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---
language:
- myv
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- myv
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: wav2vec2-large-xls-r-300m-myv-v1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: myv
metrics:
- name: Test WER
type: wer
value: 0.599548532731377
- name: Test CER
type: cer
value: 0.12953851902597
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: myv
metrics:
- name: Test WER
type: wer
value: NA
- name: Test CER
type: cer
value: NA
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-myv-v1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MYV dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8537
- Wer: 0.6160
### Evaluation Commands
1. To evaluate on mozilla-foundation/common_voice_8_0 with test split
python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1 --dataset mozilla-foundation/common_voice_8_0 --config myv --split test --log_outputs
2. To evaluate on speech-recognition-community-v2/dev_data
Erzya language not found in speech-recognition-community-v2/dev_data!
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000222
- 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: 1000
- num_epochs: 150
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 19.453 | 1.92 | 50 | 16.4001 | 1.0 |
| 9.6875 | 3.85 | 100 | 5.4468 | 1.0 |
| 4.9988 | 5.77 | 150 | 4.3507 | 1.0 |
| 4.1148 | 7.69 | 200 | 3.6753 | 1.0 |
| 3.4922 | 9.62 | 250 | 3.3103 | 1.0 |
| 3.2443 | 11.54 | 300 | 3.1741 | 1.0 |
| 3.164 | 13.46 | 350 | 3.1346 | 1.0 |
| 3.0954 | 15.38 | 400 | 3.0428 | 1.0 |
| 3.0076 | 17.31 | 450 | 2.9137 | 1.0 |
| 2.6883 | 19.23 | 500 | 2.1476 | 0.9978 |
| 1.5124 | 21.15 | 550 | 0.8955 | 0.8225 |
| 0.8711 | 23.08 | 600 | 0.6948 | 0.7591 |
| 0.6695 | 25.0 | 650 | 0.6683 | 0.7636 |
| 0.5606 | 26.92 | 700 | 0.6821 | 0.7435 |
| 0.503 | 28.85 | 750 | 0.7220 | 0.7516 |
| 0.4528 | 30.77 | 800 | 0.6638 | 0.7324 |
| 0.4219 | 32.69 | 850 | 0.7120 | 0.7435 |
| 0.4109 | 34.62 | 900 | 0.7122 | 0.7511 |
| 0.3887 | 36.54 | 950 | 0.7179 | 0.7199 |
| 0.3895 | 38.46 | 1000 | 0.7322 | 0.7525 |
| 0.391 | 40.38 | 1050 | 0.6850 | 0.7364 |
| 0.3537 | 42.31 | 1100 | 0.7571 | 0.7279 |
| 0.3267 | 44.23 | 1150 | 0.7575 | 0.7257 |
| 0.3195 | 46.15 | 1200 | 0.7580 | 0.6998 |
| 0.2891 | 48.08 | 1250 | 0.7452 | 0.7101 |
| 0.294 | 50.0 | 1300 | 0.7316 | 0.6945 |
| 0.2854 | 51.92 | 1350 | 0.7241 | 0.6757 |
| 0.2801 | 53.85 | 1400 | 0.7532 | 0.6887 |
| 0.2502 | 55.77 | 1450 | 0.7587 | 0.6811 |
| 0.2427 | 57.69 | 1500 | 0.7231 | 0.6851 |
| 0.2311 | 59.62 | 1550 | 0.7288 | 0.6632 |
| 0.2176 | 61.54 | 1600 | 0.7711 | 0.6664 |
| 0.2117 | 63.46 | 1650 | 0.7914 | 0.6940 |
| 0.2114 | 65.38 | 1700 | 0.8065 | 0.6918 |
| 0.1913 | 67.31 | 1750 | 0.8372 | 0.6945 |
| 0.1897 | 69.23 | 1800 | 0.8051 | 0.6869 |
| 0.1865 | 71.15 | 1850 | 0.8076 | 0.6740 |
| 0.1844 | 73.08 | 1900 | 0.7935 | 0.6708 |
| 0.1757 | 75.0 | 1950 | 0.8015 | 0.6610 |
| 0.1636 | 76.92 | 2000 | 0.7614 | 0.6414 |
| 0.1637 | 78.85 | 2050 | 0.8123 | 0.6592 |
| 0.1599 | 80.77 | 2100 | 0.7907 | 0.6566 |
| 0.1498 | 82.69 | 2150 | 0.8641 | 0.6757 |
| 0.1545 | 84.62 | 2200 | 0.7438 | 0.6682 |
| 0.1433 | 86.54 | 2250 | 0.8014 | 0.6624 |
| 0.1427 | 88.46 | 2300 | 0.7758 | 0.6646 |
| 0.1423 | 90.38 | 2350 | 0.7741 | 0.6423 |
| 0.1298 | 92.31 | 2400 | 0.7938 | 0.6414 |
| 0.1111 | 94.23 | 2450 | 0.7976 | 0.6467 |
| 0.1243 | 96.15 | 2500 | 0.7916 | 0.6481 |
| 0.1215 | 98.08 | 2550 | 0.7594 | 0.6392 |
| 0.113 | 100.0 | 2600 | 0.8236 | 0.6392 |
| 0.1077 | 101.92 | 2650 | 0.7959 | 0.6347 |
| 0.0988 | 103.85 | 2700 | 0.8189 | 0.6392 |
| 0.0953 | 105.77 | 2750 | 0.8157 | 0.6414 |
| 0.0889 | 107.69 | 2800 | 0.7946 | 0.6369 |
| 0.0929 | 109.62 | 2850 | 0.8255 | 0.6360 |
| 0.0822 | 111.54 | 2900 | 0.8320 | 0.6334 |
| 0.086 | 113.46 | 2950 | 0.8539 | 0.6490 |
| 0.0825 | 115.38 | 3000 | 0.8438 | 0.6418 |
| 0.0727 | 117.31 | 3050 | 0.8568 | 0.6481 |
| 0.0717 | 119.23 | 3100 | 0.8447 | 0.6512 |
| 0.0815 | 121.15 | 3150 | 0.8470 | 0.6445 |
| 0.0689 | 123.08 | 3200 | 0.8264 | 0.6249 |
| 0.0726 | 125.0 | 3250 | 0.7981 | 0.6169 |
| 0.0648 | 126.92 | 3300 | 0.8237 | 0.6200 |
| 0.0632 | 128.85 | 3350 | 0.8416 | 0.6249 |
| 0.06 | 130.77 | 3400 | 0.8276 | 0.6173 |
| 0.0616 | 132.69 | 3450 | 0.8429 | 0.6209 |
| 0.0614 | 134.62 | 3500 | 0.8485 | 0.6271 |
| 0.0539 | 136.54 | 3550 | 0.8598 | 0.6218 |
| 0.0555 | 138.46 | 3600 | 0.8557 | 0.6169 |
| 0.0604 | 140.38 | 3650 | 0.8436 | 0.6186 |
| 0.0556 | 142.31 | 3700 | 0.8428 | 0.6178 |
| 0.051 | 144.23 | 3750 | 0.8440 | 0.6142 |
| 0.0526 | 146.15 | 3800 | 0.8566 | 0.6142 |
| 0.052 | 148.08 | 3850 | 0.8544 | 0.6178 |
| 0.0519 | 150.0 | 3900 | 0.8537 | 0.6160 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.2
- Tokenizers 0.11.0