metadata

language:
  - hi
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - hi
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: wav2vec2-large-xls-r-300m-hi-d3
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: vot
        metrics:
          - name: Test WER
            type: wer
            value: 0.4204111781361566
          - name: Test CER
            type: cer
            value: 0.13869169624556316
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7.0
          type: mozilla-foundation/common_voice_7_0
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: 42.04

wav2vec2-large-xls-r-300m-hi-d3

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set:

Loss: 0.7988
Wer: 0.3713

###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-hi-d3 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs

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

Hindi language isn't available in speech-recognition-community-v2/dev_data

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.000388
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: 750
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
8.2826	1.36	200	3.5253	1.0
2.7019	2.72	400	1.1744	0.7360
0.7358	4.08	600	0.7781	0.5501
0.4942	5.44	800	0.7590	0.5345
0.4056	6.8	1000	0.6885	0.4776
0.3243	8.16	1200	0.7195	0.4861
0.2785	9.52	1400	0.7473	0.4930
0.2448	10.88	1600	0.7201	0.4574
0.2155	12.24	1800	0.7686	0.4648
0.2039	13.6	2000	0.7440	0.4624
0.1792	14.96	2200	0.7815	0.4658
0.1695	16.33	2400	0.7678	0.4557
0.1598	17.68	2600	0.7468	0.4393
0.1568	19.05	2800	0.7440	0.4422
0.1391	20.41	3000	0.7656	0.4317
0.1283	21.77	3200	0.7892	0.4299
0.1194	23.13	3400	0.7646	0.4192
0.1116	24.49	3600	0.8156	0.4330
0.1111	25.85	3800	0.7661	0.4322
0.1023	27.21	4000	0.7419	0.4276
0.1007	28.57	4200	0.8488	0.4245
0.0925	29.93	4400	0.8062	0.4070
0.0918	31.29	4600	0.8412	0.4218
0.0813	32.65	4800	0.8045	0.4087
0.0805	34.01	5000	0.8411	0.4113
0.0774	35.37	5200	0.7664	0.3943
0.0666	36.73	5400	0.8082	0.3939
0.0655	38.09	5600	0.7948	0.4000
0.0617	39.45	5800	0.8084	0.3932
0.0606	40.81	6000	0.8223	0.3841
0.0569	42.18	6200	0.7892	0.3832
0.0544	43.54	6400	0.8326	0.3834
0.0508	44.89	6600	0.7952	0.3774
0.0492	46.26	6800	0.7923	0.3756
0.0459	47.62	7000	0.7925	0.3701
0.0423	48.98	7200	0.7988	0.3713

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0