metadata

license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_16_1
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-tr-cv16.1
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_16_1
          type: common_voice_16_1
          config: tr
          split: test
          args: tr
        metrics:
          - name: Wer
            type: wer
            value: 0.41599252148275984

wav2vec2-large-xls-r-300m-tr-cv16.1

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_16_1 dataset. It achieves the following results on the evaluation set:

Loss: 0.3356
Wer: 0.4160

Model description

More information needed

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: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 2
mixed_precision_training: Native AMP

Model Inference

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

model = Wav2Vec2ForCTC.from_pretrained("rumeyskeskn/wav2vec2-large-xls-r-300m-tr-cv16.1").to("cpu")
processor = Wav2Vec2Processor.from_pretrained("rumeyskeskn/wav2vec2-large-xls-r-300m-tr-cv16.1")
audio_path = "audio.wav"

audio_array, sampling_rate = librosa.load(audio_path, sr=16000)

input_values = processor(audio_array, sampling_rate=sampling_rate).input_values[0]

input_dict = processor(input_values, return_tensors="pt", padding=True)


logits = model(input_dict.input_values).logits

pred_ids = torch.argmax(logits, dim=-1)
prediction = processor.decode(pred_ids[0])

print("Prediction:")
print(prediction)

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.669	0.39	400	1.2228	0.8840
0.6809	0.78	800	0.6371	0.6557
0.4224	1.17	1200	0.4607	0.5226
0.3151	1.56	1600	0.3671	0.4457
0.2633	1.95	2000	0.3356	0.4160

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2