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--- |
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language: |
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- mt |
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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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metrics: |
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- wer |
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model-index: |
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- name: XLS-R-300M - Maltese |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Speech Recognition |
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dataset: |
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type: mozilla-foundation/common_voice_8_0 |
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name: Common Voice 8 |
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args: mt |
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metrics: |
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- type: wer |
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value: 15.967 |
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name: Test WER |
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- name: Test CER |
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type: cer |
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value: 3.657 |
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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 - Maltese |
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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 - MT dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1895 |
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- Wer: 0.1984 |
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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: 60.0 |
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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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| 3.4219 | 3.6 | 400 | 3.3127 | 1.0 | |
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| 3.0399 | 7.21 | 800 | 3.0330 | 1.0 | |
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| 1.5756 | 10.81 | 1200 | 0.6108 | 0.5724 | |
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| 1.0995 | 14.41 | 1600 | 0.3091 | 0.3154 | |
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| 0.9639 | 18.02 | 2000 | 0.2596 | 0.2841 | |
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| 0.9032 | 21.62 | 2400 | 0.2270 | 0.2514 | |
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| 0.8145 | 25.23 | 2800 | 0.2172 | 0.2483 | |
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| 0.7845 | 28.83 | 3200 | 0.2084 | 0.2333 | |
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| 0.7694 | 32.43 | 3600 | 0.1974 | 0.2234 | |
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| 0.7333 | 36.04 | 4000 | 0.2020 | 0.2185 | |
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| 0.693 | 39.64 | 4400 | 0.1947 | 0.2148 | |
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| 0.6802 | 43.24 | 4800 | 0.1960 | 0.2102 | |
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| 0.667 | 46.85 | 5200 | 0.1904 | 0.2072 | |
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| 0.6486 | 50.45 | 5600 | 0.1881 | 0.2009 | |
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| 0.6339 | 54.05 | 6000 | 0.1877 | 0.1989 | |
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| 0.6254 | 57.66 | 6400 | 0.1893 | 0.2003 | |
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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-mt-cv8-with-lm --dataset mozilla-foundation/common_voice_8_0 --config mt --split test |
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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-mt-cv8-with-lm" |
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mt", 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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# => "għadu jilagħbu ċirku tant bilfondi" |
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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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| 19.853 | 15.967 | |
