aishell example

游雁

2024-02-19 4ebde3c4ac27c15ff39ffbd5aa601035d189497a

 funasr/bin/train.py

@@ -1,3 +1,6 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-

import os
import sys
import torch
@@ -144,9 +147,8 @@

    # dataset
    dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
    dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf"))
    dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer,
                               **kwargs.get("dataset_conf"))
    dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=True, **kwargs.get("dataset_conf"))
    dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=False, **kwargs.get("dataset_conf"))

    # dataloader
    batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
@@ -154,7 +156,7 @@
    if batch_sampler is not None:
        batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
        batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf"))
        batch_sampler_val = batch_sampler_class(dataset_tr, is_training=False, **kwargs.get("dataset_conf"))
        batch_sampler_val = batch_sampler_class(dataset_val, is_training=False, **kwargs.get("dataset_conf"))
    dataloader_tr = torch.utils.data.DataLoader(dataset_tr,
                                                collate_fn=dataset_tr.collator,
                                                batch_sampler=batch_sampler,