Czech wav2vec2-xls-r-300m-cs-250
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice 8.0 dataset as well as other datasets listed below.
It achieves the following results on the evaluation set:
- Loss: 0.1271
- Wer: 0.1475
- Cer: 0.0329
The eval.py
script results using a LM are:
- WER: 0.07274312090176113
- CER: 0.021207369275558875
Model description
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Czech using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.
The model can be used directly (without a language model) as follows:
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "cs", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-250")
model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-250")
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-cs-250 --dataset mozilla-foundation/common-voice_8_0 --split test --config cs
Training and evaluation data
The Common Voice 8.0 train
and validation
datasets were used for training, as well as the following datasets:
Šmídl, Luboš and Pražák, Aleš, 2013, OVM – Otázky Václava Moravce, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11858/00-097C-0000-000D-EC98-3.
Pražák, Aleš and Šmídl, Luboš, 2012, Czech Parliament Meetings, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11858/00-097C-0000-0005-CF9C-4.
Plátek, Ondřej; Dušek, Ondřej and Jurčíček, Filip, 2016, Vystadial 2016 – Czech data, LINDAT/CLARIAH-CZ digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University, http://hdl.handle.net/11234/1-1740.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 800
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
---|---|---|---|---|---|
3.4203 | 0.16 | 800 | 3.3148 | 1.0 | 1.0 |
2.8151 | 0.32 | 1600 | 0.8508 | 0.8938 | 0.2345 |
0.9411 | 0.48 | 2400 | 0.3335 | 0.3723 | 0.0847 |
0.7408 | 0.64 | 3200 | 0.2573 | 0.2840 | 0.0642 |
0.6516 | 0.8 | 4000 | 0.2365 | 0.2581 | 0.0595 |
0.6242 | 0.96 | 4800 | 0.2039 | 0.2433 | 0.0541 |
0.5754 | 1.12 | 5600 | 0.1832 | 0.2156 | 0.0482 |
0.5626 | 1.28 | 6400 | 0.1827 | 0.2091 | 0.0463 |
0.5342 | 1.44 | 7200 | 0.1744 | 0.2033 | 0.0468 |
0.4965 | 1.6 | 8000 | 0.1705 | 0.1963 | 0.0444 |
0.5047 | 1.76 | 8800 | 0.1604 | 0.1889 | 0.0422 |
0.4814 | 1.92 | 9600 | 0.1604 | 0.1827 | 0.0411 |
0.4471 | 2.09 | 10400 | 0.1566 | 0.1822 | 0.0406 |
0.4509 | 2.25 | 11200 | 0.1619 | 0.1853 | 0.0432 |
0.4415 | 2.41 | 12000 | 0.1513 | 0.1764 | 0.0397 |
0.4313 | 2.57 | 12800 | 0.1515 | 0.1739 | 0.0392 |
0.4163 | 2.73 | 13600 | 0.1445 | 0.1695 | 0.0377 |
0.4142 | 2.89 | 14400 | 0.1478 | 0.1699 | 0.0385 |
0.4184 | 3.05 | 15200 | 0.1430 | 0.1669 | 0.0376 |
0.3886 | 3.21 | 16000 | 0.1433 | 0.1644 | 0.0374 |
0.3795 | 3.37 | 16800 | 0.1426 | 0.1648 | 0.0373 |
0.3859 | 3.53 | 17600 | 0.1357 | 0.1604 | 0.0361 |
0.3762 | 3.69 | 18400 | 0.1344 | 0.1558 | 0.0349 |
0.384 | 3.85 | 19200 | 0.1379 | 0.1576 | 0.0359 |
0.3762 | 4.01 | 20000 | 0.1344 | 0.1539 | 0.0346 |
0.3559 | 4.17 | 20800 | 0.1339 | 0.1525 | 0.0351 |
0.3683 | 4.33 | 21600 | 0.1315 | 0.1518 | 0.0342 |
0.3572 | 4.49 | 22400 | 0.1307 | 0.1507 | 0.0342 |
0.3494 | 4.65 | 23200 | 0.1294 | 0.1491 | 0.0335 |
0.3476 | 4.81 | 24000 | 0.1287 | 0.1491 | 0.0336 |
0.3475 | 4.97 | 24800 | 0.1271 | 0.1475 | 0.0329 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.1+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
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Base model
facebook/wav2vec2-xls-r-300mDataset used to train comodoro/wav2vec2-xls-r-300m-cs-250
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Evaluation results
- Test WER on Common Voice 8self-reported7.300
- Test CER on Common Voice 8self-reported2.100
- Test WER on Robust Speech Event - Dev Dataself-reported43.440
- Test WER on Robust Speech Event - Test Dataself-reported38.500