185 lines
7.0 KiB
Markdown
185 lines
7.0 KiB
Markdown
---
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language:
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- cs
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license: apache-2.0
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tags:
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- automatic-speech-recognition
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- generated_from_trainer
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- hf-asr-leaderboard
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- mozilla-foundation/common_voice_8_0
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- robust-speech-event
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- xlsr-fine-tuning-week
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datasets:
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- mozilla-foundation/common_voice_8_0
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- ovm
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- pscr
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- vystadial2016
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base_model: facebook/wav2vec2-xls-r-300m
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model-index:
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- name: Czech comodoro Wav2Vec2 XLSR 300M 250h data
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results:
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: Common Voice 8
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type: mozilla-foundation/common_voice_8_0
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args: cs
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metrics:
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- type: wer
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value: 7.3
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name: Test WER
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- type: cer
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value: 2.1
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name: Test CER
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: Robust Speech Event - Dev Data
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type: speech-recognition-community-v2/dev_data
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args: cs
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metrics:
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- type: wer
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value: 43.44
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name: Test WER
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- task:
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type: automatic-speech-recognition
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name: Automatic Speech Recognition
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dataset:
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name: Robust Speech Event - Test Data
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type: speech-recognition-community-v2/eval_data
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args: cs
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metrics:
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- type: wer
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value: 38.5
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name: Test WER
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---
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# Czech wav2vec2-xls-r-300m-cs-250
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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 as well as other datasets listed below.
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It achieves the following results on the evaluation set:
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- Loss: 0.1271
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- Wer: 0.1475
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- Cer: 0.0329
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The `eval.py` script results using a LM are:
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- WER: 0.07274312090176113
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- CER: 0.021207369275558875
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## Model description
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Czech using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
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When using this model, make sure that your speech input is sampled at 16kHz.
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The model can be used directly (without a language model) as follows:
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```python
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import torch
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import torchaudio
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from datasets import load_dataset
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
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test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "cs", split="test[:2%]")
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processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-250")
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model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-250")
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resampler = torchaudio.transforms.Resample(48_000, 16_000)
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# Preprocessing the datasets.
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# We need to read the aduio files as arrays
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def speech_file_to_array_fn(batch):
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speech_array, sampling_rate = torchaudio.load(batch["path"])
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batch["speech"] = resampler(speech_array).squeeze().numpy()
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return batch
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test_dataset = test_dataset.map(speech_file_to_array_fn)
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inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
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with torch.no_grad():
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
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predicted_ids = torch.argmax(logits, dim=-1)
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print("Prediction:", processor.batch_decode(predicted_ids))
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print("Reference:", test_dataset[:2]["sentence"])
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```
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## Evaluation
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The model can be evaluated using the attached `eval.py` script:
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```
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python eval.py --model_id comodoro/wav2vec2-xls-r-300m-cs-250 --dataset mozilla-foundation/common-voice_8_0 --split test --config cs
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```
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## Training and evaluation data
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The Common Voice 8.0 `train` and `validation` datasets were used for training, as well as the following datasets:
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- 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.
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- 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.
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- 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.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 32
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 800
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- num_epochs: 5
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
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| 3.4203 | 0.16 | 800 | 3.3148 | 1.0 | 1.0 |
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| 2.8151 | 0.32 | 1600 | 0.8508 | 0.8938 | 0.2345 |
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| 0.9411 | 0.48 | 2400 | 0.3335 | 0.3723 | 0.0847 |
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| 0.7408 | 0.64 | 3200 | 0.2573 | 0.2840 | 0.0642 |
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| 0.6516 | 0.8 | 4000 | 0.2365 | 0.2581 | 0.0595 |
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| 0.6242 | 0.96 | 4800 | 0.2039 | 0.2433 | 0.0541 |
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| 0.5754 | 1.12 | 5600 | 0.1832 | 0.2156 | 0.0482 |
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| 0.5626 | 1.28 | 6400 | 0.1827 | 0.2091 | 0.0463 |
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| 0.5342 | 1.44 | 7200 | 0.1744 | 0.2033 | 0.0468 |
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| 0.4965 | 1.6 | 8000 | 0.1705 | 0.1963 | 0.0444 |
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| 0.5047 | 1.76 | 8800 | 0.1604 | 0.1889 | 0.0422 |
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| 0.4814 | 1.92 | 9600 | 0.1604 | 0.1827 | 0.0411 |
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| 0.4471 | 2.09 | 10400 | 0.1566 | 0.1822 | 0.0406 |
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| 0.4509 | 2.25 | 11200 | 0.1619 | 0.1853 | 0.0432 |
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| 0.4415 | 2.41 | 12000 | 0.1513 | 0.1764 | 0.0397 |
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| 0.4313 | 2.57 | 12800 | 0.1515 | 0.1739 | 0.0392 |
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| 0.4163 | 2.73 | 13600 | 0.1445 | 0.1695 | 0.0377 |
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| 0.4142 | 2.89 | 14400 | 0.1478 | 0.1699 | 0.0385 |
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| 0.4184 | 3.05 | 15200 | 0.1430 | 0.1669 | 0.0376 |
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| 0.3886 | 3.21 | 16000 | 0.1433 | 0.1644 | 0.0374 |
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| 0.3795 | 3.37 | 16800 | 0.1426 | 0.1648 | 0.0373 |
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| 0.3859 | 3.53 | 17600 | 0.1357 | 0.1604 | 0.0361 |
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| 0.3762 | 3.69 | 18400 | 0.1344 | 0.1558 | 0.0349 |
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| 0.384 | 3.85 | 19200 | 0.1379 | 0.1576 | 0.0359 |
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| 0.3762 | 4.01 | 20000 | 0.1344 | 0.1539 | 0.0346 |
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| 0.3559 | 4.17 | 20800 | 0.1339 | 0.1525 | 0.0351 |
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| 0.3683 | 4.33 | 21600 | 0.1315 | 0.1518 | 0.0342 |
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| 0.3572 | 4.49 | 22400 | 0.1307 | 0.1507 | 0.0342 |
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| 0.3494 | 4.65 | 23200 | 0.1294 | 0.1491 | 0.0335 |
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| 0.3476 | 4.81 | 24000 | 0.1287 | 0.1491 | 0.0336 |
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| 0.3475 | 4.97 | 24800 | 0.1271 | 0.1475 | 0.0329 |
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### Framework versions
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- Transformers 4.16.2
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- Pytorch 1.10.1+cu102
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- Datasets 1.18.3
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- Tokenizers 0.11.0
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