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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_16_1 |
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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-tr-cv16.1 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_16_1 |
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type: common_voice_16_1 |
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config: tr |
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split: test |
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args: tr |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.41599252148275984 |
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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-tr-cv16.1 |
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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_16_1 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3356 |
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- Wer: 0.4160 |
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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: 0.0003 |
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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: 500 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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## Model Inference |
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```python |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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model = Wav2Vec2ForCTC.from_pretrained("rumeyskeskn/wav2vec2-large-xls-r-300m-tr-cv16.1").to("cpu") |
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processor = Wav2Vec2Processor.from_pretrained("rumeyskeskn/wav2vec2-large-xls-r-300m-tr-cv16.1") |
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audio_path = "audio.wav" |
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audio_array, sampling_rate = librosa.load(audio_path, sr=16000) |
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input_values = processor(audio_array, sampling_rate=sampling_rate).input_values[0] |
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input_dict = processor(input_values, return_tensors="pt", padding=True) |
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logits = model(input_dict.input_values).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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prediction = processor.decode(pred_ids[0]) |
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print("Prediction:") |
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print(prediction) |
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``` |
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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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| 5.669 | 0.39 | 400 | 1.2228 | 0.8840 | |
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| 0.6809 | 0.78 | 800 | 0.6371 | 0.6557 | |
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| 0.4224 | 1.17 | 1200 | 0.4607 | 0.5226 | |
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| 0.3151 | 1.56 | 1600 | 0.3671 | 0.4457 | |
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| 0.2633 | 1.95 | 2000 | 0.3356 | 0.4160 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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