| | --- |
| | language: |
| | - mt |
| | license: apache-2.0 |
| | tags: |
| | - automatic-speech-recognition |
| | - mozilla-foundation/common_voice_8_0 |
| | - generated_from_trainer |
| | - robust-speech-event |
| | - hf-asr-leaderboard |
| | datasets: |
| | - mozilla-foundation/common_voice_8_0 |
| | metrics: |
| | - wer |
| | model-index: |
| | - name: XLS-R-300M - Maltese |
| | results: |
| | - task: |
| | type: automatic-speech-recognition |
| | name: Speech Recognition |
| | dataset: |
| | type: mozilla-foundation/common_voice_8_0 |
| | name: Common Voice 8 |
| | args: mt |
| | metrics: |
| | - type: wer |
| | value: 15.967 |
| | name: Test WER |
| | - name: Test CER |
| | type: cer |
| | value: 3.657 |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # XLS-R-300M - Maltese |
| |
|
| | 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. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.1895 |
| | - Wer: 0.1984 |
| | |
| | ## Model description |
| | |
| | More information needed |
| | |
| | ## Intended uses & limitations |
| | |
| | More information needed |
| | |
| | ## Training and evaluation data |
| | |
| | More information needed |
| | |
| | ## Training procedure |
| | |
| | ### Training hyperparameters |
| | |
| | The following hyperparameters were used during training: |
| | - learning_rate: 7.5e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 1000 |
| | - num_epochs: 60.0 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Wer | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | | 3.4219 | 3.6 | 400 | 3.3127 | 1.0 | |
| | | 3.0399 | 7.21 | 800 | 3.0330 | 1.0 | |
| | | 1.5756 | 10.81 | 1200 | 0.6108 | 0.5724 | |
| | | 1.0995 | 14.41 | 1600 | 0.3091 | 0.3154 | |
| | | 0.9639 | 18.02 | 2000 | 0.2596 | 0.2841 | |
| | | 0.9032 | 21.62 | 2400 | 0.2270 | 0.2514 | |
| | | 0.8145 | 25.23 | 2800 | 0.2172 | 0.2483 | |
| | | 0.7845 | 28.83 | 3200 | 0.2084 | 0.2333 | |
| | | 0.7694 | 32.43 | 3600 | 0.1974 | 0.2234 | |
| | | 0.7333 | 36.04 | 4000 | 0.2020 | 0.2185 | |
| | | 0.693 | 39.64 | 4400 | 0.1947 | 0.2148 | |
| | | 0.6802 | 43.24 | 4800 | 0.1960 | 0.2102 | |
| | | 0.667 | 46.85 | 5200 | 0.1904 | 0.2072 | |
| | | 0.6486 | 50.45 | 5600 | 0.1881 | 0.2009 | |
| | | 0.6339 | 54.05 | 6000 | 0.1877 | 0.1989 | |
| | | 0.6254 | 57.66 | 6400 | 0.1893 | 0.2003 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.17.0.dev0 |
| | - Pytorch 1.10.2+cu102 |
| | - Datasets 1.18.2.dev0 |
| | - Tokenizers 0.11.0 |
| |
|
| | #### Evaluation Commands |
| | 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` |
| |
|
| | ```bash |
| | 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 |
| | ``` |
| |
|
| |
|
| | ### Inference With LM |
| |
|
| | ```python |
| | import torch |
| | from datasets import load_dataset |
| | from transformers import AutoModelForCTC, AutoProcessor |
| | import torchaudio.functional as F |
| | model_id = "anuragshas/wav2vec2-xls-r-300m-mt-cv8-with-lm" |
| | sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "mt", split="test", streaming=True, use_auth_token=True)) |
| | sample = next(sample_iter) |
| | resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() |
| | model = AutoModelForCTC.from_pretrained(model_id) |
| | processor = AutoProcessor.from_pretrained(model_id) |
| | input_values = processor(resampled_audio, return_tensors="pt").input_values |
| | with torch.no_grad(): |
| | logits = model(input_values).logits |
| | transcription = processor.batch_decode(logits.numpy()).text |
| | # => "għadu jilagħbu ċirku tant bilfondi" |
| | ``` |
| |
|
| | ### Eval results on Common Voice 8 "test" (WER): |
| |
|
| | | Without LM | With LM (run `./eval.py`) | |
| | |---|---| |
| | | 19.853 | 15.967 | |
