chinese wav2vec2 large

Introduction

The chinese-wav2vec2-large model by TencentGameMate is a pretrained model designed for audio processing tasks. It has been pretrained on 10,000 hours of the WenetSpeech L subset, focusing primarily on audio data without a tokenizer. Therefore, to perform speech recognition, further fine-tuning with labeled text data and a tokenizer is required.

Architecture

The model uses the Wav2Vec2 architecture, which is part of the Transformers library and based on PyTorch. It is particularly suitable for pretraining tasks on audio data, leveraging powerful feature extraction capabilities to process and understand complex audio inputs.

Training

The model was pretrained using 10,000 hours of audio data from the WenetSpeech L subset. It does not include a tokenizer, which is critical for converting audio inputs into text. As such, users intending to apply this model for speech recognition should develop a tokenizer and conduct additional fine-tuning with relevant labeled text data.

Guide: Running Locally

To run the model locally, follow these steps:

1. Setup Environment:

   • Ensure Python is installed on your system.
   • Install the required packages using pip:

     pip install torch soundfile transformers==4.16.2
     

2. Prepare Model and Data:

   • Download the model from the Hugging Face Model Hub.
   • Specify the paths for the model and the audio file:

     model_path = "path_to_model"
     wav_path = "path_to_audio_file.wav"
     

3. Load and Use the Model:

   • Use the Wav2Vec2FeatureExtractor and Wav2Vec2Model from the Transformers library to process and analyze your audio data:

     feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_path)
     model = Wav2Vec2Model.from_pretrained(model_path)
     

4. Inference:

   • Load the audio file and perform inference:

     wav, sr = sf.read(wav_path)
     input_values = feature_extractor(wav, return_tensors="pt").input_values.to(device)
     with torch.no_grad():
         outputs = model(input_values)
         last_hidden_state = outputs.last_hidden_state
     

Suggested Cloud GPUs

For enhanced performance, especially with large models or datasets, consider using cloud services such as AWS EC2 with GPU instances, Google Cloud Platform, or Azure GPU VMs.

License

The chinese-wav2vec2-large model is released under the MIT License, allowing for broad usage including modification and distribution. Ensure compliance with license terms when using the model in your projects.