Add results

README.md

@@ -1,3 +1,118 @@
----
-license: apache-2.0
----
+---
+language:
+- pl
+license: apache-2.0
+tags:
+- automatic-speech-recognition
+- mozilla-foundation/common_voice_8_0
+- robust-speech-event
+- xlsr-fine-tuning-week
+datasets:
+- common_voice
+model-index:
+- name: Polish comodoro Wav2Vec2 XLSR 300M CV8
+  results:
+  - task: 
+      name: Automatic Speech Recognition 
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 8
+      type: mozilla-foundation/common_voice_8_0
+      args: pl
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 17.0
+       - name: Test CER
+         type: cer 
+         value: 3.8
+---
+# wav2vec2-xls-r-300m-pl-cv8
+
+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 8.0 dataset.
+It achieves the following results on the evaluation set while training:
+- Loss: 0.1716
+- Wer: 0.1697
+- Cer: 0.0385
+
+The `eval.py` script results are:
+WER: 0.16970531733661967
+CER: 0.03839135416519316
+
+## Model description
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Polish using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+
+The model can be used directly (without a language model) as follows:
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+test_dataset = load_dataset("mozilla-foundation/common_voice_8_0", "pl", split="test[:2%]")
+
+processor = Wav2Vec2Processor.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
+model = Wav2Vec2ForCTC.from_pretrained("comodoro/wav2vec2-xls-r-300m-pl-cv8")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+	speech_array, sampling_rate = torchaudio.load(batch["path"])
+	batch["speech"] = resampler(speech_array).squeeze().numpy()
+	return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset[:2]["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset[:2]["sentence"])
+```
+
+## Evaluation
+
+The model can be evaluated using the attached `eval.py` script:
+```
+python eval.py --model_id comodoro/wav2vec2-xls-r-300m-pl-cv8 --dataset mozilla-foundation/common-voice_8_0 --split test --config pl
+```
+
+## Training and evaluation data
+
+The Common Voice 8.0 `train` and `validation` datasets were used for training
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used:
+
+- learning_rate: 1e-4
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 1
+- total_train_batch_size: 640
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 150
+- mixed_precision_training: Native AMP
+
+The training was interrupted after 3250 steps.
+
+### Framework versions
+
+- Transformers 4.16.0.dev0
+- Pytorch 1.10.1+cu102
+- Datasets 1.17.1.dev0
+- Tokenizers 0.11.0