indicwav2vec_v1_telugu

Introduction

The indicwav2vec_v1_telugu is a speech recognition model developed by AI4BHARAT, specifically designed for the Telugu language. It leverages the capabilities of the Wav2Vec architecture to transcribe audio into text, facilitating advancements in Telugu speech processing.

Architecture

The model is built upon the Wav2Vec architecture, a popular choice for automatic speech recognition. This architecture uses unsupervised learning to extract meaningful features from raw audio data. The extracted features are then used by a downstream task, such as speech-to-text transcription, to generate textual representations of spoken language.

Training

While the specific training details are not provided in the README, models like indicwav2vec_v1_telugu typically undergo a pre-training phase using large amounts of unlabeled audio data. They are then fine-tuned with labeled datasets that provide audio-text pairs in the targeted language, in this case, Telugu. This two-step training approach allows the model to learn both general audio features and specific linguistic characteristics of the target language.

Guide: Running Locally

To run the indicwav2vec_v1_telugu model locally, follow these basic steps:

1. Setup Python Environment: Ensure you have Python installed. Create a virtual environment for the project.

   python -m venv env
   source env/bin/activate
   

2. Install Dependencies: Use pip to install the necessary libraries. The Hugging Face Transformers library will be essential.

   pip install transformers
   

3. Load the Model: Use the Transformers library to load the model and tokenizer.

   from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
   
   tokenizer = Wav2Vec2Tokenizer.from_pretrained("ai4bharat/indicwav2vec_v1_telugu")
   model = Wav2Vec2ForCTC.from_pretrained("ai4bharat/indicwav2vec_v1_telugu")
   

4. Inference: Prepare an audio file and process it through the model to obtain transcriptions.

   import torch
   import librosa
   
   audio_input, _ = librosa.load("path_to_audio.wav", sr=16000)
   input_values = tokenizer(audio_input, return_tensors="pt").input_values
   
   with torch.no_grad():
       logits = model(input_values).logits
   
   predicted_ids = torch.argmax(logits, dim=-1)
   transcription = tokenizer.decode(predicted_ids[0])
   print(transcription)
   

Cloud GPUs: Consider using cloud-based GPU services such as AWS, Google Cloud Platform, or Azure to enhance performance, especially for large-scale audio files or batch processing.

License

The indicwav2vec_v1_telugu model is released under the MIT License, allowing for flexibility in usage, modification, and distribution.