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--- |
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language: |
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- ur |
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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: wav2vec2-large-xls-r-300m-ur-cv8 |
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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: ur |
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metrics: |
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- type: wer |
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value: 42.376 |
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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: 18.18 |
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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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# wav2vec2-large-xls-r-300m-ur-cv8 |
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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: 1.1443 |
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- Wer: 0.5677 |
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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: 42 |
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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: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 200 |
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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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| 3.6269 | 15.98 | 400 | 3.3246 | 1.0 | |
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| 3.0546 | 31.98 | 800 | 2.8148 | 0.9963 | |
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| 1.4589 | 47.98 | 1200 | 1.0237 | 0.6584 | |
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| 1.0911 | 63.98 | 1600 | 0.9524 | 0.5966 | |
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| 0.8879 | 79.98 | 2000 | 0.9827 | 0.5822 | |
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| 0.7467 | 95.98 | 2400 | 0.9923 | 0.5840 | |
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| 0.6427 | 111.98 | 2800 | 0.9988 | 0.5714 | |
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| 0.5685 | 127.98 | 3200 | 1.0872 | 0.5807 | |
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| 0.5068 | 143.98 | 3600 | 1.1194 | 0.5822 | |
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| 0.463 | 159.98 | 4000 | 1.1138 | 0.5692 | |
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| 0.4212 | 175.98 | 4400 | 1.1232 | 0.5714 | |
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| 0.4056 | 191.98 | 4800 | 1.1443 | 0.5677 | |
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### Framework versions |
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- Transformers 4.16.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.1 |
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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-ur-cv8 --dataset mozilla-foundation/common_voice_8_0 --config ur --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-ur-cv8" |
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sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "ur", 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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# => "اب نے ٹ پیس ان لیتے ہیں" |
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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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| 52.146 | 42.376 | |
