README.md

metadata

language:
  - pt
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - hf-asr-leaderboard
  - mozilla-foundation/common_voice_7_0
  - pt
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: wav2vec2_base_10k_8khz_pt_cv7_2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 36.9
          - name: Test CER
            type: cer
            value: 14.82
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: sv
        metrics:
          - name: Test WER
            type: wer
            value: 40.53
          - name: Test CER
            type: cer
            value: 16.95
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 37.15
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Test Data
          type: speech-recognition-community-v2/eval_data
          args: pt
        metrics:
          - name: Test WER
            type: wer
            value: 38.95

wav2vec2_base_10k_8khz_pt_cv7_2

This model is a fine-tuned version of lgris/seasr_2022_base_10k_8khz_pt on the common_voice dataset. It achieves the following results on the evaluation set:

• Loss: 76.3426
• Wer: 0.1979

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: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 100
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
189.1362	0.65	500	80.6347	0.2139
174.2587	1.3	1000	80.2062	0.2116
164.676	1.95	1500	78.2161	0.2073
176.5856	2.6	2000	78.8920	0.2074
164.3583	3.25	2500	77.2865	0.2066
161.414	3.9	3000	77.8888	0.2048
158.283	4.55	3500	77.3472	0.2033
159.2265	5.19	4000	79.0953	0.2036
156.3967	5.84	4500	76.6855	0.2029
154.2743	6.49	5000	77.7785	0.2015
156.6497	7.14	5500	77.1220	0.2033
157.3038	7.79	6000	76.2926	0.2027
162.8151	8.44	6500	76.7602	0.2013
151.8613	9.09	7000	77.4777	0.2011
153.0225	9.74	7500	76.5206	0.2001
157.52	10.39	8000	76.1061	0.2006
145.0592	11.04	8500	76.7855	0.1992
150.0066	11.69	9000	76.0058	0.1988
146.8128	12.34	9500	76.2853	0.1987
146.9148	12.99	10000	76.3426	0.1979

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0