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--- |
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license: apache-2.0 |
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language: |
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- es |
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tags: |
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- common_voice_8_0 |
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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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datasets: |
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- mozilla-foundation/common_voice_8_0 |
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model-index: |
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- name: wave2vec-xls-r-300m-es |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_8_0 es |
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type: mozilla-foundation/common_voice_8_0 |
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args: es |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 14.6 |
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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: es |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 28.63 |
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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: es |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 29.72 |
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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-XLSR-300m-es |
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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 spanish common_voice dataset thanks to the GPU credits generously given by the OVHcloud for the Speech Recognition challenge. |
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It achieves the following results on the evaluation set |
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Without LM: |
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- Loss : 0.1900 |
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- Wer : 0.146 |
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With 5-gram: |
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- WER: 0.109 |
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- CER: 0.036 |
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### Usage with 5-gram. |
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The model can be used with n-gram (n=5) included in the processor as follows. |
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```python |
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import re |
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from transformers import AutoModelForCTC,Wav2Vec2ProcessorWithLM |
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import torch |
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# Loading model and processor |
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processor = Wav2Vec2ProcessorWithLM.from_pretrained("polodealvarado/xls-r-300m-es") |
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model = AutoModelForCTC.from_pretrained("polodealvarado/xls-r-300m-es") |
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# Cleaning characters |
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def remove_extra_chars(batch): |
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chars_to_ignore_regex = '[^a-záéíóúñ ]' |
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text = batch["translation"][target_lang] |
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batch["text"] = re.sub(chars_to_ignore_regex, "", text.lower()) |
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return batch |
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# Preparing dataset |
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def prepare_dataset(batch): |
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audio = batch["audio"] |
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batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"],return_tensors="pt",padding=True).input_values[0] |
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with processor.as_target_processor(): |
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batch["labels"] = processor(batch["sentence"]).input_ids |
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return batch |
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common_voice_test = load_dataset("mozilla-foundation/common_voice_8_0", "es", split="test",use_auth_token=True) |
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common_voice_test = common_voice_test.remove_columns(["accent", "age", "client_id", "down_votes", "gender", "locale", "segment", "up_votes"]) |
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common_voice_test = common_voice_test.cast_column("audio", Audio(sampling_rate=16_000)) |
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common_voice_test = common_voice_test.map(remove_extra_chars, remove_columns=dataset.column_names) |
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common_voice_test = common_voice_test.map(prepare_dataset) |
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# Testing first sample |
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inputs = torch_tensor(common_voice_test[0]["input_values"]) |
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with torch.no_grad(): |
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logits = model(inputs).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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text = processor.batch_decode(logits.numpy()).text |
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print(text) # 'bien y qué regalo vas a abrir primero' |
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``` |
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On the other, you can execute the eval.py file for evaluation |
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```bash |
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# To use GPU: --device 0 |
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$ python eval.py --model_id polodealvarado/xls-r-300m-es --dataset mozilla-foundation/common_voice_8_0 --config es --device 0 --split test |
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``` |
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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: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 4 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 3.6747 | 0.3 | 400 | 0.6535 | 0.5926 | |
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| 0.4439 | 0.6 | 800 | 0.3753 | 0.3193 | |
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| 0.3291 | 0.9 | 1200 | 0.3267 | 0.2721 | |
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| 0.2644 | 1.2 | 1600 | 0.2816 | 0.2311 | |
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| 0.24 | 1.5 | 2000 | 0.2647 | 0.2179 | |
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| 0.2265 | 1.79 | 2400 | 0.2406 | 0.2048 | |
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| 0.1994 | 2.09 | 2800 | 0.2357 | 0.1869 | |
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| 0.1613 | 2.39 | 3200 | 0.2242 | 0.1821 | |
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| 0.1546 | 2.69 | 3600 | 0.2123 | 0.1707 | |
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| 0.1441 | 2.99 | 4000 | 0.2067 | 0.1619 | |
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| 0.1138 | 3.29 | 4400 | 0.2044 | 0.1519 | |
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| 0.1072 | 3.59 | 4800 | 0.1917 | 0.1457 | |
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| 0.0992 | 3.89 | 5200 | 0.1900 | 0.1438 | |
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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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