metadata
language:
- hi
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-1b-hi
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_7_0
name: Common Voice 7
args: hi
metrics:
- type: wer
value: 49.972
name: Test WER
- name: Test CER
type: cer
value: 26.39
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set:
- Loss: 0.5878
- Wer: 0.3419
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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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: 100.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
1.9859 | 2.72 | 400 | 1.1663 | 0.7948 |
1.2969 | 5.44 | 800 | 0.7725 | 0.6562 |
1.1954 | 8.16 | 1200 | 0.5940 | 0.4904 |
1.164 | 10.88 | 1600 | 0.5338 | 0.4316 |
1.1464 | 13.6 | 2000 | 0.5432 | 0.4226 |
1.1553 | 16.33 | 2400 | 0.5471 | 0.4260 |
1.0985 | 19.05 | 2800 | 0.5290 | 0.4076 |
1.0421 | 21.77 | 3200 | 0.5672 | 0.4181 |
0.9831 | 24.49 | 3600 | 0.5741 | 0.4141 |
0.9827 | 27.21 | 4000 | 0.5754 | 0.4179 |
0.9669 | 29.93 | 4400 | 0.5310 | 0.3889 |
0.9496 | 32.65 | 4800 | 0.5649 | 0.4062 |
0.9112 | 35.37 | 5200 | 0.5738 | 0.3926 |
0.8838 | 38.1 | 5600 | 0.5232 | 0.3768 |
0.8666 | 40.81 | 6000 | 0.5510 | 0.3852 |
0.8366 | 43.54 | 6400 | 0.5436 | 0.3837 |
0.7957 | 46.26 | 6800 | 0.5337 | 0.3775 |
0.7834 | 48.98 | 7200 | 0.5611 | 0.3844 |
0.7685 | 51.7 | 7600 | 0.5710 | 0.4008 |
0.7431 | 54.42 | 8000 | 0.5636 | 0.3726 |
0.7353 | 57.14 | 8400 | 0.5937 | 0.3836 |
0.7001 | 59.86 | 8800 | 0.5815 | 0.3858 |
0.6799 | 62.58 | 9200 | 0.5862 | 0.3696 |
0.6459 | 65.31 | 9600 | 0.6181 | 0.3762 |
0.6121 | 68.03 | 10000 | 0.5637 | 0.3590 |
0.5942 | 70.75 | 10400 | 0.6374 | 0.3882 |
0.5769 | 73.47 | 10800 | 0.6015 | 0.3640 |
0.5689 | 76.19 | 11200 | 0.5669 | 0.3508 |
0.5461 | 78.91 | 11600 | 0.5967 | 0.3621 |
0.5286 | 81.63 | 12000 | 0.5840 | 0.3605 |
0.5057 | 84.35 | 12400 | 0.5848 | 0.3489 |
0.482 | 87.07 | 12800 | 0.5860 | 0.3488 |
0.4655 | 89.79 | 13200 | 0.5780 | 0.3453 |
0.4523 | 92.52 | 13600 | 0.6150 | 0.3532 |
0.4422 | 95.24 | 14000 | 0.5930 | 0.3452 |
0.4436 | 97.96 | 14400 | 0.5867 | 0.3428 |
Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
Evaluation Commands
- To evaluate on
mozilla-foundation/common_voice_7_0
with splittest
python eval.py --model_id anuragshas/wav2vec2-large-xls-r-300m-or --dataset mozilla-foundation/common_voice_7_0 --config or --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-xls-r-1b-hi"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "hi", 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
# => "तुम्हारे पास तीन महीने बचे हैं"
Eval results on Common Voice 7 "test" (WER):
Without LM | With LM (run ./eval.py ) |
---|---|
60.61 | 49.97 |