wav2vec2-xls-r-timit-phoneme

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
import soundfile as sf

# load model and processor
processor = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-timit-phoneme")
model = Wav2Vec2ForCTC.from_pretrained("vitouphy/wav2vec2-xls-r-300m-timit-phoneme")

# Read and process the input
audio_input, sample_rate = sf.read("audio_file.wav")
inputs = processor(audio_input, sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad():
    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

# Decode id into string
predicted_ids = torch.argmax(logits, axis=-1)
predicted_sentences = processor.batch_decode(predicted_ids)
print(predicted_sentences)

Training and evaluation data

• We split into 80/10/10 for training, validation, and testing respectively.
• That roughly corresponds to about 137/17/17 minutes.
• The model obtained 7.996% on this test set.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• 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: 2000
• training_steps: 10000
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0

Citation

@misc { phy22-phoneme,
  author       = {Phy, Vitou},
  title        = {{Automatic Phoneme Recognition on TIMIT Dataset with Wav2Vec 2.0}},
  year         = 2022,
  note         = {{If you use this model, please cite it using these metadata.}},
  publisher    = {Hugging Face},
  version      = {1.0},
  doi          = {10.57967/hf/0125},
  url          = {https://huggingface.co/vitouphy/wav2vec2-xls-r-300m-timit-phoneme}
}