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--- |
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language: |
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- pl |
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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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- robust-speech-event |
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- xlsr-fine-tuning-week |
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- hf-asr-leaderboard |
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datasets: |
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- common_voice |
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model-index: |
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- name: Polish 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: pl |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 17.0 |
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- name: Test CER |
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type: cer |
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value: 3.8 |
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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: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: pl |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 38.97 |
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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: Robust Speech Event - Test Data |
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type: speech-recognition-community-v2/eval_data |
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args: pl |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 46.05 |
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--- |
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# wav2vec2-xls-r-300m-pl-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: 0.1716 |
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- Wer: 0.1697 |
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- Cer: 0.0385 |
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The `eval.py` script results are: |
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WER: 0.16970531733661967 |
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CER: 0.03839135416519316 |
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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 Polish 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", "pl", split="test[:2%]") |
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processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8") |
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model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-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-pl-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config pl |
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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: |
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- learning_rate: 1e-4 |
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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: 1 |
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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 training was interrupted after 3250 steps. |
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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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