metadata

language:
  - pa
license: apache-2.0
tags:
  - generated_from_trainer
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_7_0
metrics:
  - wer
base_model: facebook/wav2vec2-xls-r-300m
model-index:
  - name: XLS-R-300M - Punjabi
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          name: Common Voice 7
          type: mozilla-foundation/common_voice_7_0
          args: pa-IN
        metrics:
          - type: wer
            value: 45.611
            name: Test WER
          - type: cer
            value: 15.584
            name: Test CER

XLS-R-300M - Punjabi

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

Loss: 1.2548
Wer: 0.5677

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: 0.0003
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_ratio: 0.12
num_epochs: 120
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.4804	16.65	400	1.8461	1.0
0.474	33.33	800	1.1018	0.6624
0.1389	49.98	1200	1.1918	0.6103
0.0919	66.65	1600	1.1889	0.6058
0.0657	83.33	2000	1.2266	0.5931
0.0479	99.98	2400	1.2512	0.5902
0.0355	116.65	2800	1.2548	0.5677

Framework versions

Transformers 4.15.0
Pytorch 1.10.0+cu111
Datasets 1.18.0
Tokenizers 0.10.3

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_7_0 with split test

python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-pa-in --dataset mozilla-foundation/common_voice_7_0 --config pa-IN --split test

Inference With LM

import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "anuragshas/wav2vec2-large-xls-r-300m-pa-in"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "pa-IN", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
    logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "ਉਨ੍ਹਾਂ ਨੇ ਸਾਰੇ ਤੇਅਰਵੇ ਵੱਖਰੀ ਕਿਸਮ ਦੇ ਕੀਤੇ ਹਨ"

Eval results on Common Voice 7 "test" (WER):

Without LM	With LM (run `./eval.py`)
51.968	45.611