metadata

language:
  - tt
license: apache-2.0
tags:
  - automatic-speech-recognition
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
  - cer
base_model: facebook/wav2vec2-xls-r-300m
model-index:
  - name: wav2vec2-large-xls-r-300m-Tatar
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          name: Common Voice tt
          type: mozilla-foundation/common_voice_8_0
          args: tt
        metrics:
          - type: wer
            value: 42.71
            name: Test WER With LM
          - type: cer
            value: 11.18
            name: Test CER With LM

wav2vec2-large-xls-r-300m-Tatar

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: 0.5068
Wer: 0.4263
Cer: 0.1117

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with split test

python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-300m-Tatar --dataset mozilla-foundation/common_voice_8_0 --config tt --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-xls-r-300m-Tatar"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "tt", 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: 7.5e-05
train_batch_size: 64
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
8.4116	12.19	500	3.4118	1.0	1.0
2.5829	24.39	1000	0.7150	0.6151	0.1582
0.4492	36.58	1500	0.5378	0.4577	0.1210
0.3007	48.77	2000	0.5068	0.4263	0.1117

Framework versions

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