Use ESPnet as a library, the acc doesn't improve.

Describe the bug Hi, thanks for the awesome work!
However, when I tried to implement the Commonvoice ASR by using ESPnet as a library, I met some problems.

The problem was that I used the transformer and RNN model to train the ASR by following the config in the Commonvoice egs.

But during the training process, the loss was decreasing while the training and dev acc was not increasing.

Basic environments:

• OS information: ubuntu 18.04
• python version: 3.7.10
• ESPnet version: 0.9.9
• Pytorch version: 1.4.0

Task information:

• Task: ASR
• Recipe: Commonvoice_zh_TW
• ESPnet1

Code: I followed the code in the espnet/notebook/asr_library.ipynb and modify some of them. The following is the code I implemented:

import json
import matplotlib.pyplot as plt
import kaldiio
import argparse
from espnet.bin.asr_train import get_parser
from espnet.nets.pytorch_backend.e2e_asr import E2E

with open("./dump/train_zh_TW/deltafalse/data_unigram2500.json", "r") as f:
    train_json = json.load(f)["utts"]
with open("./dump/dev_zh_TW/deltafalse/data_unigram2500.json", "r") as f:
    dev_json = json.load(f)["utts"]

parser = get_parser()
parser = E2E.add_arguments(parser)
config = parser.parse_args([
    "--config","conf/tuning/train_rnn.yaml",
    "--preprocess-conf","conf/specaug.yaml",
    "--ngpu","1",
    "--backend","pytorch",
    "--outdir","exp/python_library/results",
    "--tensorboard-dir", "tensorboard2/python_library",
    "--debugmode","1",
    "--dict", "data/zh-TW_lang_char/train_zh_TW_unigram2500_units.txt",
    "--debugdir","exp/python_library/",
    "--minibatches","0",
    "--verbose","0",
    "--resume","",
    "--train-json","dump/train_zh_TW/deltafalse/data_unigram2500.json",
    "--valid-json","dump/dev_zh_TW/deltafalse/data_unigram2500.json",
])

from espnet.utils.training.batchfy import make_batchset
use_sortagrad = config.sortagrad == -1 or config.sortagrad > 0
batch_size = config.batch_size
trainset = make_batchset(train_json, 
                         batch_size, 
                         config.maxlen_in, 
                         config.maxlen_out, 
                         config.minibatches,
                         min_batch_size=config.ngpu if config.ngpu > 1 else 1,
                         shortest_first=use_sortagrad,
                         count=config.batch_count,
                         batch_bins=config.batch_bins,
                         batch_frames_in=config.batch_frames_in,
                         batch_frames_out=config.batch_frames_out,
                         batch_frames_inout=config.batch_frames_inout,
                         iaxis=0,
                         oaxis=0,                
                        )
devset = make_batchset(dev_json, 
                       batch_size,
                       config.maxlen_in,
                       config.maxlen_out,
                       config.minibatches,
                       min_batch_size=config.ngpu if config.ngpu > 1 else 1,
                       count=config.batch_count,
                       batch_bins=config.batch_bins,
                       batch_frames_in=config.batch_frames_in,
                       batch_frames_out=config.batch_frames_out,
                       batch_frames_inout=config.batch_frames_inout,
                       iaxis=0,
                       oaxis=0)

with open("./exp/train_zh_TW_pytorch_train_transformer_specaug/results/model.json", "r") as f:
    char_list_json = json.load(f)
idim = info["input"][0]["shape"][1]
odim = info["output"][0]["shape"][1]
setattr(config, "char_list", char_list_json[2]['char_list'])
model = E2E(idim, odim, config)

import numpy
import torch
from torch.nn.utils.rnn import pad_sequence
from torch.nn.utils.clip_grad import clip_grad_norm_
from torch.utils.data import DataLoader
from espnet.nets.pytorch_backend.transformer.optimizer import get_std_opt

def collate(minibatch):
    fbanks = []
    tokens = []
    for key, info in minibatch[0]:
        fbanks.append(torch.tensor(kaldiio.load_mat(info["input"][0]["feat"])))
        tokens.append(torch.tensor([int(s) for s in info["output"][0]["tokenid"].split()]))
    ilens = torch.tensor([x.shape[0] for x in fbanks])
    return pad_sequence(fbanks, batch_first=True), ilens, pad_sequence(tokens, batch_first=True)

train_loader = DataLoader(trainset, collate_fn=collate, shuffle=True, pin_memory=True)
dev_loader = DataLoader(devset, collate_fn=collate, pin_memory=True)
model.cuda()
model_params = model.parameters()
optim = torch.optim.Adadelta(
            model_params, rho=0.95, eps=config.eps, weight_decay=config.weight_decay
        )

n_iter = len(trainset)
n_epoch = 15
total_iter = n_iter * n_epoch
train_acc = []
valid_acc = []

data = next(iter(train_loader))

for epoch in range(n_epoch):
    # training
    acc = []
    model.train()
    for data in train_loader:
        loss = model(*[d.cuda() for d in data])
        optim.zero_grad()
        loss.backward()
        acc.append(model.acc)
        norm = clip_grad_norm_(model.parameters(), 5.0)
        optim.step()
        t_r = loss.item()
    train_acc.append(numpy.mean(acc))
    # validation
    acc = []
    model.eval()
    for data in dev_loader:
        model(*[d.cuda() for d in data])
        acc.append(model.acc)
    valid_acc.append(numpy.mean(acc))
    print(f"epoch: {epoch}, train acc: {train_acc[-1]:.3f}, dev acc: {valid_acc[-1]:.3f}, loss:{t_r:.3f}")

And the training result is following:

epoch: 0, train acc: 0.352, dev acc: 0.373, loss:297.107
epoch: 1, train acc: 0.352, dev acc: 0.373, loss:362.381
epoch: 2, train acc: 0.352, dev acc: 0.373, loss:569.445
epoch: 3, train acc: 0.352, dev acc: 0.373, loss:606.235
epoch: 4, train acc: 0.352, dev acc: 0.373, loss:476.195
epoch: 5, train acc: 0.352, dev acc: 0.373, loss:278.052
epoch: 6, train acc: 0.352, dev acc: 0.373, loss:590.790
epoch: 7, train acc: 0.352, dev acc: 0.373, loss:375.243
epoch: 8, train acc: 0.352, dev acc: 0.373, loss:610.624
epoch: 9, train acc: 0.352, dev acc: 0.373, loss:444.297
epoch: 10, train acc: 0.352, dev acc: 0.373, loss:443.770
epoch: 11, train acc: 0.352, dev acc: 0.373, loss:548.410
epoch: 12, train acc: 0.352, dev acc: 0.373, loss:820.162
epoch: 13, train acc: 0.352, dev acc: 0.373, loss:295.590
epoch: 14, train acc: 0.352, dev acc: 0.373, loss:544.829

I don’t know why the train acc and dev acc don’t improve.

Thank you for helping me!

[sw005320]

worked fine without speed perturbation

I see. Thanks for the clarification. This is very strange. I’ll discuss it.

Given that the speed perturbation issue is fixed, I think your way is very reasonable. We’ll try to help you as much as possible.

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