| --- |
| language: |
| - he |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| - he |
| - robust-speech-event |
| datasets: |
| - imvladikon/hebrew_speech_kan |
| - imvladikon/hebrew_speech_coursera |
| metrics: |
| - wer |
| base_model: facebook/wav2vec2-xls-r-300m |
| model-index: |
| - name: wav2vec2-xls-r-300m-lm-hebrew |
| results: [] |
| --- |
| |
| # 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 |
