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--- |
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language: |
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- he |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- he |
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- robust-speech-event |
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datasets: |
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- imvladikon/hebrew_speech_kan |
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- imvladikon/hebrew_speech_coursera |
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metrics: |
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- wer |
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base_model: facebook/wav2vec2-xls-r-300m |
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model-index: |
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- name: wav2vec2-xls-r-300m-lm-hebrew |
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results: [] |
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--- |
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# wav2vec2-xls-r-300m-lm-hebrew |
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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 None dataset |
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with adding ngram models according to [Boosting Wav2Vec2 with n-grams in 🤗 Transformers](https://huggingface.co/blog/wav2vec2-with-ngram) |
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## Usage |
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check package: https://github.com/imvladikon/wav2vec2-hebrew |
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or use transformers pipeline: |
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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 = "imvladikon/wav2vec2-xls-r-300m-lm-hebrew" |
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sample_iter = iter(load_dataset("google/fleurs", "he_il", split="test", streaming=True)) |
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sample = next(sample_iter) |
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resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), sample["audio"]["sampling_rate"], 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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print(transcription) |
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``` |
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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: 64 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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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: 100 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- Transformers 4.16.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.17.1.dev0 |
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- Tokenizers 0.11.0 |
