metadata

language:
  - hi
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
  - hi
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
model-index:
  - name: wav2vec2-large-xls-r-300m-hi-CV7
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: 35.31946325249292
          - name: Test CER
            type: cer
            value: 11.310803379493075
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Robust Speech Event - Dev Data
          type: speech-recognition-community-v2/dev_data
          args: vot
        metrics:
          - name: Test WER
            type: wer
            value: NA
          - name: Test CER
            type: cer
            value: NA

wav2vec2-large-xls-r-300m-hi-CV7

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: 0.6588
Wer: 0.2987

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-CV7 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs

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

Training hyperparameters

The following hyperparameters were used during training: #

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: 2000
num_epochs: 60
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
12.809	1.36	200	6.2066	1.0
4.3402	2.72	400	3.5184	1.0
3.4365	4.08	600	3.2779	1.0
1.8643	5.44	800	0.9875	0.6270
0.7504	6.8	1000	0.6382	0.4666
0.5328	8.16	1200	0.6075	0.4505
0.4364	9.52	1400	0.5785	0.4215
0.3777	10.88	1600	0.6279	0.4227
0.3374	12.24	1800	0.6536	0.4192
0.3236	13.6	2000	0.5911	0.4047
0.2877	14.96	2200	0.5955	0.4097
0.2643	16.33	2400	0.5923	0.3744
0.2421	17.68	2600	0.6307	0.3814
0.2218	19.05	2800	0.6036	0.3764
0.2046	20.41	3000	0.6286	0.3797
0.191	21.77	3200	0.6517	0.3889
0.1856	23.13	3400	0.6193	0.3661
0.1721	24.49	3600	0.7034	0.3727
0.1656	25.85	3800	0.6293	0.3591
0.1532	27.21	4000	0.6075	0.3611
0.1507	28.57	4200	0.6313	0.3565
0.1381	29.93	4400	0.6564	0.3578
0.1359	31.29	4600	0.6724	0.3543
0.1248	32.65	4800	0.6789	0.3512
0.1198	34.01	5000	0.6442	0.3539
0.1125	35.37	5200	0.6676	0.3419
0.1036	36.73	5400	0.7017	0.3435
0.0982	38.09	5600	0.6828	0.3319
0.0971	39.45	5800	0.6112	0.3351
0.0968	40.81	6000	0.6424	0.3252
0.0893	42.18	6200	0.6707	0.3304
0.0878	43.54	6400	0.6432	0.3236
0.0827	44.89	6600	0.6696	0.3240
0.0788	46.26	6800	0.6564	0.3180
0.0753	47.62	7000	0.6574	0.3130
0.0674	48.98	7200	0.6698	0.3175
0.0676	50.34	7400	0.6441	0.3142
0.0626	51.7	7600	0.6642	0.3121
0.0617	53.06	7800	0.6615	0.3117
0.0599	54.42	8000	0.6634	0.3059
0.0538	55.78	8200	0.6464	0.3033
0.0571	57.14	8400	0.6503	0.3018
0.0491	58.5	8600	0.6625	0.3025
0.0511	59.86	8800	0.6588	0.2987

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0