Kyrgyz ASR: Fine-Tuning Wav2Vec2

Hi everyone,

Opening this thread to discuss about or collaborate on fine-tuning XLSR-Wav2Vec2 for Kyrgyz.

Results of the first run:
{
“epoch”: 30.0,
“eval_loss”: 0.4920000433921814,
“eval_mem_cpu_alloc_delta”: 118951837,
“eval_mem_cpu_peaked_delta”: 44984344,
“eval_mem_gpu_alloc_delta”: 0,
“eval_mem_gpu_peaked_delta”: 6326542848,
“eval_runtime”: 145.0179,
“eval_samples”: 1503,
“eval_samples_per_second”: 10.364,
“eval_wer”: 0.5470570952893938,
“init_mem_cpu_alloc_delta”: 10150057,
“init_mem_cpu_peaked_delta”: 18306,
“init_mem_gpu_alloc_delta”: 1261919232,
“init_mem_gpu_peaked_delta”: 0,
“train_mem_cpu_alloc_delta”: 2815161,
“train_mem_cpu_peaked_delta”: 300144618,
“train_mem_gpu_alloc_delta”: 3787529728,
“train_mem_gpu_peaked_delta”: 14746535424,
“train_runtime”: 13357.7567,
“train_samples”: 3466,
“train_samples_per_second”: 0.245
}

I forgot to remove " character in the preprocessing stage. I’ll try to remove it and run it again.
Hyperparams were the same as in Turkish.

My attempts are failing except the very first one.

The first run went seemingly well, but I ended up with an empty file :frowning:

I’ll try to remove the object storage instance and start all over again.

P.S. The last run’s WER score was about ~0.45 with the following params:

#!/usr/bin/env bash
python run_common_voice.py
–model_name_or_path=“facebook/wav2vec2-large-xlsr-53”
–dataset_config_name=“ky”
–output_dir=/workspace/output_models/ky6/wav2vec2-large-xlsr-kyrgyz-demo
–cache_dir=/workspace/data/ky
–overwrite_output_dir
–num_train_epochs=“15”
–per_device_train_batch_size=“16”
–per_device_eval_batch_size=“16”
–evaluation_strategy=“steps”
–learning_rate=“2.34e-4”
–warmup_steps=“500”
–fp16
–freeze_feature_extractor
–save_steps=“1000”
–eval_steps=“1000”
–save_total_limit=“2”
–dataloader_num_workers=“8”
–logging_steps=“4000”
–group_by_length
–feat_proj_dropout=“0.0”
–attention_dropout=“0.094”
–activation_dropout=“0.055”
–hidden_dropout=“0.047”
–feat_proj_dropout=“0.04”
–mask_time_prob=“0.0082”
–layerdrop=“0.041”
–gradient_checkpointing
–do_train --do_eval

This is the code I ran in OVH:

The params given above were applied in finetune.sh

Cleaning up Object storage really worked! Results are like this:

wandb: Waiting for W&B process to finish, PID 237
wandb: Program ended successfully.
wandb:
wandb: Find user logs for this run at: /workspace/wav2vec/wandb/run-20210329_070950-1smhj63f/logs/debug.log
wandb: Find internal logs for this run at: /workspace/wav2vec/wandb/run-20210329_070950-1smhj63f/logs/debug-internal.log
wandb: Run summary:
wandb: eval/loss 0.47827
wandb: eval/wer 0.48556
wandb: eval/runtime 41.7707
wandb: eval/samples_per_second 35.982
wandb: train/epoch 15.0
wandb: train/global_step 3255
wandb: _step 4
wandb: _runtime 3367
wandb: _timestamp 1617005157
wandb: train/train_runtime 3461.6067
wandb: train/train_samples_per_second 0.94
wandb: train/total_flos 7.8449973088273e+18
wandb: Run history:
wandb: eval/loss █▁▁▁
wandb: eval/wer █▃▁▁
wandb: eval/runtime █▇▁▂
wandb: eval/samples_per_second ▁▂█▇
wandb: train/epoch ▁▄▇██

F*ck! The job stopped for some reason and files weren’t saved.