metadata

language:
  - pa
license: apache-2.0
tags:
  - automatic-speech-recognition
  - hf-asr-leaderboard
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
  - cer
base_model: Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10
model-index:
  - name: wav2vec2-punjabi-V8-Abid
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          name: Common Voice pa-IN
          type: mozilla-foundation/common_voice_8_0
          args: pa-IN
        metrics:
          - type: wer
            value: 36.02
            name: Test WER With LM
          - type: cer
            value: 12.81
            name: Test CER With LM

wav2vec2-large-xlsr-53-punjabi

This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-punjabi-pam-10 on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 1.2101
Wer: 0.4939
Cer: 0.2238

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id kingabzpro/wav2vec2-large-xlsr-53-punjabi --dataset mozilla-foundation/common_voice_8_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 = "kingabzpro/wav2vec2-large-xlsr-53-punjabi"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_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

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_steps: 200
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
11.0563	3.7	100	1.9492	0.7123	0.3872
1.6715	7.41	200	1.3142	0.6433	0.3086
0.9117	11.11	300	1.2733	0.5657	0.2627
0.666	14.81	400	1.2730	0.5598	0.2534
0.4225	18.52	500	1.2548	0.5300	0.2399
0.3209	22.22	600	1.2166	0.5229	0.2372
0.2678	25.93	700	1.1795	0.5041	0.2276
0.2088	29.63	800	1.2101	0.4939	0.2238

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0