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---
language: en
datasets:
- LIUM/tedlium
tags:
- speech
- audio
- automatic-speech-recognition
---
Finetuned from [facebook/wav2vec2-large-960h-lv60-self](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self).
# Installation
1. PyTorch installation: https://pytorch.org/
2. Install transformers: https://huggingface.co/docs/transformers/installation
e.g., installation by conda
```
>> conda create -n wav2vec2 python=3.8
>> conda install pytorch cudatoolkit=11.3 -c pytorch
>> conda install -c conda-forge transformers
```
# Usage
```python
# Load the model and processor
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
import numpy as np
import torch
model = Wav2Vec2ForCTC.from_pretrained(r'yongjian/wav2vec2-large-a') # Note: PyTorch Model
processor = Wav2Vec2Processor.from_pretrained(r'yongjian/wav2vec2-large-a')
# Load input
np_wav = np.random.normal(size=(16000)).clip(-1, 1) # change it to your sample
# Inference
sample_rate = processor.feature_extractor.sampling_rate
with torch.no_grad():
model_inputs = processor(np_wav, sampling_rate=sample_rate, return_tensors="pt", padding=True)
logits = model(model_inputs.input_values, attention_mask=model_inputs.attention_mask).logits # use .cuda() for GPU acceleration
pred_ids = torch.argmax(logits, dim=-1).cpu()
pred_text = processor.batch_decode(pred_ids)
print('Transcription:', pred_text)
```
# Finetune Code
Github Repo:
https://github.com/CassiniHuy/wav2vec2_finetune |