|
--- |
|
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 |
|
--- |
|
|
|
<!-- 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-Tatar |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/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 |
|
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` |
|
|
|
```bash |
|
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 |
|
|
|
```python |
|
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 |
|
|
