--- language: - ha license: apache-2.0 tags: - generated_from_trainer - robust-speech-event - hf-asr-leaderboard datasets: - mozilla-foundation/common_voice_8_0 metrics: - wer model-index: - name: XLS-R-300M - Hausa results: - task: type: automatic-speech-recognition name: Speech Recognition dataset: type: mozilla-foundation/common_voice_8_0 name: Common Voice 8 args: ha metrics: - type: wer value: 36.295 name: Test WER - name: Test CER type: cer value: 11.073 --- # XLS-R-300M - Hausa 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 dataset. It achieves the following results on the evaluation set: - Loss: 0.6094 - Wer: 0.5234 ## 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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 13 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 1000 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.9599 | 6.56 | 400 | 2.8650 | 1.0 | | 2.7357 | 13.11 | 800 | 2.7377 | 0.9951 | | 1.3012 | 19.67 | 1200 | 0.6686 | 0.7111 | | 1.0454 | 26.23 | 1600 | 0.5686 | 0.6137 | | 0.9069 | 32.79 | 2000 | 0.5576 | 0.5815 | | 0.82 | 39.34 | 2400 | 0.5502 | 0.5591 | | 0.7413 | 45.9 | 2800 | 0.5970 | 0.5586 | | 0.6872 | 52.46 | 3200 | 0.5817 | 0.5428 | | 0.634 | 59.02 | 3600 | 0.5636 | 0.5314 | | 0.6022 | 65.57 | 4000 | 0.5780 | 0.5229 | | 0.5705 | 72.13 | 4400 | 0.6036 | 0.5323 | | 0.5408 | 78.69 | 4800 | 0.6119 | 0.5336 | | 0.5225 | 85.25 | 5200 | 0.6105 | 0.5270 | | 0.5265 | 91.8 | 5600 | 0.6034 | 0.5231 | | 0.5154 | 98.36 | 6000 | 0.6094 | 0.5234 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - 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-large-xls-r-300m-ha-cv8 --dataset mozilla-foundation/common_voice_8_0 --config ha --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-large-xls-r-300m-ha-cv8" sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ha", 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 # => "kakin hade ya ke da kyautar" ``` ### Eval results on Common Voice 8 "test" (WER): | Without LM | With LM (run `./eval.py`) | |---|---| | 47.821 | 36.295 |