--- language: - cs license: apache-2.0 tags: - automatic-speech-recognition - mozilla-foundation/common_voice_8_0 - generated_from_trainer - robust-speech-event - xlsr-fine-tuning-week - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: Czech 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: cs metrics: - name: Test WER type: wer value: 10.3 - name: Test CER type: cer value: 2.6 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: cs metrics: - name: Test WER type: wer value: 54.29 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: cs metrics: - name: Test WER type: wer value: 44.55 --- # 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 while training: - Loss: 0.2327 - Wer: 0.1608 - Cer: 0.0376 The `eval.py` script results using a LM are: WER: 0.10281503199350225 CER: 0.02622802241689026 ## Model description 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. 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: ```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", "cs", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-cv8") model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-cs-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-cs-cv8 --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 ## Training procedure ### Training hyperparameters The following hyperparameters were used during first stage of training: - learning_rate: 7e-05 - 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: 150 - mixed_precision_training: Native AMP The following hyperparameters were used during second stage of training: - learning_rate: 0.001 - 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:------:|:----:|:---------------:|:------:|:------:| | 7.2926 | 8.06 | 250 | 3.8497 | 1.0 | 1.0 | | 3.417 | 16.13 | 500 | 3.2852 | 1.0 | 0.9857 | | 2.0264 | 24.19 | 750 | 0.7099 | 0.7342 | 0.1768 | | 0.4018 | 32.25 | 1000 | 0.6188 | 0.6415 | 0.1551 | | 0.2444 | 40.32 | 1250 | 0.6632 | 0.6362 | 0.1600 | | 0.1882 | 48.38 | 1500 | 0.6070 | 0.5783 | 0.1388 | | 0.153 | 56.44 | 1750 | 0.6425 | 0.5720 | 0.1377 | | 0.1214 | 64.51 | 2000 | 0.6363 | 0.5546 | 0.1337 | | 0.1011 | 72.57 | 2250 | 0.6310 | 0.5222 | 0.1224 | | 0.0879 | 80.63 | 2500 | 0.6353 | 0.5258 | 0.1253 | | 0.0782 | 88.7 | 2750 | 0.6078 | 0.4904 | 0.1127 | | 0.0709 | 96.76 | 3000 | 0.6465 | 0.4960 | 0.1154 | | 0.0661 | 104.82 | 3250 | 0.6622 | 0.4945 | 0.1166 | | 0.0616 | 112.89 | 3500 | 0.6440 | 0.4786 | 0.1104 | | 0.0579 | 120.95 | 3750 | 0.6815 | 0.4887 | 0.1144 | | 0.0549 | 129.03 | 4000 | 0.6603 | 0.4780 | 0.1105 | | 0.0527 | 137.09 | 4250 | 0.6652 | 0.4749 | 0.1090 | | 0.0506 | 145.16 | 4500 | 0.6958 | 0.4846 | 0.1133 | Further fine-tuning with slightly different architecture and higher learning rate: | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:| | 0.576 | 8.06 | 250 | 0.2411 | 0.2340 | 0.0502 | | 0.2564 | 16.13 | 500 | 0.2305 | 0.2097 | 0.0492 | | 0.2018 | 24.19 | 750 | 0.2371 | 0.2059 | 0.0494 | | 0.1549 | 32.25 | 1000 | 0.2298 | 0.1844 | 0.0435 | | 0.1224 | 40.32 | 1250 | 0.2288 | 0.1725 | 0.0407 | | 0.1004 | 48.38 | 1500 | 0.2327 | 0.1608 | 0.0376 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0