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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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name: wav2vec2-xls-r-300m-romanian |
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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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## This model achieves WER on common-voice ro test split of WER: 12.457178% |
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# wav2vec2-xls-r-300m-romanian |
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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 an common voice ro and RSS dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.0836 |
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- eval_wer: 0.0705 |
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- eval_runtime: 160.4549 |
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- eval_samples_per_second: 11.081 |
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- eval_steps_per_second: 1.39 |
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- epoch: 14.38 |
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- step: 2703 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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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: 50 |
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- num_epochs: 15 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.13.3 |
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- Tokenizers 0.10.3 |
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Used the following code for evaluation: |
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``` |
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import torch |
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import torchaudio |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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import re |
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test_dataset = load_dataset("common_voice", "ro", split="test") |
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wer = load_metric("wer") |
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processor = Wav2Vec2Processor.from_pretrained("Dumiiii/wav2vec2-xls-r-300m-romanian") |
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model = Wav2Vec2ForCTC.from_pretrained("Dumiiii/wav2vec2-xls-r-300m-romanian") |
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model.to("cuda") |
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chars_to_ignore_regex = '['+string.punctuation+']' |
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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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batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() |
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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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# Preprocessing the datasets. |
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# We need to read the aduio files as arrays |
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def evaluate(batch): |
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inputs = processor(batch["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.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) |
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``` |
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Credits for evaluation: https://huggingface.co/anton-l |