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--- |
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language: |
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- ha |
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license: apache-2.0 |
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tags: |
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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 - Hausa |
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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: ha |
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metrics: |
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- type: wer |
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value: 36.295 |
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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: 11.073 |
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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 - Hausa |
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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 common_voice dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6094 |
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- Wer: 0.5234 |
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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: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 13 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 100 |
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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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| 2.9599 | 6.56 | 400 | 2.8650 | 1.0 | |
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| 2.7357 | 13.11 | 800 | 2.7377 | 0.9951 | |
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| 1.3012 | 19.67 | 1200 | 0.6686 | 0.7111 | |
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| 1.0454 | 26.23 | 1600 | 0.5686 | 0.6137 | |
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| 0.9069 | 32.79 | 2000 | 0.5576 | 0.5815 | |
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| 0.82 | 39.34 | 2400 | 0.5502 | 0.5591 | |
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| 0.7413 | 45.9 | 2800 | 0.5970 | 0.5586 | |
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| 0.6872 | 52.46 | 3200 | 0.5817 | 0.5428 | |
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| 0.634 | 59.02 | 3600 | 0.5636 | 0.5314 | |
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| 0.6022 | 65.57 | 4000 | 0.5780 | 0.5229 | |
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| 0.5705 | 72.13 | 4400 | 0.6036 | 0.5323 | |
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| 0.5408 | 78.69 | 4800 | 0.6119 | 0.5336 | |
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| 0.5225 | 85.25 | 5200 | 0.6105 | 0.5270 | |
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| 0.5265 | 91.8 | 5600 | 0.6034 | 0.5231 | |
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| 0.5154 | 98.36 | 6000 | 0.6094 | 0.5234 | |
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### Framework versions |
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- Transformers 4.16.1 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.2 |
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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-large-xls-r-300m-ha-cv8 --dataset mozilla-foundation/common_voice_8_0 --config ha --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-large-xls-r-300m-ha-cv8" |
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ha", 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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# => "kakin hade ya ke da kyautar" |
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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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| 47.821 | 36.295 | |
