fixbug hotwords

游雁

2024-06-24 1596f6f414f6f41da66506debb1dff19fffeb3ec

 funasr/datasets/sense_voice_datasets/datasets.py

@@ -1,5 +1,6 @@
import logging

import re
import torch
import random
import traceback
@@ -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)
@@ -65,8 +72,6 @@
        return len(self.index_ds)

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

        output = None
        for idx in range(self.retry):
@@ -92,7 +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)
@@ -100,8 +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

@@ -110,9 +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)