Update README.md

README.md

@@ -2,4 +2,43 @@
 license: afl-3.0
 language:
 - fr
----
+---
+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)
+```
+
+# Code
+GitHub Repo:
+https://github.com/CassiniHuy/wav2vec2_finetune