Wav2vec2 Xlsr 53 Espeak Cv Ft

🚀 Wav2Vec2-Large-XLSR-53 finetuned on multi-lingual Common Voice

This model leverages a pre - trained checkpoint and is fine - tuned on CommonVoice to recognize phonetic labels in multiple languages.

🚀 Quick Start

This checkpoint leverages the pretrained checkpoint wav2vec2-large-xlsr-53 and is fine - tuned on CommonVoice to recognize phonetic labels in multiple languages.

When using the model make sure that your speech input is sampled at 16kHz. Note that the model outputs a string of phonetic labels. A dictionary mapping phonetic labels to words has to be used to map the phonetic output labels to output words.

✨ Features

• Multi - lingual Phoneme Recognition: Capable of recognizing phonetic labels in multiple languages.
• Based on Pretrained Model: Leverages the wav2vec2-large-xlsr-53 pretrained checkpoint.
• Fine - tuned on CommonVoice: Fine - tuned on the CommonVoice dataset.

📚 Documentation

📖 Paper

Paper: Simple and Effective Zero - shot Cross - lingual Phoneme Recognition

👨‍🎓 Authors

Qiantong Xu, Alexei Baevski, Michael Auli

📄 Abstract

Recent progress in self - training, self - supervised pretraining and unsupervised learning enabled well performing speech recognition systems without any labeled data. However, in many cases there is labeled data available for related languages which is not utilized by these methods. This paper extends previous work on zero - shot cross - lingual transfer learning by fine - tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. This is done by mapping phonemes of the training languages to the target language using articulatory features. Experiments show that this simple method significantly outperforms prior work which introduced task - specific architectures and used only part of a monolingually pretrained model.

🔗 Original Model

The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec - 20.

💻 Usage Examples

Basic Usage

from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch

# load model and processor
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")

# load dummy dataset and read soundfiles
ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")

# tokenize
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values

# retrieve logits
with torch.no_grad():
  logits = model(input_values).logits

# take argmax and decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)
# => should give ['m ɪ s t ɚ k w ɪ l t ɚ ɪ z ð ɪ ɐ p ɑː s əl l ʌ v ð ə m ɪ d əl k l æ s ɪ z æ n d w iː aʊ ɡ l æ d t ə w ɛ l k ə m h ɪ z ɡ ɑː s p ə']

📄 License

This project is licensed under the apache - 2.0 license.

📦 Information