metadata

language:
  - hi
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
  - hi
  - robust-speech-event
  - model_for_talk
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-large-xls-r-300m-hi-cv8
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8
          type: mozilla-foundation/common_voice_8_0
          args: hi
        metrics:
          - name: Test WER
            type: wer
            value: 0.3628727037755008
          - name: Test CER
            type: cer
            value: 0.11933724247521164
      - 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

wav2vec2-large-xls-r-300m-hi-cv8

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set:

Loss: 0.6510
Wer: 0.3179

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-cv8 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs

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

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset speech-recognition-community-v2/dev_data --config hi --split validation --chunk_length_s 10 --stride_length_s 1

Note: Hindi language not found in 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: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
12.5576	1.04	200	6.6594	1.0
4.4069	2.07	400	3.6011	1.0
3.4273	3.11	600	3.3370	1.0
2.1108	4.15	800	1.0641	0.6562
0.8817	5.18	1000	0.7178	0.5172
0.6508	6.22	1200	0.6612	0.4839
0.5524	7.25	1400	0.6458	0.4889
0.4992	8.29	1600	0.5791	0.4382
0.4669	9.33	1800	0.6039	0.4352
0.4441	10.36	2000	0.6276	0.4297
0.4172	11.4	2200	0.6183	0.4474
0.3872	12.44	2400	0.5886	0.4231
0.3692	13.47	2600	0.6448	0.4399
0.3385	14.51	2800	0.6344	0.4075
0.3246	15.54	3000	0.5896	0.4087
0.3026	16.58	3200	0.6158	0.4016
0.284	17.62	3400	0.6038	0.3906
0.2682	18.65	3600	0.6165	0.3900
0.2577	19.69	3800	0.5754	0.3805
0.2509	20.73	4000	0.6028	0.3925
0.2426	21.76	4200	0.6335	0.4138
0.2346	22.8	4400	0.6128	0.3870
0.2205	23.83	4600	0.6223	0.3831
0.2104	24.87	4800	0.6122	0.3781
0.1992	25.91	5000	0.6467	0.3792
0.1916	26.94	5200	0.6277	0.3636
0.1835	27.98	5400	0.6317	0.3773
0.1776	29.02	5600	0.6124	0.3614
0.1751	30.05	5800	0.6475	0.3628
0.1662	31.09	6000	0.6266	0.3504
0.1584	32.12	6200	0.6347	0.3532
0.1494	33.16	6400	0.6636	0.3491
0.1457	34.2	6600	0.6334	0.3507
0.1427	35.23	6800	0.6397	0.3442
0.1397	36.27	7000	0.6468	0.3496
0.1283	37.31	7200	0.6291	0.3416
0.1255	38.34	7400	0.6652	0.3461
0.1195	39.38	7600	0.6587	0.3342
0.1169	40.41	7800	0.6478	0.3319
0.1126	41.45	8000	0.6280	0.3291
0.1112	42.49	8200	0.6434	0.3290
0.1069	43.52	8400	0.6542	0.3268
0.1027	44.56	8600	0.6536	0.3239
0.0993	45.6	8800	0.6622	0.3257
0.0973	46.63	9000	0.6572	0.3192
0.0911	47.67	9200	0.6522	0.3175
0.0897	48.7	9400	0.6521	0.3200
0.0905	49.74	9600	0.6510	0.3179

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0