metadata

language:
  - sat
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - sat
  - robust-speech-event
  - model_for_talk
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-300m-sat-final
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: sat
        metrics:
          - name: Test WER
            type: wer
            value: 0.3493975903614458
          - name: Test CER
            type: cer
            value: 0.13773314203730272
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: sat
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

wav2vec2-large-xls-r-300m-sat-final

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SAT dataset. It achieves the following results on the evaluation set:

Loss: 0.8012
Wer: 0.3815

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sat-final --dataset mozilla-foundation/common_voice_8_0 --config sat --split test --log_outputs

To evaluate on speech-recognition-community-v2/dev_data

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sat-final --dataset speech-recognition-community-v2/dev_data --config sat --split validation --chunk_length_s 10 --stride_length_s 1

Note: Santali (Ol Chiki) language not found in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0004
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 170
num_epochs: 200
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
10.6317	33.29	100	2.8629	1.0
2.047	66.57	200	0.9516	0.5703
0.4475	99.86	300	0.8539	0.3896
0.0716	133.29	400	0.8277	0.3454
0.047	166.57	500	0.7597	0.3655
0.0249	199.86	600	0.8012	0.3815

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0