wav2vec2 lv 60 espeak cv ft

Introduction

The wav2vec2-lv-60-espeak-cv-ft model is designed for automatic speech recognition (ASR), specifically phoneme recognition in multiple languages. It is based on the pretrained wav2vec2-large-lv60 model and fine-tuned using the Common Voice dataset to transcribe phonetic labels.

Architecture

The model leverages the wav2vec2-large-lv60 architecture, a self-supervised model that has been pretrained on a large corpus of audio data. The fine-tuning process adapts this architecture to recognize and transcribe phonetic labels across multiple languages by mapping phonemes of training languages to the target language using articulatory features.

Training

The training process involves fine-tuning the wav2vec2-large-lv60 model on the Common Voice dataset, which includes multilingual phonetic labels. The model outputs a sequence of phonetic labels that can be mapped to words using a dictionary. This approach significantly improves cross-lingual phoneme recognition without requiring task-specific architectures.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install Dependencies:

   • Ensure that you have Python and the Hugging Face Transformers library installed.
   • Install the required packages with pip install transformers datasets torch.

2. Load Model and Processor:

   from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
   from datasets import load_dataset
   import torch
   
   processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
   model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
   

3. Load and Preprocess Data:

   ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
   input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values
   

4. Perform Inference:

   with torch.no_grad():
       logits = model(input_values).logits
   predicted_ids = torch.argmax(logits, dim=-1)
   transcription = processor.batch_decode(predicted_ids)
   

   • The transcription output will be a sequence of phonetic labels.

5. Hardware Suggestions:

   • For efficient processing, consider using cloud-based GPUs such as those provided by AWS, Google Cloud, or Azure.

License

This model is licensed under the Apache 2.0 License, which allows for both commercial and non-commercial use, distribution, and modification.