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--- |
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language: |
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- ja |
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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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- generated_from_trainer |
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- robust-speech-event |
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- ja |
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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: 'XLS-R-300-m' |
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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: ja |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 95.82 |
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- name: Test CER |
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type: cer |
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value: 23.64 |
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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: de |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 100.0 |
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- name: Test CER |
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type: cer |
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value: 30.99 |
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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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# |
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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 MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - JA dataset. |
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Kanji are converted into Hiragana using the [pykakasi](https://pykakasi.readthedocs.io/en/latest/index.html) library during training and evaluation. The model can output both Hiragana and Katakana characters. Since there is no spacing, WER is not a suitable metric for evaluating performance and CER is more suitable. |
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On mozilla-foundation/common_voice_8_0 it achieved: |
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- cer: 23.64% |
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On speech-recognition-community-v2/dev_data it achieved: |
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- cer: 30.99% |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5212 |
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- Wer: 1.3068 |
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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: 7.5e-05 |
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- train_batch_size: 48 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 2000 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 4.0974 | 4.72 | 1000 | 4.0178 | 1.9535 | |
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| 2.1276 | 9.43 | 2000 | 0.9301 | 1.2128 | |
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| 1.7622 | 14.15 | 3000 | 0.7103 | 1.5527 | |
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| 1.6397 | 18.87 | 4000 | 0.6729 | 1.4269 | |
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| 1.5468 | 23.58 | 5000 | 0.6087 | 1.2497 | |
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| 1.4885 | 28.3 | 6000 | 0.5786 | 1.3222 | |
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| 1.451 | 33.02 | 7000 | 0.5726 | 1.3768 | |
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| 1.3912 | 37.74 | 8000 | 0.5518 | 1.2497 | |
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| 1.3617 | 42.45 | 9000 | 0.5352 | 1.2694 | |
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| 1.3113 | 47.17 | 10000 | 0.5228 | 1.2781 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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#### Evaluation Commands |
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1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` |
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```bash |
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python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset mozilla-foundation/common_voice_8_0 --config ja --split test --log_outputs |
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``` |
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2. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` |
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```bash |
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python ./eval.py --model_id AndrewMcDowell/wav2vec2-xls-r-300m-japanese --dataset speech-recognition-community-v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0 |
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``` |
