|
---
|
|
language:
|
|
- sk
|
|
license: apache-2.0
|
|
tags:
|
|
- automatic-speech-recognition
|
|
- mozilla-foundation/common_voice_8_0
|
|
- robust-speech-event
|
|
- xlsr-fine-tuning-week
|
|
datasets:
|
|
- common_voice
|
|
model-index:
|
|
- name: Slovak comodoro Wav2Vec2 XLSR 300M CV8
|
|
results:
|
|
- task:
|
|
name: Automatic Speech Recognition
|
|
type: automatic-speech-recognition
|
|
dataset:
|
|
name: Common Voice 8
|
|
type: mozilla-foundation/common_voice_8_0
|
|
args: sk
|
|
metrics:
|
|
- name: Test WER
|
|
type: wer
|
|
value: 59.5
|
|
- name: Test CER
|
|
type: cer
|
|
value: 15.6
|
|
---
|
|
<!-- 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-xls-r-300m-cs-cv8
|
|
|
|
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 8.0 dataset.
|
|
It achieves the following results on the evaluation set:
|
|
- Wer: 55.2
|
|
- Cer: 14.4
|
|
|
|
## Usage
|
|
|
|
The model can be used directly (without a language model) as follows:
|
|
|
|
```python
|
|
import torch
|
|
import torchaudio
|
|
from datasets import load_dataset
|
|
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
|
|
|
|
test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "sk", split="test[:2%]")
|
|
|
|
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
|
|
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-sk-cv8")
|
|
|
|
resampler = torchaudio.transforms.Resample(48_000, 16_000)
|
|
|
|
# Preprocessing the datasets.
|
|
# We need to read the aduio files as arrays
|
|
def speech_file_to_array_fn(batch):
|
|
speech_array, sampling_rate = torchaudio.load(batch["path"])
|
|
batch["speech"] = resampler(speech_array).squeeze().numpy()
|
|
return batch
|
|
|
|
test_dataset = test_dataset.map(speech_file_to_array_fn)
|
|
inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
|
|
|
|
with torch.no_grad():
|
|
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
|
|
|
|
predicted_ids = torch.argmax(logits, dim=-1)
|
|
|
|
print("Prediction:", processor.batch_decode(predicted_ids))
|
|
print("Reference:", test_dataset[:2]["sentence"])
|
|
```
|
|
|
|
## Evaluation
|
|
|
|
The model can be evaluated using the attached `eval.py` script:
|
|
```
|
|
python eval.py --model_id comodoro/wav2vec2-xls-r-300m-sk-cv8 --dataset mozilla-foundation/common_voice_8_0 --split test --config sk
|
|
```
|
|
|
|
## Training and evaluation data
|
|
|
|
The Common Voice 8.0 `train` and `validation` datasets were used for training
|
|
|
|
## Training procedure
|
|
|
|
### Training hyperparameters
|
|
|
|
The following hyperparameters were used during training:
|
|
|
|
- learning_rate: 7e-4
|
|
- train_batch_size: 32
|
|
- eval_batch_size: 8
|
|
- seed: 42
|
|
- gradient_accumulation_steps: 20
|
|
- total_train_batch_size: 640
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
|
- lr_scheduler_type: linear
|
|
- lr_scheduler_warmup_steps: 500
|
|
- num_epochs: 50
|
|
- mixed_precision_training: Native AMP
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.16.0.dev0
|
|
- Pytorch 1.10.1+cu102
|
|
- Datasets 1.17.1.dev0
|
|
- Tokenizers 0.11.0
|
|
|
