readme

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+- he
+- robust-speech-event
+model-index:
+- name: wav2vec2-xls-r-300m-lm-hebrew
+  results: []
+datasets:
+- imvladikon/hebrew_speech_kan
+- imvladikon/hebrew_speech_coursera
+language:
+- he
+metrics:
+- wer
+---
+
+# wav2vec2-xls-r-300m-lm-hebrew
+
+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
+with adding ngram models according to [Boosting Wav2Vec2 with n-grams in 🤗 Transformers](https://huggingface.co/blog/wav2vec2-with-ngram)
+
+
+## Usage
+
+check package: https://github.com/imvladikon/wav2vec2-hebrew   
+
+or use transformers pipeline:    
+
+```python
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCTC, AutoProcessor
+import torchaudio.functional as F
+
+
+model_id = "imvladikon/wav2vec2-xls-r-300m-lm-hebrew"
+
+sample_iter = iter(load_dataset("google/fleurs", "he_il", split="test", streaming=True))
+
+sample = next(sample_iter)
+resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), sample["audio"]["sampling_rate"], 16_000).numpy()
+
+model = AutoModelForCTC.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained(model_id)
+
+input_values = processor(resampled_audio, return_tensors="pt").input_values
+
+with torch.no_grad():
+    logits = model(input_values).logits
+
+transcription = processor.batch_decode(logits.numpy()).text
+print(transcription)
+```
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0003
+- train_batch_size: 64
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 100
+- mixed_precision_training: Native AMP
+
+### Training results
+
+
+### Framework versions
+
+- Transformers 4.16.0.dev0
+- Pytorch 1.10.1+cu102
+- Datasets 1.17.1.dev0
+- Tokenizers 0.11.0