wav2vec2 xlsr 53 espeak cv ft

Introduction

The WAV2VEC2-XLSR-53-ESPEAK-CV-FT model is an advanced automatic speech recognition system capable of recognizing phonetic labels across multiple languages. It leverages the pretrained wav2vec2-large-xlsr-53 model, fine-tuned on the Common Voice dataset, to enhance its phoneme recognition capabilities.

Architecture

This model is built on the wav2vec 2.0 architecture, which utilizes self-supervised learning to process audio data. It is designed to handle multilingual phoneme recognition by mapping the phonemes of training languages to target languages using articulatory features, significantly improving cross-lingual transfer learning.

Training

The model was fine-tuned using the Common Voice dataset, which comprises diverse multilingual audio samples. This fine-tuning process helps the model to generalize and perform zero-shot phoneme recognition across different languages.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install Dependencies

   • Ensure you have transformers, torch, and datasets Python libraries installed.

2. Load the Model and Processor

   from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
   processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
   model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
   

3. Prepare Input Data

   • Load your audio data sampled at 16kHz.
   • Use the processor to tokenize the audio input.

4. Inference

   • Pass the tokenized data through the model to obtain logits.
   • Decode the logits to get the phonetic transcription.

5. Example Code

   from datasets import load_dataset
   import torch
   
   ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
   input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values
   
   with torch.no_grad():
       logits = model(input_values).logits
   
   predicted_ids = torch.argmax(logits, dim=-1)
   transcription = processor.batch_decode(predicted_ids)
   

For optimal performance, consider using cloud GPUs like AWS EC2, Google Cloud, or Azure.

License

The model is distributed under the Apache 2.0 License, which allows for both personal and commercial use with proper attribution.