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--- |
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language: |
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- lv |
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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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- hf-asr-leaderboard |
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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-300M - Latvian |
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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: lv |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 9.633 |
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- name: Test CER |
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type: cer |
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value: 2.614 |
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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: lv |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 36.11 |
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- name: Test CER |
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type: cer |
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value: 14.244 |
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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: lv |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 44.12 |
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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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# XLS-R-300M - Latvian |
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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 - LV dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1660 |
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- Wer: 0.1705 |
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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: 32 |
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- eval_batch_size: 16 |
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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: 1000 |
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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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| 3.489 | 2.56 | 400 | 3.3590 | 1.0 | |
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| 2.9903 | 5.13 | 800 | 2.9704 | 1.0001 | |
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| 1.6712 | 7.69 | 1200 | 0.6179 | 0.6566 | |
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| 1.2635 | 10.26 | 1600 | 0.3176 | 0.4531 | |
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| 1.0819 | 12.82 | 2000 | 0.2517 | 0.3508 | |
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| 1.0136 | 15.38 | 2400 | 0.2257 | 0.3124 | |
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| 0.9625 | 17.95 | 2800 | 0.1975 | 0.2311 | |
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| 0.901 | 20.51 | 3200 | 0.1986 | 0.2097 | |
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| 0.8842 | 23.08 | 3600 | 0.1904 | 0.2039 | |
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| 0.8542 | 25.64 | 4000 | 0.1847 | 0.1981 | |
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| 0.8244 | 28.21 | 4400 | 0.1805 | 0.1847 | |
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| 0.7689 | 30.77 | 4800 | 0.1736 | 0.1832 | |
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| 0.7825 | 33.33 | 5200 | 0.1698 | 0.1821 | |
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| 0.7817 | 35.9 | 5600 | 0.1758 | 0.1803 | |
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| 0.7488 | 38.46 | 6000 | 0.1663 | 0.1760 | |
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| 0.7171 | 41.03 | 6400 | 0.1636 | 0.1721 | |
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| 0.7222 | 43.59 | 6800 | 0.1663 | 0.1729 | |
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| 0.7156 | 46.15 | 7200 | 0.1633 | 0.1715 | |
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| 0.7121 | 48.72 | 7600 | 0.1666 | 0.1718 | |
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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 anuragshas/wav2vec2-xls-r-300m-lv-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config lv --split test |
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``` |
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2. To evaluate on `speech-recognition-community-v2/dev_data` |
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```bash |
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python eval.py --model_id anuragshas/wav2vec2-xls-r-300m-lv-cv8-with-lm --dataset speech-recognition-community-v2/dev_data --config lv --split validation --chunk_length_s 5.0 --stride_length_s 1.0 |
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``` |
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### Inference With LM |
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```python |
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import torch |
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from datasets import load_dataset |
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from transformers import AutoModelForCTC, AutoProcessor |
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import torchaudio.functional as F |
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model_id = "anuragshas/wav2vec2-xls-r-300m-lv-cv8-with-lm" |
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "lv", split="test", streaming=True, use_auth_token=True)) |
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sample = next(sample_iter) |
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resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() |
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model = AutoModelForCTC.from_pretrained(model_id) |
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processor = AutoProcessor.from_pretrained(model_id) |
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input_values = processor(resampled_audio, return_tensors="pt").input_values |
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with torch.no_grad(): |
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logits = model(input_values).logits |
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transcription = processor.batch_decode(logits.numpy()).text |
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# => "domāju ka viņam viss labi" |
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``` |
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### Eval results on Common Voice 8 "test" (WER): |
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| Without LM | With LM (run `./eval.py`) | |
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|---|---| |
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| 16.997 | 9.633 | |
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