Wav2vec2 Lv 60 Espeak Cv Ft

🚀 Wav2Vec2-Large-LV60 finetuned on multi-lingual Common Voice

This project leverages the pre - trained checkpoint wav2vec2-large-lv60 and fine - tunes it on CommonVoice to recognize phonetic labels in multiple languages. It offers a solution for multi - lingual phoneme recognition, enabling more accurate speech - related tasks across different languages.

✨ Features

• Multi - lingual Support: Recognize phonetic labels in multiple languages.
• Leverage Pretrained Model: Based on the powerful wav2vec2-large-lv60 checkpoint.
• Zero - shot Cross - lingual Transfer: As described in the related paper, it can transcribe unseen languages through fine - tuning.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 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-lv-60-espeak-cv-ft")
 model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-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 ɹ ɪ z ð ɪ ɐ p ɑː s əl ʌ v ð ə m ɪ d əl k l æ s ᵻ z æ n d w iː ɑːɹ ɡ l æ d t ə w ɛ l k ə m h ɪ z ɡ ɑː s p əl']

📚 Documentation

Important Notes

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.

Paper Reference

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 Source

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

🔧 Technical Details

No specific technical details (more than 50 - word specific technical descriptions) are provided in the original document, so this section is skipped.

📄 License

This project is licensed under the apache - 2.0 license.