            prompt_ids_len = len(prompt_ids) - 1  # [sos, task]
            self.prompt_ids_len = prompt_ids_len

@@ -108,9 +122,12 @@
            if target_ids_len > 200:
                continue

            eos = self.tokenizer.encode(self.eos, allowed_special="all")  # [eos]
            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
            ids = prompt_ids + target_ids + eos  # [sos, task, lid, text, eos]
            ids_lengths = len(ids)

            text = torch.tensor(ids, dtype=torch.int64)
@@ -186,7 +203,7 @@
                )

        if self.batch_type != "example":
            for i in range(3):
            for i in range(10):
                outputs = self._filter_badcase(outputs, i=i)

        return outputs