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--- |
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language: |
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- ro |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- hf-asr-leaderboard |
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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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- gigant/romanian_speech_synthesis_0_8_1 |
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base_model: facebook/wav2vec2-xls-r-300m |
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model-index: |
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- name: wav2vec2-ro-300m_01 |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Robust Speech Event |
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type: speech-recognition-community-v2/dev_data |
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args: ro |
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metrics: |
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- type: wer |
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value: 46.99 |
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name: Dev WER (without LM) |
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- type: cer |
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value: 16.04 |
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name: Dev CER (without LM) |
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- type: wer |
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value: 38.63 |
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name: Dev WER (with LM) |
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- type: cer |
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value: 14.52 |
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name: Dev CER (with LM) |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: Common Voice |
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type: mozilla-foundation/common_voice_8_0 |
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args: ro |
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metrics: |
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- type: wer |
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value: 11.73 |
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name: Test WER (without LM) |
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- type: cer |
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value: 2.93 |
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name: Test CER (without LM) |
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- type: wer |
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value: 7.31 |
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name: Test WER (with LM) |
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- type: cer |
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value: 2.17 |
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name: Test CER (with LM) |
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- task: |
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type: automatic-speech-recognition |
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name: 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: ro |
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metrics: |
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- type: wer |
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value: 43.23 |
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name: Test WER |
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--- |
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You can test this model online with the [**Space for Romanian Speech Recognition**](https://huggingface.co/spaces/gigant/romanian-speech-recognition) |
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The model ranked **TOP-1** on Romanian Speech Recognition during HuggingFace's Robust Speech Challenge : |
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* [**The 🤗 Speech Bench**](https://huggingface.co/spaces/huggingface/hf-speech-bench) |
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* [**Speech Challenge Leaderboard**](https://huggingface.co/spaces/speech-recognition-community-v2/FinalLeaderboard) |
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# Romanian Wav2Vec2 |
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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 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) dataset, with extra training data from [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) dataset. |
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Without the 5-gram Language Model optimization, it achieves the following results on the evaluation set (Common Voice 8.0, Romanian subset, test split): |
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- Loss: 0.1553 |
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- Wer: 0.1174 |
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- Cer: 0.0294 |
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## Model description |
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The architecture is based on [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) with a speech recognition CTC head and an added 5-gram language model (using [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) and [kenlm](https://github.com/kpu/kenlm)) trained on the [Romanian Corpora Parliament](gigant/ro_corpora_parliament_processed) dataset. Those libraries are needed in order for the language model-boosted decoder to work. |
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## Intended uses & limitations |
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The model is made for speech recognition in Romanian from audio clips sampled at **16kHz**. The predicted text is lowercased and does not contain any punctuation. |
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## How to use |
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Make sure you have installed the correct dependencies for the language model-boosted version to work. You can just run this command to install the `kenlm` and `pyctcdecode` libraries : |
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```pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode``` |
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With the framework `transformers` you can load the model with the following code : |
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``` |
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from transformers import AutoProcessor, AutoModelForCTC |
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processor = AutoProcessor.from_pretrained("gigant/romanian-wav2vec2") |
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model = AutoModelForCTC.from_pretrained("gigant/romanian-wav2vec2") |
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``` |
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Or, if you want to test the model, you can load the automatic speech recognition pipeline from `transformers` with : |
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``` |
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from transformers import pipeline |
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asr = pipeline("automatic-speech-recognition", model="gigant/romanian-wav2vec2") |
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``` |
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## Example use with the `datasets` library |
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First, you need to load your data |
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We will use the [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) dataset in this example. |
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``` |
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from datasets import load_dataset |
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dataset = load_dataset("gigant/romanian_speech_synthesis_0_8_1") |
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``` |
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You can listen to the samples with the `IPython.display` library : |
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``` |
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from IPython.display import Audio |
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i = 0 |
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sample = dataset["train"][i] |
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Audio(sample["audio"]["array"], rate = sample["audio"]["sampling_rate"]) |
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``` |
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The model is trained to work with audio sampled at 16kHz, so if the sampling rate of the audio in the dataset is different, we will have to resample it. |
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In the example, the audio is sampled at 48kHz. We can see this by checking `dataset["train"][0]["audio"]["sampling_rate"]` |
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The following code resample the audio using the `torchaudio` library : |
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``` |
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import torchaudio |
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import torch |
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i = 0 |
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audio = sample["audio"]["array"] |
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rate = sample["audio"]["sampling_rate"] |
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resampler = torchaudio.transforms.Resample(rate, 16_000) |
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audio_16 = resampler(torch.Tensor(audio)).numpy() |
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``` |
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To listen to the resampled sample : |
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``` |
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Audio(audio_16, rate=16000) |
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``` |
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Know you can get the model prediction by running |
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``` |
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predicted_text = asr(audio_16) |
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ground_truth = dataset["train"][i]["sentence"] |
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print(f"Predicted text : {predicted_text}") |
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print(f"Ground truth : {ground_truth}") |
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``` |
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## Training and evaluation data |
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Training data : |
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- [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) : train + validation + other splits |
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- [Romanian Speech Synthesis](https://huggingface.co/datasets/gigant/romanian_speech_synthesis_0_8_1) : train + test splits |
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Evaluation data : |
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- [Common Voice 8.0 - Romanian subset](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0) : test split |
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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: 0.003 |
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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: 3 |
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- total_train_batch_size: 48 |
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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: 50.0 |
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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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| 2.9272 | 0.78 | 500 | 0.7603 | 0.7734 | 0.2355 | |
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| 0.6157 | 1.55 | 1000 | 0.4003 | 0.4866 | 0.1247 | |
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| 0.4452 | 2.33 | 1500 | 0.2960 | 0.3689 | 0.0910 | |
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| 0.3631 | 3.11 | 2000 | 0.2580 | 0.3205 | 0.0796 | |
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| 0.3153 | 3.88 | 2500 | 0.2465 | 0.2977 | 0.0747 | |
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| 0.2795 | 4.66 | 3000 | 0.2274 | 0.2789 | 0.0694 | |
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| 0.2615 | 5.43 | 3500 | 0.2277 | 0.2685 | 0.0675 | |
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| 0.2389 | 6.21 | 4000 | 0.2135 | 0.2518 | 0.0627 | |
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| 0.2229 | 6.99 | 4500 | 0.2054 | 0.2449 | 0.0614 | |
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| 0.2067 | 7.76 | 5000 | 0.2096 | 0.2378 | 0.0597 | |
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| 0.1977 | 8.54 | 5500 | 0.2042 | 0.2387 | 0.0600 | |
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| 0.1896 | 9.32 | 6000 | 0.2110 | 0.2383 | 0.0595 | |
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| 0.1801 | 10.09 | 6500 | 0.1909 | 0.2165 | 0.0548 | |
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| 0.174 | 10.87 | 7000 | 0.1883 | 0.2206 | 0.0559 | |
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| 0.1685 | 11.65 | 7500 | 0.1848 | 0.2097 | 0.0528 | |
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| 0.1591 | 12.42 | 8000 | 0.1851 | 0.2039 | 0.0514 | |
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| 0.1537 | 13.2 | 8500 | 0.1881 | 0.2065 | 0.0518 | |
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| 0.1504 | 13.97 | 9000 | 0.1840 | 0.1972 | 0.0499 | |
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| 0.145 | 14.75 | 9500 | 0.1845 | 0.2029 | 0.0517 | |
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| 0.1417 | 15.53 | 10000 | 0.1884 | 0.2003 | 0.0507 | |
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| 0.1364 | 16.3 | 10500 | 0.2010 | 0.2037 | 0.0517 | |
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| 0.1331 | 17.08 | 11000 | 0.1838 | 0.1923 | 0.0483 | |
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| 0.129 | 17.86 | 11500 | 0.1818 | 0.1922 | 0.0489 | |
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| 0.1198 | 18.63 | 12000 | 0.1760 | 0.1861 | 0.0465 | |
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| 0.1203 | 19.41 | 12500 | 0.1686 | 0.1839 | 0.0465 | |
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| 0.1225 | 20.19 | 13000 | 0.1828 | 0.1920 | 0.0479 | |
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| 0.1145 | 20.96 | 13500 | 0.1673 | 0.1784 | 0.0446 | |
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| 0.1053 | 21.74 | 14000 | 0.1802 | 0.1810 | 0.0456 | |
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| 0.1071 | 22.51 | 14500 | 0.1769 | 0.1775 | 0.0444 | |
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| 0.1053 | 23.29 | 15000 | 0.1920 | 0.1783 | 0.0457 | |
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| 0.1024 | 24.07 | 15500 | 0.1904 | 0.1775 | 0.0446 | |
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| 0.0987 | 24.84 | 16000 | 0.1793 | 0.1762 | 0.0446 | |
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| 0.0949 | 25.62 | 16500 | 0.1801 | 0.1766 | 0.0443 | |
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| 0.0942 | 26.4 | 17000 | 0.1731 | 0.1659 | 0.0423 | |
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| 0.0906 | 27.17 | 17500 | 0.1776 | 0.1698 | 0.0424 | |
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| 0.0861 | 27.95 | 18000 | 0.1716 | 0.1600 | 0.0406 | |
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| 0.0851 | 28.73 | 18500 | 0.1662 | 0.1630 | 0.0410 | |
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| 0.0844 | 29.5 | 19000 | 0.1671 | 0.1572 | 0.0393 | |
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| 0.0792 | 30.28 | 19500 | 0.1768 | 0.1599 | 0.0407 | |
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| 0.0798 | 31.06 | 20000 | 0.1732 | 0.1558 | 0.0394 | |
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| 0.0779 | 31.83 | 20500 | 0.1694 | 0.1544 | 0.0388 | |
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| 0.0718 | 32.61 | 21000 | 0.1709 | 0.1578 | 0.0399 | |
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| 0.0732 | 33.38 | 21500 | 0.1697 | 0.1523 | 0.0391 | |
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| 0.0708 | 34.16 | 22000 | 0.1616 | 0.1474 | 0.0375 | |
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| 0.0678 | 34.94 | 22500 | 0.1698 | 0.1474 | 0.0375 | |
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| 0.0642 | 35.71 | 23000 | 0.1681 | 0.1459 | 0.0369 | |
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| 0.0661 | 36.49 | 23500 | 0.1612 | 0.1411 | 0.0357 | |
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| 0.0629 | 37.27 | 24000 | 0.1662 | 0.1414 | 0.0355 | |
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| 0.0587 | 38.04 | 24500 | 0.1659 | 0.1408 | 0.0351 | |
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| 0.0581 | 38.82 | 25000 | 0.1612 | 0.1382 | 0.0352 | |
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| 0.0556 | 39.6 | 25500 | 0.1647 | 0.1376 | 0.0345 | |
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| 0.0543 | 40.37 | 26000 | 0.1658 | 0.1335 | 0.0337 | |
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| 0.052 | 41.15 | 26500 | 0.1716 | 0.1369 | 0.0343 | |
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| 0.0513 | 41.92 | 27000 | 0.1600 | 0.1317 | 0.0330 | |
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| 0.0491 | 42.7 | 27500 | 0.1671 | 0.1311 | 0.0328 | |
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| 0.0463 | 43.48 | 28000 | 0.1613 | 0.1289 | 0.0324 | |
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| 0.0468 | 44.25 | 28500 | 0.1599 | 0.1260 | 0.0315 | |
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| 0.0435 | 45.03 | 29000 | 0.1556 | 0.1232 | 0.0308 | |
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| 0.043 | 45.81 | 29500 | 0.1588 | 0.1240 | 0.0309 | |
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| 0.0421 | 46.58 | 30000 | 0.1567 | 0.1217 | 0.0308 | |
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| 0.04 | 47.36 | 30500 | 0.1533 | 0.1198 | 0.0302 | |
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| 0.0389 | 48.14 | 31000 | 0.1582 | 0.1185 | 0.0297 | |
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| 0.0387 | 48.91 | 31500 | 0.1576 | 0.1187 | 0.0297 | |
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| 0.0376 | 49.69 | 32000 | 0.1560 | 0.1182 | 0.0295 | |
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### Framework versions |
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- Transformers 4.16.2 |
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- Pytorch 1.10.0+cu111 |
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- Tokenizers 0.11.0 |
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- pyctcdecode 0.3.0 |
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- kenlm |
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