
# 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
{"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": []}]}
transformers pytorch safetensors wav2vec2 automatic-speech-recognition generated_from_trainer he robust-speech-event dataset:imvladikon/hebrew_speech_kan dataset:imvladikon/hebrew_speech_coursera base_model:facebook/wav2vec2-xls-r-300m license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #he #robust-speech-event #dataset-imvladikon/hebrew_speech_kan #dataset-imvladikon/hebrew_speech_coursera #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us 


# wav2vec2-xls-r-300m-lm-hebrew

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset
with adding ngram models according to Boosting Wav2Vec2 with n-grams in  Transformers


## Usage

check package: URL   

or use transformers pipeline:    



## 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
# wav2vec2-xls-r-300m-lm-hebrew

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset
with adding ngram models according to Boosting Wav2Vec2 with n-grams in  Transformers ## Usage

check package: URL   

or use transformers pipeline: ## 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
TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #he #robust-speech-event #dataset-imvladikon/hebrew_speech_kan #dataset-imvladikon/hebrew_speech_coursera #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us 
 # wav2vec2-xls-r-300m-lm-hebrew

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset
with adding ngram models according to Boosting Wav2Vec2 with n-grams in  Transformers ## Usage

check package: URL   

or use transformers pipeline: ## 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
passage: TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #he #robust-speech-event #dataset-imvladikon/hebrew_speech_kan #dataset-imvladikon/hebrew_speech_coursera #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us 
# wav2vec2-xls-r-300m-lm-hebrew

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset
with adding ngram models according to Boosting Wav2Vec2 with n-grams in  Transformers## Usage

check package: URL   

or use transformers pipeline:## 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