README.md

metadata

language:
  - es
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: xls-r-spanish-test
    results:
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_7_0 es
          type: mozilla-foundation/common_voice_7_0
          args: es
        metrics:
          - name: Test WER
            type: wer
            value: 13.89
      - task:
          name: Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_7_0 es
          type: mozilla-foundation/common_voice_7_0
          args: es
        metrics:
          - name: Test CER
            type: wer
            value: 3.85

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ES dataset. It achieves the following results on the evaluation set:

• Loss: 0.1461
• Wer: 1.0063

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 7.5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 2000
• num_epochs: 5.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.953	0.15	1000	2.9528	1.0
1.1519	0.3	2000	0.3735	1.0357
1.0278	0.45	3000	0.2529	1.0390
0.9922	0.61	4000	0.2208	1.0270
0.9618	0.76	5000	0.2088	1.0294
0.9364	0.91	6000	0.2019	1.0214
0.9179	1.06	7000	0.1940	1.0294
0.9154	1.21	8000	0.1915	1.0290
0.8985	1.36	9000	0.1837	1.0211
0.9055	1.51	10000	0.1838	1.0273
0.8861	1.67	11000	0.1765	1.0139
0.892	1.82	12000	0.1723	1.0188
0.8778	1.97	13000	0.1735	1.0092
0.8645	2.12	14000	0.1707	1.0106
0.8595	2.27	15000	0.1713	1.0186
0.8392	2.42	16000	0.1686	1.0053
0.8436	2.57	17000	0.1653	1.0096
0.8405	2.73	18000	0.1689	1.0077
0.8382	2.88	19000	0.1645	1.0114
0.8247	3.03	20000	0.1647	1.0078
0.8219	3.18	21000	0.1611	1.0026
0.8024	3.33	22000	0.1580	1.0062
0.8087	3.48	23000	0.1578	1.0038
0.8097	3.63	24000	0.1556	1.0057
0.8094	3.79	25000	0.1552	1.0035
0.7836	3.94	26000	0.1516	1.0052
0.8042	4.09	27000	0.1515	1.0054
0.7925	4.24	28000	0.1499	1.0031
0.7855	4.39	29000	0.1490	1.0041
0.7814	4.54	30000	0.1482	1.0068
0.7859	4.69	31000	0.1460	1.0066
0.7819	4.85	32000	0.1464	1.0062
0.7784	5.0	33000	0.1460	1.0063

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0