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--- |
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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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- mozilla-foundation/common_voice_8_0 |
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- generated_from_trainer |
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- robust-speech-event |
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- xlsr-fine-tuning-week |
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datasets: |
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- common_voice |
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model-index: |
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- name: Czech comodoro Wav2Vec2 XLSR 300M CV8 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: 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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- name: Test WER |
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type: wer |
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value: 15.9 |
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- name: Test CER |
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type: cer |
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value: 3.7 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-xls-r-300m-cs-cv8 |
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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. |
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It achieves the following results on the evaluation set while training: |
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- Loss: 0.2327 |
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- Wer: 0.1608 |
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- Cer: 0.0376 |
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The `eval.py` script results are: |
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WER: 0.1590958616454367 |
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CER: 0.036940922561315544 |
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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-cv8") |
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model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-cv8") |
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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-cv8 --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 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during first stage of training: |
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- learning_rate: 7e-05 |
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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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- gradient_accumulation_steps: 20 |
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- total_train_batch_size: 640 |
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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: 500 |
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- num_epochs: 150 |
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- mixed_precision_training: Native AMP |
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The following hyperparameters were used during second stage of training: |
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- learning_rate: 0.001 |
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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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- gradient_accumulation_steps: 20 |
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- total_train_batch_size: 640 |
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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: 500 |
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- num_epochs: 50 |
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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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| 7.2926 | 8.06 | 250 | 3.8497 | 1.0 | 1.0 | |
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| 3.417 | 16.13 | 500 | 3.2852 | 1.0 | 0.9857 | |
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| 2.0264 | 24.19 | 750 | 0.7099 | 0.7342 | 0.1768 | |
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| 0.4018 | 32.25 | 1000 | 0.6188 | 0.6415 | 0.1551 | |
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| 0.2444 | 40.32 | 1250 | 0.6632 | 0.6362 | 0.1600 | |
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| 0.1882 | 48.38 | 1500 | 0.6070 | 0.5783 | 0.1388 | |
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| 0.153 | 56.44 | 1750 | 0.6425 | 0.5720 | 0.1377 | |
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| 0.1214 | 64.51 | 2000 | 0.6363 | 0.5546 | 0.1337 | |
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| 0.1011 | 72.57 | 2250 | 0.6310 | 0.5222 | 0.1224 | |
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| 0.0879 | 80.63 | 2500 | 0.6353 | 0.5258 | 0.1253 | |
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| 0.0782 | 88.7 | 2750 | 0.6078 | 0.4904 | 0.1127 | |
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| 0.0709 | 96.76 | 3000 | 0.6465 | 0.4960 | 0.1154 | |
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| 0.0661 | 104.82 | 3250 | 0.6622 | 0.4945 | 0.1166 | |
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| 0.0616 | 112.89 | 3500 | 0.6440 | 0.4786 | 0.1104 | |
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| 0.0579 | 120.95 | 3750 | 0.6815 | 0.4887 | 0.1144 | |
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| 0.0549 | 129.03 | 4000 | 0.6603 | 0.4780 | 0.1105 | |
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| 0.0527 | 137.09 | 4250 | 0.6652 | 0.4749 | 0.1090 | |
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| 0.0506 | 145.16 | 4500 | 0.6958 | 0.4846 | 0.1133 | |
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Further fine-tuning with slightly different architecture and higher learning rate: |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:| |
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| 0.576 | 8.06 | 250 | 0.2411 | 0.2340 | 0.0502 | |
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| 0.2564 | 16.13 | 500 | 0.2305 | 0.2097 | 0.0492 | |
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| 0.2018 | 24.19 | 750 | 0.2371 | 0.2059 | 0.0494 | |
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| 0.1549 | 32.25 | 1000 | 0.2298 | 0.1844 | 0.0435 | |
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| 0.1224 | 40.32 | 1250 | 0.2288 | 0.1725 | 0.0407 | |
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| 0.1004 | 48.38 | 1500 | 0.2327 | 0.1608 | 0.0376 | |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
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