README.md · Plim/xls-r-1b-cv_8-fr at a010ddae94e0ee8585b358de0b72bce883983cad

metadata

language:
  - fr
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - robust-speech-event
model-index:
  - name: XLS-R-1B - French
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: fr
        metrics:
          - name: Test WER
            type: wer
            value: 18.33
          - name: Test CER
            type: cer
            value: 5.6
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: fr
        metrics:
          - name: Test WER
            type: wer
            value: 60.25
          - name: Test CER
            type: cer
            value: 15.68

Model description

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7.5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2000
num_epochs: 6.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.9827	0.29	1000	inf	0.2937
1.0203	0.57	2000	inf	0.2711
1.0048	0.86	3000	inf	0.2620
0.9858	1.15	4000	inf	0.2522
0.9709	1.43	5000	inf	0.2365
0.9347	1.72	6000	inf	0.2332
0.9256	2.01	7000	inf	0.2261
0.8936	2.29	8000	inf	0.2203
0.877	2.58	9000	inf	0.2096
0.8393	2.87	10000	inf	0.2017
0.8156	3.15	11000	inf	0.1936
0.8015	3.44	12000	inf	0.1880
0.774	3.73	13000	inf	0.1834
0.8372	4.01	14000	inf	0.1934
0.8075	4.3	15000	inf	0.1923
0.8069	4.59	16000	inf	0.1877
0.8064	4.87	17000	inf	0.1955
0.801	5.16	18000	inf	0.1891
0.8022	5.45	19000	inf	0.1895
0.792	5.73	20000	inf	0.1854

It achieves the best result on the validation set on STEP 13000:

Wer: 0.1834

Some problem occurs when calculating the validation loss.

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.3.dev0
Tokenizers 0.11.0

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8 with split test

python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset mozilla-foundation/common_voice_8_0 --config fr --split test

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

python eval.py --model_id Plim/xls-r-1b-cv_8-fr --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0